Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Thu, 19 Feb 2026 05:20:02 GMT https://www.techtarget.com/searchbusinessanalytics editor@techtarget.com <p>With its latest Analyst Studio update, ThoughtSpot continues its progress toward becoming an agentic platform.</p> <p>First <a href="https://www.techtarget.com/searchbusinessanalytics/news/366617947/ThoughtSpot-adds-data-preparation-with-Analyst-Studio-launch">released in January 2025</a>, Analyst Studio is ThoughtSpot's <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a> suite. Initial features included connectors that enable analysts and engineers to combine data from disparate sources, an AI-assisted <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a>-based development environment, and capabilities aimed at helping customers control data management costs.</p> <p>Among other tools, the update, released on Wednesday, adds SpotCache, a caching capability that builds on Analyst Studio's pre-existing cost management capabilities, a data preparation agent that enables users to perform tasks using natural language and a native spreadsheet interface for scaling data preparation workloads.</p> <p>Coming just over two months after ThoughtSpot unveiled plans <a href="https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents">automate its analytics capabilities</a> with agents, the new version of Analyst Studio represents ThoughtSpot's progression beyond agentic analytics toward agentic data management as well, according to Donald Farmer, founder and principal of TreeHive Strategy.</p> <p>"I don't think this release is a big deal in itself, but it steadily moves ThoughtSpot forward on the path to an agentic data platform," he said. "With each release, the workflow is less dashboard-centric."</p> <p>Michael Ni, an analyst at Constellation Research, similarly noted that the Analyst Studio is significant because it shows ThoughtSpot moving beyond its roots as an analytics specialist toward becoming a more broad-based data and analytics provider.</p> <p>"Thoughtspot addresses a key pain by reducing prep friction, improving cost predictability and tightening governance," he said. "At the same time, it's strategic for ThoughtSpot. The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving."</p> <p>Based in Mountain View, Calif., ThoughtSpot provided an AI-powered analytics platform from its inception in 2012. Now, just as peers such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/252493644/Data-prep-in-browser-highlights-Tableau-BI-platform-update">Tableau</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">Qlik</a> did before, ThoughtSpot is expanding beyond its roots to provide a wider array of data, analytics and AI development capabilities.</p> <section class="section main-article-chapter" data-menu-title="Predictable data prep"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Predictable data prep</h2> <p>While some enterprises are <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">investing heavily</a> in building AI tools that make employees better informed and operations more efficient, the expense required to develop and maintain agents, chatbots and other AI applications has <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">proven prohibitive</a> for many others.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving. </figure> <figcaption> <strong>Michael Ni</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>AWS recently made cost control one of the focal points of <a href="https://www.techtarget.com/searchdatamanagement/news/366635663/Latest-AWS-data-management-features-target-cost-control">the data management capabilities</a> it introduced during its annual re:Invent conference. In addition, numerous database vendors have made performance a priority so that customers can run more efficient workloads.</p> <p>With SpotCache now available in Analyst Studio, ThoughtSpot is similarly taking aim at helping customers reduce spending on part of the development process.</p> <p>Caching is the process of storing data in a temporary storage area -- a cache -- to enable fast access that improves the performance of applications and other systems. Using SpotCache, developers and analysts can create representations of data that can be queried an unlimited number of times in ThoughtSpot, which lowers costs by reducing the frequency data must be accessed in <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluate-cloud-data-warehouses-based-on-data-outcomes">cloud data warehouses</a>.&nbsp;</p> <p>Given that SpotCache addresses one of the problems enterprises encounter when trying to develop cutting-edge AI tools, it is perhaps the most valuable new feature in Analyst Studio, according to Ni.</p> <p>"SpotCache is the sleeper hit in their announcement," he said. "While agentic data prep is powerful, cost certainty is what unlocks enterprise scale. If leaders know they can run unlimited AI-driven queries without blowing up their warehouse bill, adoption accelerates. That's what makes this the most strategically significant feature."</p> <p>Farmer likewise highlighted SpotCache, noting that cloud cost control -- or lack thereof -- has been <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">an ongoing problem</a>.</p> <p>"I like that they are tackling the problem of scaling with predictable cost management," he said. "That has been a barrier to broader adoption for some time. So, SpotCache stands out as arguably the most valuable new feature here."</p> <p>Beyond SpotCache, ThoughtSpot's Analyst Studio update includes the following features:</p> <ul type="disc" class="default-list"> <li>A governed spreadsheet interface so that users familiar with Excel worksheets can perform data preparation tasks such as advanced manipulations in a familiar environment without having to leave Analyst Studio.</li> <li>A data prep agent that enables analysts to profile datasets, generate queries and troubleshoot schemas using natural language.</li> <li>Unified Data Mashup, a feature that enables data teams to deliver a unified, <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">trusted view</a> of their organization's business by blending data across cloud data warehouses, business applications and files such as Google Sheets and Microsoft Excel spreadsheets within Analyst Studio's SQL-based development environment.</li> <li>Flexibility to choose live connections for real-time needs or cached snapshots through SpotCache.</li> </ul> <p>Like Farmer and Ni, Anjali Kumari, ThoughtSpot's vice president of product management, named SpotCache the most valuable of Analyst Studio's new features. Meanwhile, she noted that the impetus for designing the new capabilities came from observing how agentic AI and generative AI have <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">intensified the need for data</a>.</p> <p>"Getting data ready for AI has become the most important step in how organizations use, benefit and see valuable return on investment," Kumari said. "We understand the pressures that are placed on data analysts, and these tools are designed to streamline their role by addressing their top concerns -- speed, efficiency and cost."</p> <p>While ThoughtSpot's expansion beyond business intelligence into data preparation with Analyst Studio is beneficial for the vendor's users, pairing analytics and data management is not unique. Not only do hyperscale cloud providers such as AWS, Google and Microsoft offer an array of data management, application development and analytics tools, but so do <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">Domo</a>, Qlik, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366553555/Sisense-unveils-composable-toolkit-for-app-development">Sisense</a>, Tableau and other one-time analytics specialists.</p> <p>However, the launch of a data prep agent is unique, according to Farmer.</p> <p>"The biggest differentiator from other prep tools, such as Tableau or [Microsoft's] Power Query, is that ThoughtSpot offers a natural language data prep agent whereas AI in other tools is mostly limited to 'smart suggestions' or separate AI copilots," he said.</p> <p>In addition, by integrating data preparation, data modeling and analytics in a single workflow designed for consumption via AI applications, ThoughtSpot is doing something different than its <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-cloud-based-analytics-tools-for-enterprise-use">closest competition</a>, according to Ni.</p> <p>"Data prep is consolidating into platforms, and every business intelligence vendor has some version of it. What's interesting here is that ThoughtSpot is making data prep part of an agentic operating model," he said. "Instead of separate tooling for prep, modeling and analysis, they're collapsing it into one workflow designed for AI readiness."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the Analyst Studio update generally available, ThoughtSpot is focused on turning its platform into an enabler of autonomous action, according to Kumari.</p> <p>Spotter is <a href="https://www.techtarget.com/searchbusinessanalytics/news/366615693/ThoughtSpot-AI-agent-Spotter-enables-conversational-BI">ThoughtSpot's agent-powered interface</a>, and the vendor provides Spotter agents for specific tasks such as building dashboards and embedding intelligence. Together, with Spotter as the central orchestrator, ThoughtSpot aims to automate analysis and data preparation to deliver insights within user workflows.</p> <p>"To power this, we are investing heavily in data readiness and expanding our semantic and modeling capabilities, ensuring these agents operate on a robust, context-aware foundation," Kumari said.</p> <p>As ThoughtSpot expands beyond its roots, Farmer advised the vendor to add <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">multi-agent coordination capabilities</a>. ThoughtSpot's <a target="_blank" href="https://modelcontextprotocol.io/docs/getting-started/intro" rel="noopener">Model Context Protocol</a> server enables agents to securely interact with data sources. Soon, as more enterprises deploy agents, the agents will need similar secure connections to each other to become fully autonomous.</p> <p>"Multi-agent coordination [is] moving from a single prep agent to a 'hive' where a prep agent automatically communicates with a security agent to apply row-level permissions during the transformation process," Farmer said.</p> <p>In addition, ThoughtSpot could add <a href="https://www.techtarget.com/whatis/definition/write-back">write-back</a> capabilities and add to its burgeoning support for unstructured data by integrating unstructured data processing directly into Analyst Studio.</p> <p>Ni, meanwhile, suggested that ThoughtSpot move beyond descriptive BI to incorporate more forward-looking <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Descriptive-vs-prescriptive-vs-predictive-analytics-explained">predictive and prescriptive capabilities</a>.</p> <p>"Descriptive BI is yesterday, diagnostic AI is today, and predictive and prescriptive intelligence that tell me what could happen and where I should focus is tomorrow," he said "ThoughtSpot is strong at explaining the past and present. … Their next leap is forecasting impact and prioritizing what matters next."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> A data prep agent and caching capabilities aimed at helping users control spending help the vendor stand out from its peers as it evolves toward becoming an agentic data platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchbusinessanalytics/news/366639258/ThoughtSpot-boosts-agentic-push-with-Analyst-Studio-update Wed, 18 Feb 2026 09:00:00 GMT <p>Streaming data specialist Redpanda on Wednesday launched new features in its Agentic Data Plane aimed at enabling customers to create a unified governance layer for managing connections between agents and data sources.</p> <p>Redpanda <a href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite">first launched the Agentic Data Plane</a> (ADP) in October 2025 featuring capabilities that enabled connectivity between agents and streaming data sources, including support for <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">the Model Context Protocol</a>&nbsp;(MCP) and&nbsp;<a href="https://www.techtarget.com/searchenterpriseai/news/366622027/Google-intros-tools-for-building-agents-and-a-new-protocol">Agent2Agent Protocol&nbsp;</a>(A2A) frameworks.</p> <p>However, capabilities that govern those connections were not yet ready.</p> <p>Now, the vendor is adding features such as AI Gateway to provide users a centralized governance pane, AI observability via the <a target="_blank" href="https://opentelemetry.io/docs/specs/otel/protocol/" rel="noopener">OpenTelemetry Protocol</a> (OTLP) to inspect and monitor agent behavior and new security controls.</p> <p>Given that agents and multi-agent systems require proper governance frameworks to ensure that they act in accordance with an enterprise's policies and meet regulatory requirements, the new ADP features are significant for Redpanda customers, according to William McKnight, president of McKnight Consulting.</p> <p>"The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI," he said. "The update addresses critical barriers by providing unified security and operational control over AI costs and token budgets. This update enables 'glass box' visibility and framework flexibility, allowing users to move … from risky experimentation to secure production."</p> <p>Based in San Francisco, Redpanda provides <a href="https://www.techtarget.com/searchdatamanagement/news/366574612/Redpanda-serverless-streaming-option-targets-cost-control">a streaming data platform</a> that enables users to capture and process data to fuel real-time analysis. Like many data management providers, the vendor has responded to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing interest in AI development</a> and added tools that let customers connect data to agents and other AI applications in addition to traditional data products such as dashboards and reports.</p> <section class="section main-article-chapter" data-menu-title="Keeping control"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Keeping control</h2> <p>Agents need governance.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Unlike chatbots and other AI applications, agents are <a href="https://www.techtarget.com/searchenterpriseai/tip/A-technical-guide-to-agentic-AI-workflows">capable of autonomous behavior</a>. Rather than requiring user prompts before taking action, they can be trained to work independently.</p> <p>For example, they can constantly analyze an enterprise's data estate to surface insights that a human analyst might never have discovered. They can take on repetitive, menial work so that employees can be more efficient and spend time doing more meaningful work. And they can work together to optimize complex processes such as managing supply chains.</p> <p>Because they can make entire organizations better informed and more efficient, agents have been <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Agents-semantic-layers-among-top-data-analytics-trends">the dominant trend</a> in AI development over the past two years. But because just one agent taking the wrong action can <a target="_blank" href="https://hackernoon.com/22-examples-of-incompetent-ai-agents" rel="noopener">cause significant harm</a>, strict policies and procedures must be &nbsp;in place to ensure that agents can be trusted when put into production.</p> <p>With most AI initiatives <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">never making it past the pilot stage</a>, a mix of customer feedback and Redpanda's own experiences developing agents provided the impetus for adding new ADP features designed to engender trust that agents will act as intended once deployed, according to Tyler Akidau, the streaming data vendor's chief technology officer.</p> <p>"Our roadmap has been developed in response to direct customer feedback, our own experiences developing and deploying agents internally and our vision for what is needed to unlock agentic AI in the enterprise," he said.</p> <p>AI Gateway acts as a unified access layer for connecting agents with AI models and MCP servers by centralizing routing data, enforcing organizational policies, limiting <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">spending that can spiral</a> when cloud usage isn't controlled and enabling observability of AI systems.</p> <p>Observability includes automatically generated metrics, traces, logs and transcripts using the OTLP Protocol so that users can check agent behavior in their Redpanda console and take appropriate action such as debugging when necessary.</p> <p>In addition, the ADP now includes security through <a href="https://www.techtarget.com/searchsecurity/feature/How-to-use-OpenID-Connect-for-authentication">the OpenID Connect standard</a> and fine-grained authorization policies so that every interaction with an agent, whether by a human user or another agent, is properly checked and governed.</p> <p>Meanwhile, the ADP is designed to work in conjunction with <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">any agentic framework</a> so that customers can easily run and govern agents fueled by Redpanda's streaming data capabilities.</p> <p>Collectively, the new ADP features are valuable for Redpanda customers given that they address numerous concerns and that they add governance to data that fuels real-time analysis, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This announcement … is comprehensive," he said." Redpanda's platform addresses data and AI governance, observability and even FinOps objectives. That's a broader set of capabilities than most platforms have. The announcement [also] stands out because Redpanda is building these capabilities onto a data streaming platform rather than a standard data-at-rest platform."&nbsp;</p> <p>Perhaps the most significant of the new ADP features is AI Gateway, according to McKnight. Meanwhile, from a competitive standpoint, Redpanda's new governance capabilities could help differentiate the vendor's capabilities from competing streaming data platforms <a href="https://www.techtarget.com/searchdatamanagement/news/366633567/Streaming-specialist-Confluent-unveils-AI-development-suite">such as Confluent</a>, which is the industry standard for commercial Apache Kafka and is now under agreement to be <a href="https://www.techtarget.com/searchdatamanagement/news/366636098/IBM-acquiring-Confluent-to-boost-AI-development-capabilities">acquired by IBM</a>, he continued.</p> <p>Redpanda's streaming capabilities are comparable to Confluent's, outperforming Confluent in some benchmark testing, McKnight noted. But AI governance is where Redpanda could truly stand apart.</p> <p>"Redpanda is starting to differentiate by shifting from 'data piping' to a dedicated AI governance infrastructure," McKnight said. "Unlike standard tools that require fragmented security at every source, its AI Gateway will provide a centralized control plane for managing policies, token budgets and Model Context Protocol servers."</p> <p>Petrie similarly suggested that the breadth of the new ADP features help Redpanda distinguish itself from competitors, which include AWS, Google Cloud and Microsoft, beyond <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">streaming data specialists</a>.</p> <p>"This announcement strengthens its competitive standing by integrating so many capabilities into one streaming solution," he said. "To get the same features from the larger vendors, you would need to buy multiple products. Redpanda also has the advantage of platform neutrality -- it operates across sources, systems and clouds."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Following the initial launch of the ADP last October, Redpanda's new governance features represent the second phase of the streaming data vendor's ADP rollout. With an overarching goal of helping customers deploy agentic AI across their organization, more features that enable users to <a href="https://www.techtarget.com/searchenterpriseai/opinion/Building-governance-for-machine-speed-The-path-to-trusted-AI-autonomy">trust and deploy agents</a> are a prominent part of Redpanda's roadmap, according to Akidau.</p> <p>"We'll be delivering additional features in our AI and MCP gateways, rolling out more agent evaluation functionality, delivering manual and automatic agent kill switches, connecting agents to more data sources, and rolling out [a SQL engine] for federated query support," he said.</p> <p>One way that Redpanda could better serve its current users and perhaps attract new ones would be to market AI Gateway as a tool for <a href="https://www.techtarget.com/searcherp/feature/Predictability-emerging-as-enterprise-ITs-new-north-star">financial governance</a> in addition to technical governance, according to McKnight.</p> <p>"They could also solidify the 'real-time' advantage by demonstrating that [its] low-latency foundation creates better agents, not just faster data," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> New Agentic Data Plane features enable users to create a governance layer for agents and could help the vendor differentiate itself from its closest competitors. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a373894778.jpg https://www.techtarget.com/searchdatamanagement/news/366639150/Streaming-specialist-Redpanda-adds-governance-to-AI-suite Wed, 18 Feb 2026 09:00:00 GMT <p>With retrieval-augmented generation pipelines struggling to deliver the relevant data that agents and other AI applications need to deliver trustworthy outputs, Graphwise launched GraphRAG to provide customers with an alternative designed to enable more successful AI development.</p> <p>Retrieval-augmented generation (<a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">RAG</a>) is a framework for connecting applications such as agents and chatbots with data sources. However, with most AI initiatives failing to make it past the pilot stage and into production, standard RAG pipelines haven't proven good enough on their own to enable enterprises to deliver usable, trustworthy AI tools.</p> <p>In January, <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks launched Instructed Retriever</a>, an alternative to RAG that adds more context to data retrieval such as user instructions and previous examples. &nbsp;Traditional RAG systems only use a user's query.</p> <p>Graphwise's GraphRAG, which was released on Feb. 16, unites agents and other applications with a <a href="https://www.techtarget.com/searchenterpriseai/definition/knowledge-graph-in-ML">knowledge graph</a> that acts as a semantic layer and is similarly aimed at improving on standard RAG pipelines.</p> <p>Alan Morrison, an independent analyst, noted that while knowledge graphs can be traced back to the 1960s, they are taking on greater importance because agents need the context knowledge graphs provide to perform to enterprise standards. As a result, GraphRAG is a significant addition for Graphwise users.</p> <p>"Graphwise can bring all enterprise data, content and knowledge together using standard-based graph description logic that's been around for decades, but only now becoming indispensable because the agent paradigm is here, and agents desperately need reliable context," he said. "With GraphRAG, tapping the power of that contextualized data becomes simpler."</p> <p>Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, likewise noted that GraphRAG is valuable for Graphwise users given that it combines graph technology and RAG.</p> <p>"Bringing them together is powerful," he said. "GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve."</p> <p>With a North American headquarters in New York City and a European headquarters in Sofia, Bulgaria, Graphwise is a graph technology vendor formed in 2024 when Ontotext merged with Semantic Web Company. Competitors include specialists such as <a href="https://www.techtarget.com/searchdatamanagement/news/366630145/Neo4js-latest-targets-graph-database-performance-at-scale">Neo4j</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366618412/TigerGraph-launches-Savanna-to-aid-AI-development">TigerGraph</a> as well as broader-based database providers featuring graph database capabilities including AWS, Google Cloud and Microsoft.</p> <section class="section main-article-chapter" data-menu-title="Improving AI development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Improving AI development</h2> <p>Problems related to data aren't the sole reason <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">most AI initiatives fail</a> before making it into production. Unrealistic expectations, lack of a clear business case and difficulties integrating applications into real-world workflows are among the other reasons an estimated 80% of all AI projects fail.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>But issues with data -- including the lack of sufficient high-quality, relevant data -- are among the main ones.</p> <p>Graphwise's new feature targets the discovery of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">relevant data</a>. Agents and other applications are built for specific tasks. For example, many enterprises are building agents that autonomously handle customer service. If the pipelines that feed those agents don't deliver the specific data relevant to an individual customer and their problem, the agent won't be effective.</p> <p>Semantic modeling -- ensuring that metadata is consistently and clearly classified whenever it is ingested or transformed -- is one means of improving search relevance and is <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">gaining popularity</a> as enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">invest more heavily in AI initiatives</a>. Graphwise's knowledge graph serves as a semantic layer, adding context to data and finding relationships between data points to make them easily discoverable.</p> <p>GraphRAG unites large language models, an enterprise's data, a structured knowledge graph and multiple search methods such as similarity search and keyword search to deliver appropriate data to agents and other applications to provide accurate outputs at a much higher rate than standard RAG.</p> <p>"The development of GraphRAG was driven by a specific structural failure in the market we call the 'Prototype Plateau,'" said Andreas Blumauer, founder of Semantic Web Company and Graphwise's senior vice president of growth. "While customers were indeed requesting better accuracy, the primary motivation came from observing enterprises stuck in a cycle of failed pilots."</p> <p>In particular, <a href="https://www.techtarget.com/searchenterpriseai/tip/RAG-best-practices-for-enterprise-AI-teams">RAG systems</a> didn't provide enough context to retrieving data, he continued.</p> <p>"Our motivation was to transform RAG from a simple associative engine into a reasoning engine," Blumauer said. "By injecting a 'Semantic Backbone', we moved beyond probability-based guesses to explicit, logic-based relationships."</p> <p>Specific GraphRAG features include the following:</p> <ul type="disc" class="default-list"> <li>Semantic Metadata Control Plane, a semantic model designed to substantially improve the accuracy of AI outputs, including reducing the likelihood of <a href="https://www.techtarget.com/searchenterpriseai/tip/Why-does-AI-hallucinate-and-can-we-prevent-it">AI hallucinations</a>, by grounding responses in an enterprise's consistent metadata.</li> <li>Explainability and Provenance Panels that display how AI responses are generated, enabling users to check for accuracy and supporting regulatory compliance by providing transparency.</li> <li>Visual debugging and monitoring capabilities that allow developers and engineers to trace an error path and drastically reduce the amount of time previously needed to troubleshoot.</li> <li>A low-code interface that enables business users to adjust AI logic without involving <a href="https://www.techtarget.com/whatis/definition/Python">Python</a> code experts.</li> <li>Built-in templates that provide governance and enable query expansion that would otherwise require extensive research and development and technical support.</li> <li><a target="_blank" href="https://www.w3.org/2004/02/skos/" rel="noopener">Simple Knowledge Organization System</a> (SKOS)-like enrichment to capture domain-specific intelligence so that AI tools can understand an enterprise's unique terminology and ensure that users get accurate responses regardless of how they phrase a query.</li> </ul> <p>Most valuable are the Semantic Metadata Control Plane and SKOS-style enrichment, according to Morrison, who noted that the control plane is where enterprises can make data accessible and discoverable across their entire data estate while SKOS-style enrichment allows non-technical users to work with data.</p> <p>Catanzano likewise highlighted the Semantic Metadata Control Plane. In addition, he noted the value of the <a href="https://www.techtarget.com/searchenterpriseai/tip/Low-code-no-code-tools-simplify-AI-customization-for-engineers">low-code interface</a>. Meanwhile, capabilities such as GraphRAG help Graphwise differentiate from competing graph technology vendors by integrating knowledge graphs with AI, Catanzano continued.</p> <p>"Its capabilities, such as explainability, provenance, and domain-specific intelligence, position it as a leader in making generative AI reliable and scalable, surpassing the limitations of traditional graph database vendors," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With GraphRAG now available, Graphwise's product development plans include adding AI-assisted automation capabilities and improving the memory of its platform, according to Blumauer.</p> <p>Memory initiatives include moving beyond session-based interactions to retaining user preferences and context to provide more personalization. AI-assisted automation plans include tools that augment text <a href="https://www.techtarget.com/searchdatamanagement/tip/How-to-make-a-metadata-management-framework">with metadata</a> to make it discoverable and generating <a href="https://www.techtarget.com/searchdatamanagement/definition/schema">schemas</a> to aid data modeling.</p> <p>"A major theme is reducing manual effort through AI-assisted automation," Blumauer said.</p> <p>Morrison advised that Graphwise to develop a multi-layer <a target="_blank" href="https://www.linkedin.com/pulse/context-graphs-capturing-why-age-ai-dharmesh-shah-oyyze/" rel="noopener">context graph</a> to expand on the context GraphRAG currently provides.</p> <p>Catanzano, meanwhile, suggested that Graphwise develop new integrations with data and AI providers to <a href="https://www.techtarget.com/searchbusinessanalytics/news/252515720/Gartner-Augmented-analytics-ecosystem-for-BI-now-key">expand its ecosystem</a> and create prebuilt templates that simplify its platform for enterprises in certain industries.</p> <p>"Industry-specific templates … would not only deepen its value for current users but also attract new customers seeking tailored, ready-to-deploy solutions," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With standard RAG pipelines proving unreliable, the vendor's new feature uses knowledge graphs to add needed context to the data retrieval process that fuels AI outputs. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1304896250.jpg https://www.techtarget.com/searchdatamanagement/news/366639196/Graphwise-aims-to-boost-AI-accuracy-with-GraphRAG-launch Tue, 17 Feb 2026 14:34:00 GMT <p>Tuesday was a day of doubles for SurrealDB.</p> <p>The startup <a href="https://www.techtarget.com/searchdatamanagement/feature/The-rise-of-multi-model-databases-to-support-data-variety">multimodel database</a> vendor secured $23 million in venture capital funding, an extension of its Series A round that nearly doubles its total funding to $44 million. In addition, SurrealDB launched version 3.0 of its platform.</p> <p>The $23 million brings SurrealDB's Series A round to $38 million, with Chalfen Ventures and Begin Capital joining previous investors FirstMark and Georgian in their investment in the database specialist. As part of the deal, Mike Chalfen, founder of Chalfen Ventures, joins SurrealDB as a director</p> <p>Meanwhile, SurrealDB 3.0 includes new features such as a new control layer and improved vector storage and indexing capabilities, and is designed to help customers unify multiple data models -- relational, document, <a href="https://www.techtarget.com/searchdatamanagement/news/366593101/Graph-technology-helps-battle-election-misinformation">graph</a>, time-series, vector, <a href="https://www.techtarget.com/searchbusinessanalytics/news/252523998/Geospatial-data-a-key-means-of-combating-climate-change">geospatial</a> and key-value -- to fuel AI development.</p> <p>Both the new funding and platform update are significant for SurrealDB users, with one providing the cash that will enable SurrealDB to grow its multimodel capabilities and the other demonstrating those capabilities, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI," he said. "They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. This level of funding can help SurrealDB deepen its product capabilities and get more serious go-to-market activities."</p> <p>Based in London, SurrealDB provides a platform that supports various data types so that users can integrate data to inform decisions based on more than just one type of data. Other vendors providing multimodel capabilities include <a href="https://www.techtarget.com/searchdatamanagement/news/366636081/Couchbase-adds-agentic-AI-development-suite-to-Capella-DBaaS">Couchbase</a>, Redis and the <a href="https://www.theserverside.com/tip/MySQL-vs-PostgreSQL-Compare-popular-open-source-databases">open-source PostgreSQL platform</a>, while hyperscale cloud providers AWS, Google Cloud and Microsoft also offer multimodel databases.</p> <section class="section main-article-chapter" data-menu-title="Cash infusion"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Cash infusion</h2> <p>SurrealDB's latest funding comes at a time when venture capital investments in data management vendors are few and far between.</p> <p>Throughout the 2010s and into the early 2020s, funding poured into the data and analytics market. In 2021 alone, vendors such as Aiven, Confluent, Databricks, Reltio, SnapLogic, ThoughtSpot and TigerGraph raised $100 million or more with Databricks' funding round reaching $1 billion. In early 2022, Aiven raised another $210 million and Sigma secured $300 million.</p> <p>But then <a href="https://www.techtarget.com/searchbusinessanalytics/news/252520740/Tech-stock-sell-off-signals-tough-times-for-data-vendors">tech stock prices plummeted</a> in mid-2022, and the funding for data and analytics vendors evaporated.</p> <p>Since then, while vendors such as <u>Aerospike</u> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366585775/Differentiation-key-as-Sigma-Computing-raises-200M">Sigma</a> have attracted investments, few data and analytics vendors have raised capital. The common theme among the data and analytics vendors that continue to attract funding is their enablement of AI development.</p> <p>Databricks, for example, has focused heavily on AI, and continues to <a href="https://www.techtarget.com/searchdatamanagement/news/366636532/Databricks-adds-4B-funding-round-IPO-could-be-next">attract massive amounts of investment capital</a>.</p> <p>SurrealDB aims to be a layer in the AI development process. It's that focus on enabling customers to develop agents and other AI applications that helped the vendor raise funding, according to Tobie Morgan Hitchcock, SurrealDB's co-founder and CEO.</p> <p>"Raising now signals we're not just another database vendor, but increasingly an enabling layer for enterprise AI workflows," he said. "SurrealDB is being used as part of enterprise AI deployments, including agentic workflows that depend on fast, consistent data access. … Investors are leaning into infrastructure that makes AI systems production-grade, which is exactly what we are building."</p> <p>Matt Aslett, an analyst at ISG Software Research, similarly noted that SurrealDB's ability to attract venture capital funding reflects its focus on helping enterprises <a target="_blank" href="https://www.reuters.com/business/ai-venture-funding-continued-surge-third-quarter-data-shows-2025-10-06/" rel="noopener">build AI applications</a>.</p> <p>"While many VCs are chasing potential returns from investment in AI specialists, there is always investor interest in startups with the potential to make an impact in the lucrative database market, especially providers that are responding to the need for innovation to support AI initiatives," he said.</p> <p>SurrealDB plans to use the added $23 million to accelerate product engineering and improve go-to-market efforts, according to Hitchcock.</p> <p>"It lets us scale the team and the platform in parallel, shipping more capability, hardening reliability and security, and supporting larger deployments," he said. "In short, it accelerates our path from rapid adoption to durable, global scale."</p> <p>Given that SurrealDB, which was founded in 2021, is a relatively new database vendor compared to peers such as <a href="https://www.techtarget.com/searchdatamanagement/news/252525824/ArangoDB-expands-scope-of-graph-database-platform">ArangoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/252514648/Redis-launches-JSON-database-capabilities-with-RedisJSON-20">Redis</a>, improving its platform and increasing its profile are wise areas of focus, according to Aslett.</p> <p>"SurrealDB is in the early stages, and its new funding round will help the company accelerate the development of both its core database and its platform capabilities, as well as expanding investment in support and services resources as well as raising its profile in a crowded market," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Platform update"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Platform update</h2> <p>While the new funding will be used, in part, to fuel future product development, SurrealDB 3.0 represents the vendor's current product development.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI. They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>SurrealDB is designed to provide agents and other applications with unified data -- disparate data types integrated as one -- to give them proper context and memory so that they remember facts even as <a target="_blank" href="https://explodingtopics.com/blog/data-generated-per-day" rel="noopener">data volume and complexity increase</a>. To provide that proper context and memory, SurrealDB positions context graphs in its database next to the data.</p> <p>With that focus on enabling customers to build intelligence applications that feature contextual awareness and the memory to recall and learn from previous interactions, SurrealDB, though a startup competing against more established vendors for market share, has an opportunity to play the role of disruptor, according to Aslett.</p> <p>"The evolving requirements for operational databases are to support the development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by machine learning, generative AI and agentic AI," he said. "These evolving requirements are providing opportunities for emerging database providers to disrupt established incumbents."</p> <p>ISG predicts that within the next two years, around three-quarters of all enterprises will have adopted operational databases specifically designed to support the <a href="https://www.techtarget.com/whatis/definition/What-is-AI-inference">AI inferencing</a> capabilities that intelligent applications require, Aslett added.</p> <p>SurrealDB 3.0 builds on previous platform capabilities by adding the following:</p> <ul type="disc" class="default-list"> <li>Surrealism, a layer that enables developers and administrators to customize <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- the rules, processes and operations that determine how an enterprise uses SurrealDB -- including access controls and version controls.</li> <li>Improved vector search and indexing to enable the discovery and use of unstructured data such as text and images.</li> <li>Support for both <a href="https://www.techtarget.com/whatis/feature/Structured-vs-unstructured-data-The-key-differences">structured and unstructured data</a>.</li> <li>Architectural changes that add stability and improve SurrealDB's performance such as separating data values from data expressions.</li> <li>An improved developer experience, including a refined model that enables users to define custom API endpoints directly within the database, among other functions.</li> </ul> <p>A combination of customer feedback and market observations provided SurrealDB with the impetus for developing the individual features that comprise its update, according to Hitchcock.</p> <p>"The focus is on removing friction -- making the platform easier to adopt, operate, and scale -- while expanding the capabilities teams need in production," he said. "The goal is to keep the developer experience simple as use cases become more demanding."</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Looking ahead, SurrealDB is focused on three main initiatives, according to Hitchcock: maturing its platform to meet the needs of enterprises at scale, expanding its capabilities so customers don't have to integrate it with as many other tools to build AI applications and continuing to enhance <a href="https://www.techtarget.com/searchsoftwarequality/feature/6-key-ways-to-improve-developer-productivity">the developer experience</a>.</p> <p>"As more customers deploy AI in production, we're investing in capabilities that make it easier to deploy and scale AI-powered applications and agentic workflows," Hitchcock said. "Our focus is broad. … It's all about simplification."</p> <p>Despite still prioritizing some foundational capabilities such enabling enterprise-scale workloads and adding features that allow users to simplify their AI development stacks, SurrealDB is establishing itself as a viable alternative to other database vendors, according to Aslett.</p> <p>All database providers are similarly adding capabilities that foster AI development, such <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">as vector storage and indexing</a>. But Aslett noted that SurrealDB's support for various data types and the quality if its vector storage and indexing capabilities stand out.</p> <p>"SurrealDB is ahead of many established providers in terms of delivering differentiating capabilities, including enhanced vector search and indexing as well as native agent memory and context graphs, … an intuitive visual user interface and support for relational, document, graph, time-series, vector, search, geospatial and key-value data types in a single database."</p> <p>Petrie similarly noted that SurrealDB is staking out a place for itself in <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">a competitive market</a> with the variety of its multimodel capabilities.</p> <p>"I'm impressed with the range of data types that SurrealDB already supports as a Series A startup," he said. "This really simplifies your agentic AI architecture. The more you can consolidate diverse data and models onto a single platform, with real-time performance and memory, the better you can streamline your projects and reduce time to production."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Though funding isn't flowing as freely into data management as it once was, the startup is attracting interest by focusing on enabling customers to unify data for AI development. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1250581414.jpg https://www.techtarget.com/searchdatamanagement/news/366639042/SurrealDB-raises-23M-launches-update-to-fuel-agentic-AI Tue, 17 Feb 2026 09:51:00 GMT <p>Data visualization translates information into charts, maps or other graphics to show patterns, trends and outliers in a way that can be grasped quickly. The goal is to make complex data easier to understand and act on.</p> <p>Data visualization is a core component of both business intelligence (<a href="https://www.techtarget.com/searchbusinessanalytics/definition/business-intelligence-BI">BI</a>) and&nbsp;<a href="https://www.techtarget.com/searchenterpriseai/definition/data-science">data science</a>&nbsp;applications. It spans the data analytics process, from initial data exploration to communicating the final results. Visualizing data helps analysts find relationships in data sets, validate analytics models, track key performance indicators (<a href="https://www.techtarget.com/searchbusinessanalytics/definition/key-performance-indicators-KPIs">KPIs</a>), then explain findings to business executives and other end users.</p> <p>BI and data science teams commonly embed data visualizations in interactive <a href="https://www.techtarget.com/searchbusinessanalytics/definition/business-intelligence-dashboard">business intelligence dashboards</a> and static reports. Visualization also supports more elaborate <a href="https://www.techtarget.com/searchcio/definition/data-storytelling">data storytelling</a>, which combines data, narrative and visuals to offer insights and inform decisions. In addition, business users often create visualizations themselves in <a href="https://www.techtarget.com/searchbusinessanalytics/definition/self-service-business-intelligence-BI">self-service BI</a> environments.</p> <p>Virtually every professional discipline relies on data visualization. Corporate executives monitor performance and inform stakeholders. Marketers optimize campaigns. Supply chain managers track shipments and manage inventory. Computer scientists explore advancements in artificial intelligence (<a href="https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence">AI</a>).</p> <p>Without data visualization, the meaning of BI data would be less obvious to business users. It's central to&nbsp;<a href="https://www.techtarget.com/searchbusinessanalytics/definition/advanced-analytics">advanced analytics</a>&nbsp;for similar reasons. When data scientists build predictive analytics or&nbsp;<a href="https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML">machine learning</a>&nbsp;models, visualizing the outputs helps them monitor results and confirm that the models perform as intended more easily than interpreting raw numerical data. Visualization also plays a key role in&nbsp;<a href="https://www.techtarget.com/searchdatamanagement/definition/big-data">big data</a>&nbsp;projects, where businesses need to understand large volumes of data.</p> <section class="section main-article-chapter" data-menu-title="Why is data visualization important?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why is data visualization important?</h2> <p>Data visualization provides a quick and effective way to communicate information that's critical to decision-making in organizations.</p> <p>While business professionals have different areas and levels of expertise, well-designed visualizations make data analysis easier to understand, showing at a glance what is happening or has changed in a particular area. Executives, business managers and operational employees can act faster based on this visual evidence.</p> <p>Among other things, visualizations help businesses to:</p> <ul class="default-list"> <li>Identify factors that influence customer behavior.</li> <li>Find products or services that need to be improved.</li> <li>Make information more memorable for stakeholders.</li> <li>Decide when and where to deploy specific products.</li> <li>Predict product demand, sales or revenue.</li> </ul> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/ba-data_visualization_timeline-f.png"> <img data-src="https://www.techtarget.com/rms/onlineImages/ba-data_visualization_timeline-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/ba-data_visualization_timeline-f_mobile.png 960w,https://www.techtarget.com/rms/onlineImages/ba-data_visualization_timeline-f.png 1280w" alt="Timeline of data visualization's progress throughout centuries" height="594" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>The timeline depicting the history of data visualization starts hundreds of years before the introduction of modern technology. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Benefits of data visualization"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Benefits of data visualization</h2> <p>While data visualization serves many purposes, it also supports several ways organizations interpret and act on data, including:&nbsp;&nbsp;</p> <ul class="default-list"> <li><b>Actionable insights.</b>&nbsp;BI dashboards and other data visualization tools help people absorb information quickly, gain better insights and take next steps faster.</li> <li><b>Exploration of complex relationships.</b>&nbsp;Modern analytics and visualization platforms reveal complex relationships across many variables to drive more data-based decisions.</li> <li><b>Clear storytelling.</b>&nbsp;Focused visuals and narratives maintain the audience's interest with information they can readily understand.</li> <li><b>Interactivity.</b>&nbsp;Users can drill into the data behind the charts for deeper analysis.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Data visualization challenges and disadvantages"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data visualization challenges and disadvantages</h2> <p>While data visualization is meant to offer clarity, it can introduce certain risks, including:</p> <ul class="default-list"> <li><b>Complexity.</b>&nbsp;Dense visuals obscure data insights. Without visualization training, there's an increased risk that analysts and business users will create cluttered designs or the wrong visual type for the data.</li> <li><b>Misinterpretation.</b>&nbsp;Users might draw incorrect conclusions if visualizations aren't fully clear or if they don't examine the data closely enough.</li> <li><b>Data privacy and security.</b>&nbsp;Organizations face risks related to both the security of data visualization platforms and compliance with data privacy regulations.</li> <li><b>Bias.</b>&nbsp;Visualizations and the data behind them should be scrutinized for signs of bias. Failing to do so could compromise the credibility of the analytics results.</li> <li><b>Environmental impact. </b>Visuals generated with AI often require massive computing power, which can increase energy consumption and affect corporate sustainability goals.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Data visualization, AI and big data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data visualization, AI and big data</h2> <p>Companies increasingly use machine learning and other AI tools to &nbsp;process massive amounts of data that can be difficult and time-consuming to sort through and analyze without their help. While these AI-driven analytics applications in big data environments offer new opportunities to present information to stakeholders, they also require new data visualization approaches.</p> <p>Conventional visualizations, such as pie charts,&nbsp;<a href="https://www.techtarget.com/searchsoftwarequality/definition/histogram">histograms</a>&nbsp;and graphs, remain useful for summaries but are limited for large-scale data exploration. For deeper discovery, modern big-data platforms pair advanced visuals -- for example, heat maps and scatter plots -- with cloud-powered AI to automatically highlight trends or new opportunities that might otherwise remain buried in the data. This architecture keeps visual designs fast and clear, even when they're built on massive, streaming data sets.</p> <p>Despite their potential value, data visualization projects on big data platforms have drawbacks, including:</p> <ul class="default-list"> <li><b>Information overload. </b>Complex visualizations can confuse users or lead them to make inaccurate conclusions.</li> <li><b>Technical maintenance. </b>Modern data pipelines require constant maintenance to prevent dashboards from breaking. IT teams must also monitor cloud costs.</li> <li><b>Lack of trust. </b>Without governance and transparency, AI-generated insights are opaque, making it difficult to gain user confidence in them.</li> <li><b>Need for advanced skills. </b>Creating sophisticated visuals requires specialized skills. In addition, business users need data literacy skills to understand complex analytics results.</li> <li><b>Data quality issues. </b>Manually checking data quality in big data systems is not feasible. Organizations need automated tools to identify quality and accuracy issues before data reaches dashboards.</li> <li><b>Hidden bias. </b>Even technically accurate visualizations can mislead users without proper context or when data sample sizes are small.</li> </ul> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/Vy1axaMB980?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="Examples of data visualization"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Examples of data visualization</h2> <p>&nbsp;Tables, bar charts, pie charts and other traditional data visualization methods are still widely used for their simplicity and accessibility. However, <a href="https://www.techtarget.com/searchbusinessanalytics/feature/8-top-data-science-applications-and-use-cases-for-businesses">data science applications</a> and data storytelling often demand more advanced visualization techniques, such as bullet graphs for tracking performance, heat maps for identifying patterns, bubble charts for analyzing relationships among variables and Sankey diagrams for visualizing flows and processes.</p> <p>Other types of data visualizations that continue to be popular include:</p> <ul class="default-list"> <li><b>Line charts.&nbsp;</b>Among the most basic and widely used techniques, line charts show how variables change over time, making them ideal for trend analysis.</li> <li><b>Area charts.&nbsp;</b>A variation of line charts, area charts fill the space between the x-axis and a line with color or shading. This design shows both how data changes over time and the total volume of the values; different groups of values can also be compared. Some common examples of area charts include tracking population growth or total sales over a specific time period.&nbsp;&nbsp;</li> <li><b>Treemaps.&nbsp;</b>As the name suggests, treemaps use a tree-like structure to show hierarchical data and how the parts add up to the whole through nested and often color-coded rectangles. The space-filling design reveals patterns or outliers in complex data sets. One example of a treemap is a company's entire budget broken down by department, using different-sized rectangles to show the percentage given to each department.</li> <li><b>Scatter plots.&nbsp;</b>This type of chart shows the relationship between two different variables. Each observation is represented by a dot placed on the chart based on its values for the two variables: one on the x-axis and the other on the y-axis. Scatter plots are helpful to find patterns in data, such as correlations and outliers. For example, a scatter plot can show the connection between the number of hours studied on the x-axis and final test scores on the y-axis; an upward trend would indicate that more studying is associated with higher test scores.</li> <li><b>Population pyramids.&nbsp;</b>These use back-to-back bar charts to display a population's distribution by age, sex and other characteristics. They can show trends that identify demographic shifts driven by events, such as mass migrations or health crises. Business uses include analyzing spending by customer groups, planning retail locations and tracking workforce demographics.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Common data visualization use cases"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Common data visualization use cases</h2> <p>Across industries, teams use data visualization to see what has changed, why it changed and what to do next.</p> <ul class="default-list"> <li><b>Sales and marketing. </b>Interactive dashboards track real-time sales performance. Trend lines show customer engagement. Comparative charts highlight marketing campaigns with the highest ROI. Modern analytics platforms integrate AI to provide predictive forecasting that helps marketers anticipate future trends and optimize budget allocation.</li> <li><b>Healthcare.&nbsp;</b>Choropleth maps show health indicators across regions, such as heart disease mortality rates by state or county, to highlight geographic disparities. These visualizations help healthcare organizations and public health officials identify areas that require more resources. Hospitals also use data visualizations to help diagnose medical conditions and track patient treatments and outcomes.</li> <li><b>Financial services.</b> Finance professionals rely on data visualization to track asset performance and decide when to execute trades. Candlestick charts are the industry standard for visualizing price movements within a specified time frame to show market trends and reactions. In addition, visualized data helps banks analyze credit risks and customer portfolios.</li> <li><b>Insurance.</b> Insurers similarly use data visualizations to analyze policy risks and pricing when customers apply for coverage.</li> <li><b>Manufacturers.</b> Data visualization helps them plan production, monitor manufacturing operations and manage inventories of materials and supplies.</li> <li><b>Logistics.&nbsp;</b>Route optimization dashboards combine real-time data on traffic, weather and inventory to pinpoint the fastest and most cost-effective distribution paths. These visualizations help logistics organizations reduce delivery times, save on fuel costs and better manage their vehicle fleets.</li> <li><b>Travel.</b> Airlines use visualizations of data on ticket sales and flight occupancy rates to adjust flight schedules and plan crew assignments. Similarly, hotel chains track room occupancy and pricing data to guide marketing campaigns and promotions aimed at maximizing bookings.</li> <li><b>Politics.&nbsp;</b>A bivariate choropleth map visualizes voting patterns with demographic overlays showing income or education levels. Time series charts track trends in polling numbers and campaign fundraising to help politicians determine where their messaging yields the most results.</li> <li><b>Scientific research.</b> Scientific visualization turns complex data from experiments and other data collection initiatives into high-dimensional charts and 3D models. These provide researchers with clearer ways to present scientific findings, such as molecular structures or atmospheric changes.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="The science of data visualization"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The science of data visualization</h2> <p>Data visualization is grounded in how humans process information. Psychologist Daniel Kahneman, building on decades of collaborative research with colleague Amos Tversky, defined the concept of two systems of thinking in his book <i>Thinking, Fast and Slow</i>:</p> <ul class="default-list"> <li><b>System 1 (fast thinking).</b> Quick, automatic and intuitive processing that requires little conscious effort. We use it for everyday tasks, such as reading a sign, solving simple math problems, detecting aggression in voices and distinguishing between colors.</li> <li><b>System 2 (slow thinking).</b> Deliberate and logical processing that requires intentional mental engagement. This system is used for more involved tasks, such as solving complex math problems, preparing tax forms and parking in a tight space.</li> </ul> <p>Research published by MIT in 2025 shows that data visualization design choices convey social meaning and can shape levels of trust before people even check the data. For example, the MIT researchers found that highly polished data visualizations were often perceived as promotional and less trustworthy, whereas plain designs were perceived as more objective. The researchers suggested that designers must account for both cognitive processing and social meaning when creating data visualizations.</p> </section> <section class="section main-article-chapter" data-menu-title="Data visualization tools and vendors"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data visualization tools and vendors</h2> <p>In the past, data visualization was often limited to using Microsoft Excel to convert spreadsheet data into tables and charts. Modern <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-business-intelligence-tools-to-know-about">BI and analytics platforms</a>, such as Looker Studio, Power BI, Qlik Sense and Tableau, and open-source visualization libraries, such as D3, Matplotlib and Plotly, have transformed data visualization. These tools connect to governed cloud data to prepare it for analysis, then deliver interactive dashboards, reports, alerts and AI-generated insights.</p> <p>Many now have a variety of AI features, such as automated trend detection, <a href="https://www.techtarget.com/whatis/definition/natural-language-query">natural language querying</a> and predictive analytics driven by machine learning. Tools with generative AI capabilities, such as Tableau Pulse and Power BI Copilot, tell users what has changed in data sets and why.</p> <p>BI and data visualization tools typically integrate directly with cloud data platforms, such as Google BigQuery, Snowflake, Databricks and Microsoft Fabric's OneLake. This architecture gives users a way to explore governed data in a unified environment that ensures KPI definitions are consistent across their organization.</p> <p>For guidance on tool purchases, buyers can consult the vendor rankings and analysis in Gartner Magic Quadrant and Forrester Wave reports on BI platforms. Some notable vendors listed in the 2025 versions of those reports include AWS, Domo, Google, Microsoft, Oracle, Qlik, ThoughtSpot and Salesforce, which owns Tableau.</p> </section> <section class="section main-article-chapter" data-menu-title="New and emerging trends in data visualization"> <h2 class="section-title"><i class="icon" data-icon="1"></i>New and emerging trends in data visualization</h2> <p>Leaders want answers quickly, so visualization is moving from passive to active thanks to these technological advances.</p> <ul class="default-list"> <li><b>AI</b><b>‑driven visualization. </b>Tools now turn natural language queries into charts and dashboards. This shift toward on-the-fly visual creation speeds up results for business leaders who would otherwise wait for data analysts to provide answers.</li> <li><b>Augmented analytics.</b> Tools automatically detect issues and trends, explaining them with quick visuals, so teams can act faster. Recent research from analysts and business schools finds that more enterprises are deploying agent‑style assistants that use governance as the guardrail to ensure answers are trustworthy.</li> <li><b>Real</b><b>‑time streaming dashboards. </b>Users can watch live KPIs with continuous updates rather than a daily refresh. As needed, they can click into a metric to see details and set rules, such as triggering an alert via Microsoft Teams when a threshold is crossed. This avoids the need for multiple tools and speeds response in areas such as <a href="https://www.techtarget.com/iotagenda/definition/Internet-of-Things-IoT">IoT</a> monitoring, fraud detection and logistics tracking.</li> <li><b>Immersive visualization.</b> Augmented reality and virtual reality technologies help users <a target="_blank" href="https://www.nist.gov/information-technology/extended-reality" rel="noopener">explore</a> 3D or spatial data when static charts hide key relationships, such as factory layouts and geospatial routes. AR/VR lets teams inspect data at true scale, toggle layers and capture context for design reviews and fieldwork.</li> </ul> <p><i>Data visualization is a subset of the broader concept of data analytics. Learn the different ways in which&nbsp;</i><a href="https://www.techtarget.com/searchbusinessanalytics/tip/366542263/8-ways-to-drive-business-value-with-advanced-analytics"><i>advanced analytics tools drive business value</i></a><i>.</i></p> <p><i>Former TechTarget editors Cameron Hashemi-Pour, Kate Brush and Ed Burns also contributed to this article.</i></p> </section> Data visualization translates information into charts, maps or other graphics to show patterns, trends and outliers in a way that can be grasped quickly. https://cdn.ttgtmedia.com/visuals/digdeeper/5.jpg https://www.techtarget.com/searchbusinessanalytics/definition/data-visualization Tue, 17 Feb 2026 09:15:00 GMT <p>The volume and variety of enterprise data collected for analytics and AI applications continue to increase. To gain valuable business insights from these complex data assets, organizations are also increasingly investing in data science tools and other data management and analytics technologies.</p> <p>For example, in a survey conducted by the Data &amp; AI Leadership Exchange in late 2025, 91% of chief data officers and other senior executives from 109 large businesses said their organizations are spending more money on data and AI initiatives. Ninety-seven percent said such investments are delivering measurable business value, according to a report on the annual survey <a target="_blank" href="https://static1.squarespace.com/static/62adf3ca029a6808a6c5be30/t/6942c3cb535da44088c2dbff/1765983179572/2026+AI+%26+Data+Leadership+Executive+Benchmark+Survey+Final.pdf" rel="noopener">published</a> in January 2026.</p> <p>A wide range of technologies can be used in <a href="https://www.techtarget.com/searchbusinessanalytics/feature/8-top-data-science-applications-and-use-cases-for-businesses">data science applications</a>. To help data leaders choose the right ones to achieve their organization's business goals, here are 18 top data science tools, listed in alphabetical order with details on their features and capabilities. The list was compiled by TechTarget editors based on research of available technologies and market analysis from Forrester Research and Gartner.</p> <section class="section main-article-chapter" data-menu-title="1. Apache Spark"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. Apache Spark</h2> <p>Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has made it a widely used platform since it was created in 2009, resulting in the Spark project being one of the largest open source communities among <a href="https://www.techtarget.com/searchdatamanagement/feature/15-big-data-tools-and-technologies-to-know-about">big data technologies</a>.</p> <p>Due to its speed, Spark is a good fit for <a href="https://www.techtarget.com/searchbusinessanalytics/definition/continuous-intelligence">continuous intelligence</a> applications driven by near-real-time processing of streaming data. However, it's a general-purpose distributed processing engine that's equally suited for SQL batch jobs, such as extract, transform and load processes. In fact, Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.</p> <p>Spark is still <a href="https://www.techtarget.com/searchdatamanagement/feature/Hadoop-vs-Spark-Comparing-the-two-big-data-frameworks">often used with Hadoop</a>, but it also runs standalone on top of other file systems and data stores. It features an extensive set of developer libraries and APIs, including a machine learning library and support for Python, Scala, Java, and R in addition to SQL. These capabilities make it easier for data scientists and analysts to develop Spark applications.</p> </section> <section class="section main-article-chapter" data-menu-title="2. D3"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. D3</h2> <p>Another open source tool, D3 is a JavaScript library for creating custom data visualizations in a web browser. Short for <i>data-driven documents</i>, D3 uses web standards such as HTML, Scalable Vector Graphics and CSS rather than its own graphical vocabulary. D3's developers describe it as a flexible tool that enables users to design dynamic, interactive visualizations.</p> <p>First released in 2011 and originally known as D3.js, the tool lets visualization designers use the Document Object Model API to bind data to documents representing the contents of a graphic; DOM manipulation methods can then be applied to make data-driven transformations to the documents. Animations, annotation capabilities and user-interaction features such as panning and zooming can be built into visualizations.</p> <p>D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, even basic charts might require significant coding -- and many data scientists don't have JavaScript skills. As a result, they might be more comfortable with Tableau, Power BI or another commercial data visualization tool, while D3 is used by data visualization developers and specialists who are also members of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/How-to-structure-and-manage-a-data-science-team">data science teams</a>.</p> </section> <section class="section main-article-chapter" data-menu-title="3. IBM SPSS"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. IBM SPSS</h2> <p>IBM SPSS is a family of software for managing and analyzing complex statistical data and creating predictive models. It includes two primary products, IBM SPSS Statistics and IBM SPSS Modeler, plus several others that work with or incorporate them. IBM acquired the technologies when it bought SPSS Inc. in 2009.</p> <p>SPSS Statistics is a statistical analysis tool that helps users identify complex relationships, patterns and trends in data. It also supports <a href="https://www.techtarget.com/searchbusinessanalytics/definition/data-preparation">data preparation</a>, predictive modeling and forecasting. The tool includes a menu-driven UI, its own command syntax and sets of Python and R extension commands that add analytics capabilities beyond its built-in ones. AI Output Assistant, a feature added in 2025, interprets tables, charts and statistical outputs, generates data visualizations and summarizes analytics results.</p> <p>SPSS Modeler is a <a href="https://www.techtarget.com/searchenterpriseai/feature/Data-science-vs-machine-learning-How-are-they-different">data science and machine learning</a> tool that focuses on data mining and predictive modeling. It's designed for ad hoc analytics applications that combine data from multiple sources, while SPSS Statistics is geared toward regular reporting on specific data sets. SPSS Modeler includes a drag-and-drop UI and supports various types of machine learning algorithms. It also provides model management and deployment capabilities and can run R extensions and Python scripts for Spark.</p> <p>Users can export prepared data from SPSS Statistics to SPSS Modeler and run predictive models created in SPSS Modeler in the statistical analysis tool.</p> </section> <section class="section main-article-chapter" data-menu-title="4. Julia"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Julia</h2> <p>Julia is an open source programming language used for numerical computing and data science applications, such as machine learning. In a 2012 blog post announcing Julia's initial release, its four creators said they set out to design a single language that met all their needs. A key goal was to avoid the need to write programs in one language and then convert them to another for execution.</p> <p>To that end, Julia combines the convenience of using a high-level dynamic language with performance that's comparable to statically typed languages, such as C and Java. Users don't have to define data types in programs, but an option allows them to do so. A multiple dispatch approach used at runtime also helps boost execution speed.</p> <p>The documentation for Julia notes that because its compiler differs from the interpreters in data science languages like Python and R, new users "may find that Julia's performance is unintuitive at first." But, it claims, "once you understand how Julia works, it is easy to write code that is nearly as fast as C."</p> </section> <section class="section main-article-chapter" data-menu-title="5. Jupyter Notebook/JupyterLab"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. Jupyter Notebook/JupyterLab</h2> <p><a href="https://www.techtarget.com/searchenterpriseai/tutorial/How-to-use-and-run-Jupyter-Notebook-A-beginners-guide">Jupyter Notebook</a> and JupyterLab are open source web applications that enable interactive collaboration among data scientists, data engineers, mathematicians, researchers and other users. They're computational notebook tools used to create, edit and share software code, as well as explanatory text, images and other information. For example, Jupyter users can add code, computations, comments and data visualizations to a single notebook document, which can then be shared with and revised by colleagues.</p> <p>As a result, notebooks "can serve as a complete computational record" of interactive sessions involving various data science team members, according to Jupyter Notebook's documentation. The notebook documents are JSON files with built-in version control capabilities. In addition, users can render notebooks as static webpages for viewing by people who don't have Jupyter installed on their systems.</p> <p>Jupyter Notebook was the original tool -- it was initially part of the open source IPython interactive toolkit project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name, but in addition to supporting those three languages, Jupyter provides modular kernels for dozens of others. JupyterLab is a web-based UI added in 2018 that's more flexible and extensible than Jupyter Notebook.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Keras"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Keras</h2> <p>Keras is a programming interface that simplifies the use of several popular machine learning platforms by data scientists. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow, PyTorch and JAX. Keras initially supported multiple back ends, then was tied exclusively to TensorFlow starting with its 2.4.0 release in 2020. However, multiplatform support was restored in Keras 3.0, a full rewrite released in late 2023.</p> <p>As a high-level API, Keras was designed to accelerate <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-build-a-machine-learning-model-in-7-steps">implementation of machine learning models</a> -- in particular, deep learning <a href="https://www.techtarget.com/searchenterpriseai/definition/neural-network">neural networks</a> -- through a "quick and easy" development process, as the technology's documentation puts it. Keras enables data scientists to experiment during the model development process with less coding than other deep learning options require. Models can also be run on all the supported back-end platforms without any code changes.</p> <p>The Keras framework includes a sequential interface for creating relatively simple linear stacks of neural-network building blocks called <i>layers</i> with inputs and outputs, as well as a functional API for building more complex graphs of layers and writing deep learning models from scratch.</p> </section> <section class="section main-article-chapter" data-menu-title="7. Matlab"> <h2 class="section-title"><i class="icon" data-icon="1"></i>7. Matlab</h2> <p>Offered by software vendor MathWorks since 1984, Matlab is a high-level programming language and analytics platform for numerical computing, mathematical modeling and data visualization. It's primarily used by conventional engineers and scientists to analyze data, design algorithms and develop embedded systems for wireless communications, industrial control, signal processing and other applications. Users often pair it with a companion Simulink tool that offers model-based design and simulation capabilities.</p> <p>While Matlab isn't as widely used in data science applications as languages such as Python, R and Julia, it does support <a href="https://www.techtarget.com/searchenterpriseai/tip/AI-vs-machine-learning-vs-deep-learning-Key-differences">machine learning and deep learning</a>, predictive modeling, <a href="https://www.techtarget.com/searchbusinessanalytics/feature/6-big-data-benefits-for-businesses">big data analytics</a>, computer vision and other work done by data scientists. Data types and high-level functions built into the platform are designed to speed up exploratory data analysis and data preparation in analytics applications.</p> <p>Matlab -- short for <i>matrix laboratory</i> -- is considered relatively easy to learn and use. The platform includes prebuilt applications but also lets users build their own. It also provides a library of add-on toolboxes with discipline-specific software and hundreds of built-in functions, including the ability to visualize data in 2D and 3D plots.</p> </section> <section class="section main-article-chapter" data-menu-title="8. Matplotlib"> <h2 class="section-title"><i class="icon" data-icon="1"></i>8. Matplotlib</h2> <p>Matplotlib is an open source Python plotting library that's used to visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib. It works in Python scripts, the Python and IPython shells, Jupyter Notebook, JupyterLab, web application servers and various GUI toolkits.</p> <p>The library's large codebase can be challenging to master, but it's organized in a hierarchical structure that enables users to build visualizations primarily with high-level commands. The top component in the hierarchy is pyplot, a module that provides a state-machine environment and a set of simple plotting functions like those in Matlab.</p> <p>First released in 2003, Matplotlib also includes an object-oriented interface that supports low-level commands for more complex data plotting and can be used with pyplot or on its own. The library is primarily focused on creating 2D visualizations but offers an add-on toolkit with 3D plotting features.</p> </section> <section class="section main-article-chapter" data-menu-title="9. NumPy"> <h2 class="section-title"><i class="icon" data-icon="1"></i>9. NumPy</h2> <p>Short for <i>Numerical Python</i>, <a href="https://www.techtarget.com/whatis/definition/What-is-NumPy-Explaining-how-it-works-in-Python">NumPy</a> is an open source Python library that's used widely in scientific computing as well as data science and machine learning applications. The library consists of multidimensional array objects and processing routines that enable various mathematical and logic functions. It also supports linear algebra, random number generation and other operations.</p> <p>One of NumPy's core components is the N-dimensional array, or ndarray, which represents a collection of items that are the same type and size. An associated data-type object describes the format of the data elements in an array. The same data can be shared by multiple ndarrays, and data changes made in one can be viewed in another.</p> <p>NumPy was created in 2005 by combining and modifying elements of two earlier libraries. It's generally considered one of the most useful Python libraries due to its numerous built-in functions. NumPy is also known for its speed, which partly results from the use of optimized C code at its core. In addition, various other Python libraries are built on top of NumPy.</p> </section> <section class="section main-article-chapter" data-menu-title="10. Pandas"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10. Pandas</h2> <p>Another popular open source Python library, pandas is used to manipulate and analyze data. Built on top of NumPy, it features two primary data structures: Series, a one-dimensional array that holds data of any type, and DataFrame, a two-dimensional structure that can contain columns of different data types and supports data manipulation with integrated indexing. Both accept data from NumPy ndarrays and other inputs. A DataFrame can also incorporate multiple Series objects.</p> <p>Created in 2008, pandas provides built-in data visualization capabilities and exploratory data analysis functions. It supports file formats and languages such as CSV, SQL, HTML and JSON. Additional features include data aggregation and transformation, integrated handling of missing data and the ability to quickly merge and join data sets.</p> <p>To optimize its performance, key code paths in pandas are written in C or Cython, a superset of Python designed to provide C-like performance. The library can be used with various kinds of analytical and statistical data, including tabular, time series and text data sets.</p> </section> <section class="section main-article-chapter" data-menu-title="11. Python"> <h2 class="section-title"><i class="icon" data-icon="1"></i>11. Python</h2> <p>Python is the most widely used programming language for data science applications and scientific and numeric computing, and one of the most popular languages overall. The Python open source project's website describes it as a high-level interpreted, interactive, object-oriented language with a simple syntax, built-in data structures, and dynamic typing and binding capabilities. Python also supports both procedural and functional programming, as well as extensions written in C or C++.</p> <p>The multipurpose language is used for a wide range of data-driven tasks, including data analysis, data visualization, AI, <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-natural-language-processing-vs-machine-learning">natural language processing</a> and robotic process automation. Python includes an extensive library of functions and modules that can streamline application development, and thousands of third-party modules are available in the Python Package Index repository.</p> <p>Python 3.x is the recommended version for production use. Older Python 2.x releases can still also be downloaded from the Python website, but maintenance and technical support for the 2.x line ended in 2020.</p> </section> <section class="section main-article-chapter" data-menu-title="12. PyTorch"> <h2 class="section-title"><i class="icon" data-icon="1"></i>12. PyTorch</h2> <p>PyTorch is an open source Python library used to <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-PyTorch-vs-TensorFlow-for-AI-and-machine-learning">build and train deep learning models</a> based on neural networks. It was designed to be easier to use than Torch, a precursor machine learning framework written primarily in the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators.</p> <p>First released in 2017, PyTorch uses array-like tensors to encode model inputs, outputs and parameters. Its tensors are similar to NumPy's multidimensional arrays, which can be converted into tensors for processing in PyTorch, and vice versa. By default, PyTorch runs in an "eager mode" that executes computational operations immediately, an approach suited to model development. But operations can also be combined into computational graphs to deliver higher performance in production deployments.</p> <p>Other PyTorch components include an automatic differentiation package; a module for building neural networks; and ExecuTorch, a tool for deploying models on mobile phones and edge devices. In addition to the main Python API, PyTorch provides a C++ one that can be used as a separate front-end interface or to create extensions for Python applications. Users can run models built in PyTorch on CPUs, GPUs and custom hardware accelerators.</p> </section> <section class="section main-article-chapter" data-menu-title="13. R"> <h2 class="section-title"><i class="icon" data-icon="1"></i>13. R</h2> <p>The <a href="https://www.techtarget.com/searchbusinessanalytics/definition/R-programming-language">R programming language</a> is an open source environment designed for statistical computing and graphics applications as well as data manipulation, analysis and visualization. Many data scientists, academic researchers and statisticians use R to retrieve, cleanse, analyze and present data, making it one of the most popular languages for data science and advanced analytics.</p> <p>Thousands of user-created packages with libraries of code that enhance R's functionality are also available. One example is ggplot2, a well-known package for creating graphics that's part of the tidyverse collection of R-based data science tools. In addition, multiple vendors offer integrated development environments and commercial code libraries for R.</p> <p>R is an interpreted language, like Python, and it has a reputation for being relatively intuitive. It was created in the 1990s as an alternative version of S, a statistical programming language developed in the 1970s. R's name is both a play on S and a reference to the first letter of the names of its two creators.</p> </section> <section class="section main-article-chapter" data-menu-title="14. SAS"> <h2 class="section-title"><i class="icon" data-icon="1"></i>14. SAS</h2> <p>SAS is an integrated software suite for statistical analysis, advanced analytics, AI, BI and data management. Developed and sold by software vendor SAS Institute Inc., the platform helps users integrate, cleanse, prepare and manipulate data, then analyze it using different <a href="https://www.techtarget.com/searchbusinessanalytics/feature/15-common-data-science-techniques-to-know-and-use">statistical and data science techniques</a>. SAS supports a range of analytics tasks, from basic BI and data visualization to risk management, operational analytics, data mining, predictive analytics and machine learning.</p> <p>SAS development began in 1966 at North Carolina State University. Its use began to grow in the early 1970s, and SAS Institute was founded in 1976 as an independent company. The software was initially built for use by statisticians -- SAS was short for Statistical Analysis System. But over time, the SAS platform expanded to include a broad set of functionality.</p> <p>Development and marketing are now focused primarily on SAS Viya, a cloud-based version of the platform that was launched in 2016 and redesigned to be cloud-native in 2020. Viya supports Python, R, Java, Lua and REST APIs for programming. It also includes built-in AI governance features and SAS Viya Copilot, a conversational AI assistant that uses Microsoft Foundry services to help users generate SAS code and build AI and analytics models.</p> </section> <section class="section main-article-chapter" data-menu-title="15. Scikit-learn"> <h2 class="section-title"><i class="icon" data-icon="1"></i>15. Scikit-learn</h2> <p>Scikit-learn is an open source Python machine learning library that's built on the SciPy and NumPy scientific computing libraries and Matplotlib for plotting data. It supports both <a href="https://www.techtarget.com/searchenterpriseai/feature/Comparing-supervised-vs-unsupervised-learning">supervised and unsupervised machine learning</a> and includes numerous algorithms and models, called <i>estimators</i> in scikit-learn parlance. It also provides functionality for model fitting, selection and evaluation, as well as data preprocessing and transformation.</p> <p>Initially called scikits.learn, the library began as a Google Summer of Code project in 2007 and was publicly released in 2010. The first part of its name is short for <i>SciPy toolkit</i> and is also used by other SciPy add-on packages. Scikit-learn primarily works on numeric data that's stored in NumPy arrays or SciPy sparse matrices.</p> <p>The library's suite of tools also enables other tasks, such as loading data sets and creating workflow pipelines that combine data transformer objects and estimators. But scikit-learn has some limits due to design constraints. For example, it doesn't support deep learning or <a href="https://www.techtarget.com/searchenterpriseai/definition/reinforcement-learning">reinforcement learning</a>, and GPUs aren't supported by default. The library's website also says its developers "only consider well-established algorithms for inclusion."</p> </section> <section class="section main-article-chapter" data-menu-title="16. SciPy"> <h2 class="section-title"><i class="icon" data-icon="1"></i>16. SciPy</h2> <p>SciPy is another open source Python library that supports scientific computing. Short for <i>Scientific Python</i>, it features a set of mathematical algorithms and high-level commands and classes for data manipulation and visualization. The library is organized into more than a dozen subpackages that contain algorithms and functions for different scientific computing domains. That includes areas such as data optimization, integration and interpolation, as well as clustering, image processing and statistics.</p> <p>SciPy is built on top of NumPy and can operate on NumPy arrays. But it extends beyond NumPy's capabilities by providing additional array computing tools and specialized data structures, including sparse matrices and K-dimensional trees.</p> <p>SciPy also predates NumPy: It was created in 2001 by combining multiple add-on modules from the Numeric library, one of NumPy's two predecessors. Like NumPy, SciPy uses compiled code to optimize performance. In its case, most of the performance-critical parts of the library are written in C, C++ or Fortran.</p> </section> <section class="section main-article-chapter" data-menu-title="17. TensorFlow"> <h2 class="section-title"><i class="icon" data-icon="1"></i>17. TensorFlow</h2> <p>TensorFlow is an open source machine learning platform developed by Google that's particularly popular for building deep learning neural networks. Like PyTorch, TensorFlow structures data inputs as tensors akin to NumPy multidimensional arrays. It supports the same two processing methods as PyTorch, but in reverse: By default, TensorFlow creates computational graphs to flow data through a set of operations specified by developers, while also offering an eager execution programming environment that runs operations individually.</p> <p>Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and incorporates Keras as a high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations -- <i>ops</i>, for short -- can be built in C++.</p> <p>The platform also includes TFX, a module initially called TensorFlow that automates the deployment of production machine learning pipelines. In addition, it supports LiteRT, a runtime tool for mobile and IoT devices. TensorFlow models can run on CPUs, GPUs and Google's special-purpose Tensor Processing Units.</p> </section> <section class="section main-article-chapter" data-menu-title="18. Weka"> <h2 class="section-title"><i class="icon" data-icon="1"></i>18. Weka</h2> <p>Weka is an open source workbench that provides a collection of <a href="https://www.techtarget.com/whatis/definition/machine-learning-algorithm">machine learning algorithms</a> for use in data mining tasks. Weka's algorithms, called <i>classifiers</i>, can be applied directly to data sets without any programming via a GUI or a command-line interface that offers additional functionality. They can also be implemented through a Java API.</p> <p>The workbench can be used for classification, clustering, regression, and association rule mining applications. It also includes a set of data preprocessing and visualization tools. Weka supports integration with R, Python, Spark and other libraries, such as scikit-learn. For deep learning uses, an add-on package combines it with the Eclipse Deeplearning4j library.</p> <p>Weka is free software licensed under the GNU General Public License. It was developed at the University of Waikato in New Zealand starting in 1992. An initial version was rewritten in Java to create the current workbench, which was first released in 1999. Weka stands for the Waikato Environment for Knowledge Analysis. It is also the name of a flightless bird native to New Zealand that the technology's developers say has "an inquisitive nature."</p> </section> <section class="section main-article-chapter" data-menu-title="Data science and machine learning platforms"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data science and machine learning platforms</h2> <p>Numerous software vendors offer commercially licensed platforms that provide integrated functionality for machine learning, AI and other data science applications. These product offerings are diverse: They include machine learning operations hubs, <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-top-AutoML-tools-for-machine-learning-workflows">automated machine learning platforms</a> and full-function analytics suites, with some products combining MLOps, AutoML and analytics capabilities. Many of the platforms incorporate some of the data science tools listed above.</p> <p>IBM SPSS Modeler, Matlab and SAS can also be counted among the data science platforms. Other prominent platform options for data science teams include the following technologies:</p> <ul class="default-list"> <li>Altair RapidMiner.</li> <li>Alteryx One.</li> <li>Amazon SageMaker.</li> <li>Anaconda.</li> <li>Azure Machine Learning.</li> <li>BigML.</li> <li>Databricks Data Intelligence Platform.</li> <li>Dataiku.</li> <li>DataRobot.</li> <li>Domino Enterprise AI Platform.</li> <li>Google Cloud Vertex AI Platform.</li> <li>H2O AI Cloud.</li> <li>IBM Watson Studio.</li> <li>Knime.</li> <li>Qubole.</li> <li>Saturn Cloud.</li> </ul> <p>Some platforms, such as Dataiku and H2O, are also available in free open source or community editions. Knime combines an underlying open source analytics platform with a commercial Knime Business Hub software package that supports team-based collaboration and analytics workflow automation, deployment and management capabilities.</p> <p><b>Editor's note: </b><i>TechTarget editors updated this article in February 2026 for timeliness and to add new information.</i></p> <p><i>Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.</i></p> </section> Numerous tools are available for data science applications. Read about 18, including their features, capabilities and uses, to see if they fit your analytics needs. https://cdn.ttgtmedia.com/rms/onlineimages/keyboard_g1140860048.jpg https://www.techtarget.com/searchbusinessanalytics/feature/15-data-science-tools-to-consider-using Mon, 16 Feb 2026 00:00:00 GMT <p>Big data management and analytics tools are transformative technologies for companies of all sizes across various industries. For example, <a href="https://www.techtarget.com/searchdatamanagement/The-ultimate-guide-to-big-data-for-businesses">big data environments</a> give retailers detailed insights into their entire supply chain. Manufacturers can monitor and manage all the production equipment in their factories. Marketers in these and other industries can analyze every customer touchpoint, from website visits to phone calls, emails, chats and purchases.</p> <p>Yet there are still lots of questions -- and confusion -- about how to get the most out of <a href="https://www.techtarget.com/searchdatamanagement/feature/Building-a-big-data-architecture-Core-components-best-practices">big data architectures</a>. The following are six best practices that data management and analytics leaders should adopt when their organization decides to <a href="https://www.techtarget.com/searchdatamanagement/feature/15-big-data-tools-and-technologies-to-know-about">invest in big data technologies</a>.</p> <section class="section main-article-chapter" data-menu-title="1. Focus on business needs, not the technology"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. Focus on business needs, not the technology</h2> <p>Thanks to big data technologies, data management and analytics teams can handle data volumes and complex analytics applications that previously were beyond all but the most advanced companies and government agencies. However, organizations can get carried away by the technology, assuming that there must be an advantage to using any new tools or capabilities.</p> <p>For example, many businesses want to implement <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Examples-of-real-time-analytics-for-businesses">real-time analytics applications</a>. Analyzing data in real time as it's created and updated enables organizations to gain immediate insights into customer behavior, market trends and operational performance. But two business-related problems often make that a challenge:</p> <ol class="default-list"> <li>Data is generated and collected at a level of detail that many business users don't require.</li> <li>Even if big data systems deliver actionable real-time analytics, business processes and workflows don't enable users to make decisions at that pace. As a result, the actions of business executives and workers lag behind the data analysis.</li> </ol> <p>This mismatch between the flow of data and the cadence of business decisions can overload users with information that just gets in their way as they try to do their job. It also leads to unnecessary spending on analytics technology, when less immediate "right-time analytics" might better suit business rhythms.</p> <p>Big data is a valuable business asset, but it may well be a wasted one without <a href="https://www.techtarget.com/searchbusinessanalytics/feature/8-big-data-use-cases-for-businesses-and-industry-examples">strong use cases to justify deployments</a>.</p> </section> <section class="section main-article-chapter" data-menu-title="2. Incorporate AI into big data applications in sensible ways"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. Incorporate AI into big data applications in sensible ways</h2> <p>The ways AI is transforming data management and analytics processes should <a href="https://www.techtarget.com/searchenterpriseai/tip/How-do-big-data-and-AI-work-together">factor into big data strategies</a>. For example, AI tools can automate data preparation tasks and extract insights from text, images and other unstructured or semistructured data. Generative AI chatbots enable users to explore and analyze data through conversational natural language queries. They can also suggest issues to investigate in data sets and recommend appropriate data visualizations.</p> <p>However, we see the same pattern with AI as with real-time analytics. Agentic AI is a particular case in point. It's a technology with great promise: AI systems that <a href="https://www.techtarget.com/searchenterpriseai/feature/Real-world-agentic-AI-examples-and-use-cases">autonomously explore data, execute tasks and deliver insights</a> without explicit human direction. Vendors are embedding AI agents into data platforms and analytics tools, and numerous organizations have launched pilot and proof-of-concept projects. Yet many of those projects have failed to reach production use.</p> <p>Organizations that do well with agentic AI treat it as a tool for achieving desired business outcomes. They identify specific analytics workflows where agentic automation can deliver measurable business value, then redesign those workflows to accommodate how agents function rather than just replace existing human activities with AI ones.</p> <p>It's the same with incorporating other AI technologies into big data applications. Data management and analytics leaders should first ask about business needs, current pain points and how AI could streamline internal processes and improve decision-making. Technology choices follow from the answers they get.</p> </section> <section class="section main-article-chapter" data-menu-title="3. Collect lots of data for both current and future analytics uses"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. Collect lots of data for both current and future analytics uses</h2> <p>While the massive data volumes commonly collected in big data systems enable new types of analytics applications, data scientists and analysts often feel overwhelmed by all that data. Swamping even experienced analytics professionals with more data than they can comfortably work with certainly isn't something you should do. Indeed, many data lakes where big data is stored have become more like swamps, with sprawling data sets that are difficult to manage and analyze effectively.</p> <p>However, <a href="https://www.techtarget.com/searchdatamanagement/feature/Big-data-collection-processes-challenges-and-best-practices">collecting and using all that data</a> doesn't have to be a problem. Data science teams can use AI tools and machine learning algorithms to analyze big data volumes that are too large for conventional analytics techniques. The case for broad data collection grows stronger based on how AI learns. A large data repository provides the context that enables AI models and agents to understand an organization's business well enough to recommend useful actions.</p> <p>Data can also still be valuable even if it isn't used immediately. A <a href="https://www.techtarget.com/searchdatamanagement/feature/How-to-build-an-enterprise-big-data-strategy-in-4-steps">comprehensive big data strategy</a> collects data both to support business decision-making now and to be available for future analytics use cases and scenarios. Down the road, for example, data scientists might find patterns in consolidated streaming data sets that help them detect business problems or opportunities.</p> <p>But don't collect data indiscriminately or manage it haphazardly. While storage is relatively cheap, managing large amounts of data requires time and attention. Data sets without solid lineage documentation or <a href="https://www.techtarget.com/searchdatamanagement/feature/Proactive-practices-for-data-quality-improvement">data quality controls</a> due to a lack of data management resources are potential liabilities. Use input from business leaders to focus the collection process on data with immediate or expected future value, excluding data deemed not useful.</p> </section> <section class="section main-article-chapter" data-menu-title="4. Apply rigorous controls to track and manage data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Apply rigorous controls to track and manage data</h2> <p>Big data is typically diverse, with a variety of structured, unstructured, and semistructured data types. For example, audio files of customer support calls might be stored in a big data environment alongside related product images, documents and social media content, as well as business data such as transactions and account records. Big data systems also commonly contain data from sensors, emails, videos, logs and external data sources.</p> <p>This varied data also has diverse uses. Most organizations don't identify all the possible use cases for their big data environments in advance. Even if they do, they can't develop all the required analytics applications simultaneously.</p> <p>This reality initially made data lakes attractive. They enable raw data to be stored in native formats and structured as needed for specific analytics uses. Yet the promise of data lakes proved difficult to fulfill in practice. Heightening the swamp effect, organizations often lose track of what their data lake contains. Many also can't reliably track where data originated, how it was ingested or how it's been transformed.</p> <p>Newer data lakehouse architectures address these issues by combining the storage flexibility of data lakes with the more rigorous data management functions of traditional data warehouses. Open table formats, <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-Apache-Iceberg-is-essential-for-modern-data-lakehouses">such as Apache Iceberg and Delta Lake</a>, add transactional consistency and data versioning to previously ungoverned data storage. Data managers can maintain audit trails, enforce access controls and evolve schemas without disrupting analytics operations.</p> </section> <section class="section main-article-chapter" data-menu-title="5. Govern data for regulatory compliance and increased usability"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. Govern data for regulatory compliance and increased usability</h2> <p>In today's regulatory environment, strong data governance isn't optional. Organizations face a growing number of general data security and privacy laws, such as the EU's GDPR and the California Consumer Privacy Act. Some companies also must comply with industry regulations, such as HIPAA, which protects healthcare information in the U.S.</p> <p>AI regulations are also <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">now a factor to consider</a>. For example, new provisions of the EU AI Act scheduled to take effect in August 2026 require qualifying organizations deploying AI systems classified as high-risk to meet a set of data governance and management requirements, as well as risk management and human oversight obligations. Similar laws are progressing in many other jurisdictions.</p> <p>As a result, <a href="https://www.techtarget.com/searchdatamanagement/definition/data-governance">data governance processes</a> that support regulatory compliance efforts are an essential component of big data strategies. However, effective governance does more than just ensure an organization doesn't break the law. Well-governed data is also a better resource for analytics applications.</p> <p>Partly, this is a matter of confidence. If data is carefully administered within a governance framework, data scientists and analysts feel freer to explore and experiment with new analytics scenarios that could spur business innovation. Data that's properly defined, cataloged, secured and managed is also easier to work with and more likely to produce accurate analytics results.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Balance cost, data sovereignty and other issues in the cloud"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Balance cost, data sovereignty and other issues in the cloud</h2> <p>In most enterprises, the cloud is now the default IT infrastructure model for new systems and applications. But cloud deployments pose new data management issues, especially when big data environments span multiple cloud providers and geographic regions.</p> <p>While multi-cloud strategies offer resilience and the ability to choose data platforms and tools that best fit individual applications, they can increase processing costs and complicate data governance and management. Data sovereignty is also now a <a href="https://www.techtarget.com/searchsecurity/tip/Data-sovereignty-compliance-challenges-and-best-practices">pressing concern in many cloud implementations</a>. Governments worldwide are asserting jurisdiction over personal data within their borders by mandating local storage and restricting cross-border data transfers, among other measures. Local restrictions are also being applied to the data used in AI applications.</p> <p>As a result, a <a href="https://www.techtarget.com/searchcloudcomputing/definition/hybrid-cloud">hybrid cloud approach</a> isn't merely convenient but necessary for many organizations. In hybrid deployments, cloud systems are often used for most applications, while on-premises infrastructure is used for data workloads that must remain local due to privacy or AI regulations and applications running on hard-to-replace legacy systems.</p> <p>Data and IT leaders should balance all these factors -- data needs, cost efficiency, regulatory compliance, operational resilience and the flexibility to adapt systems and applications as business requirements change -- when they design cloud-based big data environments.</p> <p><b>Editor's note: </b><i>This article was updated in February 2026 for timeliness and to add new information.</i></p> <p><i>Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.</i></p> </section> These best practices can help data leaders create an effective big data strategy that meets an organization's analytics needs and delivers valuable business benefits. https://cdn.ttgtmedia.com/visuals/searchSAP/hardware_infrastructure/sap_article_015.jpg https://www.techtarget.com/searchbusinessanalytics/tip/6-essential-big-data-best-practices-for-businesses Wed, 11 Feb 2026 22:46:00 GMT <p>Although the terms <i>call center</i> and <i>contact center</i> are often used interchangeably, the distinction has become more consequential as organizations invest in omnichannel engagement, automation and AI-driven customer support.</p> <p>In a 2025 Gartner survey of service and support leaders, 77% said they feel pressure from senior executives to deploy AI, and 75% reported increased budgets for AI initiatives compared to the prior year. What was once a difference in channels now shapes technology strategy, data use and customer experience outcomes.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/definition/Call-Center">Call centers</a> were once the gold standard for customer service, but advances in digital communication, customer data platforms and automation have steadily reshaped how businesses interact with customers. That shift is reinforced by sustained enterprise investment: A 2024 Forrester survey found that 67% of AI decision-makers planned to increase spending on generative AI initiatives in the year ahead.</p> <p>As analog and simple telephone communication gave way to multiple digital channels, many call centers by necessity morphed into more complex, multifunctional <a href="https://www.techtarget.com/searchcustomerexperience/definition/contact-center">contact centers</a>.</p> <p>A call center consists of customer service professionals, known as call center agents, who handle inbound and outbound calls. Agents who take inbound calls&nbsp;help customers with account inquiries, scheduling, technical support, complaints and questions about products and services. Outbound calls focus on telemarketing, fundraising, lead generation, scheduling, customer retention and debt collection.</p> <p>Call centers continue to provide dependable, real-time customer service through voice interactions. However, they are typically optimized for phone-based workflows and limited customer context compared with modern contact centers.</p> <p>While many contact centers include traditional call handling, they are designed to orchestrate interactions across voice and digital channels, unify customer context and route engagements based on intent and history. By using multiple channels, <a href="https://www.techtarget.com/searchcustomerexperience/feature/Benefits-of-omnichannel-marketing">companies can collect more marketing data</a> and enable customers to interact with the business in more convenient ways.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f.png 1280w" alt="Difference between call centers and contact centers" height="333" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Call centers and contact centers share some similarities, but their differences are noteworthy. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <section class="section main-article-chapter" data-menu-title="Call centers vs. contact centers"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Call centers vs. contact centers</h2> <p>Call centers and contact centers provide customer service and outreach, but they differ in several key areas, including channels of communication, types of customer data collected, customer self-service (<a href="https://www.techtarget.com/whatis/definition/customer-self-service-CSS">CSS</a>) capabilities, agent skills and job requirements, and technologies and applications.</p> <h3>Channels of communication</h3> <p>Call centers emerged at a time before digital channels and they continue to use the phone as the major channel of communication. Still, they benefit many businesses because phone calls with live agents can offer a&nbsp;personalized experience&nbsp;that other channels often lack. However, the multiple channels provided by contact centers offer customers the convenience of interacting with a company on the channel of their choice.</p> <h3>Types of customer data collected</h3> <p>Because contact centers provide more communication channels than call centers, they can collect more diverse customer data, enhance&nbsp;<a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-profiling">customer profiling</a>, provide targeted customer support and improve customer experiences. Contact centers, for example, can use social media data to determine customer affiliations and attitudes that might not be apparent over the phone.</p> <p>Still, call centers can use speech analysis software to analyze phone calls and gain some degree of insight into a customer's behavior and buying patterns.</p> <h3>Customer self-service</h3> <p>For&nbsp;CSS capabilities, most call centers use interactive voice response (<a href="https://www.techtarget.com/searchcustomerexperience/definition/Interactive-Voice-Response-IVR">IVR</a>) systems -- automated phone assistants that respond to voices and keypad entries. IVR systems can route callers to relevant agents and perform simple tasks, such as reorders, but they can also annoy customers with lengthy menu options that fail to address specific needs.</p> <p>Contact center CSS&nbsp;goes beyond IVR and includes chatbots, FAQ webpages, forums and online knowledge bases to help customers resolve inquiries independently. Contact center CSS can also provide automated text messages that confirm or cancel appointments and mobile applications where customers can place or change orders. CSS tools can help reduce customer wait times, live agent workloads and operating costs.</p> <h3>Agent skills and job requirements</h3> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-agent-training-programs">Customer service skills and experience are essential</a> for call center and contact centers agents to solve problems and provide customers with the intangibles of empathy, patience and friendliness. Contact center agents require additional skills to handle interactions over multiple channels, including phone, email, live chat, text messaging and social media. Their job might require reading comprehension, sound writing skills, social media etiquette and multitasking capabilities.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg"> <img data-src="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg 960w,https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg 1280w" alt="multidimensional contact centers image" height="288" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact centers are seen as a multidimensional force for businesses. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Why the distinction matters now"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why the distinction matters now</h2> <p>As organizations adopt advanced analytics, automation and GenAI, the gap between voice-centric call centers and omnichannel contact centers continues to widen. Contact centers are increasingly treated as engagement platforms that unify data, AI and human agents across channels, rather than as expanded call-handling operations.</p> <h3>Technologies and applications</h3> <p>Automation is also changing expectations for customer support. According to Metrigy <a target="_blank" href="https://metrigy.com/the-evolving-role-of-ai-in-customer-experience-insights-from-metrigys-2024-25-study/" rel="noopener">research</a>, AI is fully automating roughly 20% of customer interactions today, and CX leaders expect that figure to rise to approximately 37% by 2028. As automation expands, contact centers require broader data integration, orchestration and governance capabilities that extend beyond traditional call center models.</p> <p>Aside from the basic requirements of phones, computers and headsets, call center technologies include the following:</p> <ul type="disc" class="default-list"> <li><strong>IVR.</strong>&nbsp;Automated phone assistants select the right agent or department to service a customer based on voice and keypad responses.</li> <li><strong>Automated call distributor (ACD).&nbsp;</strong>After an IVR determines the best route for the caller, an ACD automatically transfers the caller to that agent or department.</li> <li><strong>Speech analysis software.&nbsp;</strong>These tools can <a href="https://www.techtarget.com/searchcustomerexperience/tip/Sentiment-analysis-Why-its-necessary-and-how-it-improves-CX">analyze calls to detect customer emotions</a>, such as satisfaction and anger. They also determine when to follow up with unsatisfied customers.</li> <li><strong>Workforce management (WFM) system.</strong>&nbsp;Certain days in a call center can be busier than others. WFM systems can&nbsp;schedule the appropriate number of agents for each day.</li> <li><strong>Enhanced internet access.&nbsp;</strong>Agents who work remotely need a fast and secure connection to use call center software, which might require internet upgrades.</li> </ul> <p>Although some call center and contact center technologies overlap, the multifunctional aspects of contact centers, together with <a href="https://www.techtarget.com/searchcustomerexperience/opinion/How-contact-center-modernization-plays-into-AI-strategies">GenAI's penetration into the contact center</a>, dictate implementing additional technologies and applications, including the following:</p> <ul type="disc" class="default-list"> <li><strong>Email response management system.</strong>&nbsp;These systems can organize, track and archive large volumes of emails.</li> <li><strong>Omnichannel routing.&nbsp;</strong>Because contact centers use multiple channels, agents might struggle to manage various interactions. Omnichannel routing&nbsp;uses AI to identify a customer's intent&nbsp;and forward all requests to a live agent, regardless of the channel.</li> <li><b>Advanced analytics</b><strong>.</strong>&nbsp;This capability includes various AI technologies and analysis techniques, providing a holistic view of the customer journey and predictive insights into a customer's future choices.</li> <li><strong>Channel reports.&nbsp;</strong>Reporting software collects raw data across channels to create key performance indicators (KPIs), such as first contact resolution and customer effort scores.&nbsp;Managers can monitor KPIs&nbsp;to ensure quality assurance across channels.</li> </ul> <p>Gartner <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290" rel="noopener">forecasts</a> that agentic AI will autonomously resolve the majority of common customer service issues over time, reducing operational costs and reshaping agent roles. Against that backdrop, GenAI is expected to enhance automated customer support through chatbots and virtual assistants, personalize interactions with tailored responses, improve agent effectiveness with real-time assistance and simulation training, and accelerate content creation for FAQs and knowledge bases.</p> <p>For organizations evaluating customer support strategies, the difference between a call center and a contact center is no longer semantic. It reflects how customer interactions are captured, analyzed and acted on across the business. Companies that approach contact centers as integrated engagement platforms -- rather than as upgraded call centers -- are better positioned to scale service quality, govern automation responsibly and adapt to evolving customer expectations.</p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/SRKWbLNV4bs?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> <p><strong>Editor's note:</strong><i> This article has been updated to provide the latest information on call centers and contact centers</i><i> and provide enterprise technology buyers up-to-date insights on market advancements.</i></p> <p><i>Tim Murphy is a former site editor for TechTarget's Customer Experience and Content Management sites. He now covers broader CIO topics.</i></p> <p><i>Ron Karjian is an industry editor and writer at TechTarget covering business analytics, artificial intelligence, data management, security and enterprise applications.</i></p> </section> Call centers focus on voice support, while contact centers manage customer interactions across channels using shared data, automation and AI to shape modern CX strategies. https://cdn.ttgtmedia.com/rms/onlineimages/customer_service11.jpg https://www.techtarget.com/searchcustomerexperience/feature/Call-center-vs-contact-center-Whats-the-difference Wed, 11 Feb 2026 00:00:00 GMT <p>Contact centers sit at the intersection of customer experience, brand trust and operational efficiency. As customer expectations rise and AI becomes embedded in service operations, the challenges facing contact centers have grown more complex -- and more consequential.</p> <p>Customer service has moved beyond single-channel support, with contact centers now expected to manage interactions across voice and digital channels while maintaining consistency, context and speed. Contact centers have <a href="https://www.techtarget.com/searchcustomerexperience/feature/Call-center-vs-contact-center-Whats-the-difference">evolved beyond mere call-handling hubs</a> into sophisticated, multichannel engagement centers that play a vital role in shaping customer experiences. With the advent of digital transformation, contact centers now integrate various communication platforms, including phone calls, email, chat, social media and video conferencing.</p> <p>The commercial landscape for businesses and customers is rapidly changing, <a href="https://www.techtarget.com/searchcustomerexperience/feature/Important-contact-center-AI-features-and-their-benefits">driven by technological advancements</a>, evolving customer expectations and the increasing importance of personalized service. Enterprises are under pressure to deliver consistent, high-quality customer interactions over different modes of communication, while managing costs and maintaining operational efficiency.</p> <p>Customer interactions now span multiple channels, yet customers expect consistent context, personalization and responsiveness regardless of how they engage. This complex environment necessitates a strategic approach to managing contact centers, addressing inherent challenges and <a href="https://www.techtarget.com/searchcustomerexperience/How-to-choose-a-contact-center-software-system">using technology to enhance customer service capabilities</a>.</p> <section class="section main-article-chapter" data-menu-title="Key contact center challenges and remedies"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Key contact center challenges and remedies</h2> <p>Providing different modes of interaction is among the many challenges for modern contact centers. Other issues include agent attrition, increased customer expectations, ever-growing customer queues, generalization of content, barriers to understanding and security.</p> <h3>1. Meeting customer expectations</h3> <p>Customers expect quick, personalized and seamless interactions across all channels. They also expect an interaction in one channel to be consistent with the experience they've had in other channels. They increasingly demand high levels of service and are less tolerant of delays, repeating their information and impersonal responses.</p> <p>Advanced CRM systems and AI-driven analytics can help understand, contextualize and anticipate customer needs, <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-comprehensively-personalize-the-customer-experience">enabling more personalized and consistent interactions</a>. Regularly updating service protocols to align with customer feedback is equally important.</p> <p>Meeting these expectations increasingly depends on how well organizations unify customer data and govern AI-assisted interactions across channels, not just on agent performance alone.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f.png 1280w" alt="Contact center challenges and remedies" height="487" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>For every challenge confronting contact centers, there's a remedy. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>2. High contact volumes and longer wait times</h3> <p>Managing the high volumes of customer contacts, especially during peak times, can lead to long wait times and customer dissatisfaction. When customers call into contact centers of certain businesses, the first response they might typically get is a recording, "We're currently experiencing high call volumes" -- at least during normal business hours. This kind of experience, exacerbated by limited staffing and inefficient call routing, frustrates customers.</p> <p>Implementing intelligent call routing and queuing systems can optimize resource allocation and reduce wait times. Most new systems <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-manage-remote-call-center-agents">enable contact center agents to work from home</a>, which increases the flexibility of companies deploying agents globally. Self-service options, such as chatbots and automated responses, can reduce contact volumes, but they also raise expectations for the quality and efficiency of the interactions that reach live agents.</p> <p>Chatbots can handle routine types of interactions, like password resets, quick orders and simple questions, but complex situations that require empathy and understanding are still best left to humans. Improvements in machine learning and AI can also help mitigate high contact volumes and wait times and provide customers with other ways to resolve their queries independently.</p> <h3>3. Personalization shortfalls and content generification</h3> <p>Generic responses and interactions usually fail to meet customer expectations for personalized service. This lack of personalization inevitably results in decreased customer satisfaction and loyalty.</p> <p>Using <a href="https://www.techtarget.com/searchcustomerexperience/tip/Customer-interaction-analytics-spurs-better-business-results">customer data and analytics to tailor interactions</a> and recommendations can improve personalization, but doing so effectively requires strong data governance and consistent context across channels. Training call center agents to express empathy and use customer information effectively during their interactions is especially important. New large language models can improve the quality of agent responses by combining the specifics of customer data with best practices in knowledge bases.</p> <h3>4. Language barriers</h3> <p>Contact centers often serve a diverse, global customer base. Language barriers can impede effective communication, leading to misunderstandings and frustration. Any enterprise that aspires to be global must deal with this issue. Even companies that see themselves as local will become global when they put their presence on the web.</p> <p>Hiring multilingual agents and providing language training can bridge communication gaps. Additionally, real-time translation services and AI-powered language tools have come a long way and can facilitate smoother interactions.</p> <h3>5. Agent attrition</h3> <p>High turnover rates among contact center agents <a href="https://www.techtarget.com/searchcustomerexperience/tip/Why-contact-centers-have-high-turnover-and-how-to-combat-it">pose a significant challenge</a>. Increased job openings and competition for talent in good economies can only make this problem worse. Attrition is usually costly, impacting operational efficiency and the quality of customer interactions. Factors contributing to high attrition include job stress, lack of career advancement opportunities and inadequate compensation.</p> <p>In many environments, tool sprawl and cognitive overload also contribute to burnout, making technology simplification as important as compensation and career development.</p> <p>Good customer service is vital to retention and brand loyalty. <a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-agent-training-programs">Implementing comprehensive training programs</a>, offering competitive salaries and creating clear career progression paths can help reduce attrition. Providing a supportive work environment and recognizing agent contributions also play a crucial role in retaining talent. Technology has made it possible for more agents to work remotely, enabling companies to find the best qualified representatives wherever they're located.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f.png 1280w" alt="Contact center agent salaries in the U.S." height="403" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact center agents in some regions demand higher than average salaries. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>&nbsp;6. Lack of subject matter expertise</h3> <p>Agents often face complex queries requiring specialized knowledge. As the "first line of defense" in resolving customer inquiries, it's often difficult, if not impossible, for contact center agents to achieve mastery or even appear to be knowledgeable in all aspects of company products. The result could be incorrect or inadequate information conveyed to the customer.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/answer/5-ways-to-improve-call-center-agent-performance">Continuous training and access to a centralized knowledge base</a> can empower remote work agents with the necessary information to handle complex queries effectively. Encouraging collaboration and knowledge sharing among agents can also enhance overall understanding.</p> <h3>7. Quantitative and qualitative performance metrics</h3> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Top-7-call-center-agent-performance-metrics-to-track">Accurately measuring and analyzing contact center performance</a> is essential for continuous improvement. Traditional metrics often don't fully capture the quality of customer interactions or agent performance since measuring customer satisfaction can often be subjective.</p> <p>Adopting a comprehensive set of KPIs that include quantitative <i>and</i> qualitative metrics can provide a more accurate picture of performance. Incorporating customer feedback and sentiment analysis into performance reviews can also provide valuable insights and a more holistic view of contact center effectiveness.</p> <h3>8. Data access vs. protection</h3> <p>Contact centers store and handle sensitive customer information, making data security a foundational requirement for customer trust rather than a secondary compliance concern. As the types and frequency of interactions increase, breaches are becoming more frequent and consequential, leading to significant financial and reputational damage. More sophisticated deep fakes are rendering voice recognition ineffective as a method of customer verification.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-security-best-practices-to-protect-customer-data">Implementing comprehensive cybersecurity measures</a>, including encryption, multifactor authentication, and regular security audits, safeguard customer data. Sensitive customer data can be better protected through advanced security protocols, security tools such as system scanners with <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-train-agents-on-call-center-fraud-detection">data loss prevention, and fraud detection</a>. Most companies need to adopt zero trust architectures and principles, and agents need to be trained on data protection protocols. It should be standard practice to have a culture of security awareness, including periodic companywide security training.</p> <p>Across these challenges, AI increasingly acts as both a solution and a source of new complexity, raising the bar for data quality, governance and trust in contact center operations.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg"> <img data-src="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg 960w,https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg 1280w" alt="Multifunctional contact centers" height="288" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact centers are evolving into complex facilities that meet business and customer needs. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Build on flexibility, scalability and humanity"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Build on flexibility, scalability and humanity</h2> <p>Addressing contact center challenges requires more than incremental tooling changes. As customer expectations rise and AI reshapes service interactions, contact centers must balance efficiency with empathy, automation with oversight, and data access with security. Organizations that approach these challenges strategically -- rather than tactically -- are better positioned to turn their contact centers into long-term assets rather than ongoing cost centers.</p> <p><b>Editor's note:</b><i>&nbsp;This article has been updated to reflect the changing nature of modern contact center challenges.</i></p> <p><i>Jerald Murphy is senior vice president of research and consulting at Nemertes Research. He has more than three decades of technology experience, including neural networking research, integrated circuit design, computer programming, global data center designing and CEO of a managed services company.</i></p> </section> Modern contact centers face persistent challenges around customer expectations, staffing and data access. Addressing them requires more than incremental operational fixes. https://cdn.ttgtmedia.com/rms/onlineimages/chatbot_g1250576636.jpg https://www.techtarget.com/searchcustomerexperience/tip/Contact-center-challenges-and-how-to-overcome-them Wed, 11 Feb 2026 00:00:00 GMT <p>As agentic AI becomes the new means of building business intelligence tools and analyzing data, Qlik is keeping up with the competition.</p> <p>On Tuesday, the vendor made its agentic experience generally available in Qlik Cloud.</p> <p>The suite of capabilities includes the <a href="https://www.techtarget.com/searchbusinessanalytics/news/366623785/Qlik-unveils-agentic-AI-capabilities-launches-lakehouse">previously available Qlik Answers</a> to provide a natural language interface, powered by the Qlik Analytics Engine, for exploring and analyzing data -- structured <a target="_blank" href="https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data" rel="noopener">and unstructured</a> -- and Discovery Agent, a tool that monitors data and metrics for changes and anomalies so data teams can quickly act to address issues or take advantage of competitive opportunities.</p> <p>In addition, Qlik's agentic experience features a <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol</a> (MCP) server so that customers can connect AI applications they've developed to data in Qlik to inform decisions, and Data Products for Analysis, a feature that monitors curated, governed datasets to ensure quality.</p> <p>Beyond launching its agentic experience, Qlik recently joined <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">the Open Semantic Interchange</a> , a consortium of data management and analytics vendors committed to creating an open standard for semantic data modeling to make data more consistent and discoverable for AI.</p> <p>Vendors such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/366622614/Tableau-enters-the-agentic-AI-era-with-the-launch-of-Next">Tableau</a>, ThoughtSpot and Domo likewise provide natural language interfaces and agents that simplify complex, time-consuming analytics tasks. Similarly, numerous vendors -- including <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637466/GoodData-launches-MCP-server-to-fuel-AI-powered-analysis">GoodData</a> and Sisense along with the aforementioned trio -- now provide MCP servers.</p> <p>However, whether or not Qlik is the first to offer agentic AI features and tools that enable AI development, the capabilities are significant for the vendor's users and show that Qlik is keeping up with evolving AI trends, according to Mike Leone, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"This feels like the necessary evolution of what they started with Qlik Answers," he said. "We saw them tackle unstructured data first. Now they are connecting that brainpower to structured data and the agents people will be using every day via MCP. They understand the future is injecting trusted context directly into the messy reality of operational workflows rather than forcing users back into a dashboard."</p> <p>David Menninger, an analyst at ISG Research, likewise noted that Qlik's agentic experience shows that the vendor is evolving to meet current customer needs.</p> <p>"It's significant in the sense that agentic AI is the battlefield right now," he said. "Enterprises expect their software providers to be adding these types of features."</p> <p>Based in King of Prussia, Penn., Qlik is a longtime analytics vendor that added a data integration platform beginning with <a href="https://www.techtarget.com/searchbusinessanalytics/news/252445579/Qlik-Podium-acquisition-aims-to-boost-BI-data-management">its 2018 acquisition of Podium Data</a>. In response to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">surging interest in AI development</a> over the past three years, Qlik, like many data management and analytics vendors, has added AI capabilities and a suite for AI development that now includes its agentic experience.</p> <section class="section main-article-chapter" data-menu-title="AI-powered analytics"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI-powered analytics</h2> <p>A strong data foundation is a prerequisite for any AI initiative. Without high-quality data that can be trusted to inform agents and other AI tools, projects will never make it past the pilot stage. In fact, the absence of a trustworthy data foundation is one of the main reasons <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">many AI projects fail</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> We saw them tackle unstructured data first. Now they are connecting that brainpower to structured data and the agents people will be using every day via MCP. They understand the future is injecting trusted context directly into the messy reality of operational workflows rather than forcing users back into a dashboard. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>As interest in AI development has increased over the past few years, Qlik has made it a priority to help customers create a strong data foundation.</p> <p>The vendor <a href="https://www.techtarget.com/searchdatamanagement/news/366595446/Qlik-launches-Talend-Cloud-aims-to-ensure-data-is-trusted">launched Qlik Talend Cloud</a> in July 2024 to help users integrate their data, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366610199/Qlik-AutoML-update-targets-trust-with-visibility-simplicity">updated its AutoML capabilities</a> in September 2024 to provide greater visibility into machine learning model performance and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">introduced an AI Trust Score</a> in July 2025 to help customers understand the preparedness of their data.</p> <p>Now, with the general availability of its agentic experience, Qlik is delivering features that build on a strong data foundation to generate insights.</p> <p>Qlik Answers calls on an enterprise's data foundation to enable AI-powered data exploration and analysis, providing citations and explanations about how it reached its conclusion so users can audit responses. The MCP server enables agents to connect to trusted data to inform their actions. And the Discovery Agent continuously monitors the data foundation for potential insights.</p> <p>Meanwhile, Data Products for Analytics helps ensure that reusable <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">data remains trustworthy</a>.</p> <p>Regarding the impetus for developing the features that comprise the agentic experience, customer conversations and market observations were each factors, according to Drew Clarke, Qlik's executive vice president of product and technology.</p> <p>"Customer feedback was the spark, but the bigger driver is where enterprise AI is headed," he said. "Teams do not just want another chat interface. They want systems that can reason across analytics and documents, keep permissions intact and explain what they did."</p> <p>Although it has been generally available for a year-and-a-half, Qlik Answers is perhaps the most valuable feature of the new agentic experience given that it provides users with governed, explainable responses to their queries, according to Menninger.</p> <p>Regarding Qlik's competitive standing now that its agentic experience is generally available, he added that many analytics vendors now <a href="https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents">provide similar capabilities</a> with subtle differences that give different vendors advantages in specific niches of AI-driven analysis.</p> <p>"At this point, there is very little new under the 'AI sun,'" Menninger said. "All the software providers are chasing the same goals of making their products easier to use and helping to automate more business processes through agentic AI. Each will have its own advantages, depending on its existing strengths. For instance, Qlik has the advantage over some of its competitors with its data capabilities."</p> <p>Leone likewise noted that competitive advantages are subtle, with Qlik's main differentiator its integration of AI-powered analysis with <a href="https://www.techtarget.com/searchbusinessanalytics/news/366587232/Trusted-data-key-for-Qlik-as-it-develops-foundation-for-AI">a strong data foundation</a>.</p> <p>"The differentiator isn't the agentic capability itself since the entire market is heading in that direction," he said. "The real value is likely how they are layering this on top of the foundation they built with Qlik Answers. By ensuring the data feeding those agents is grounded in that same governance and lineage, they are tackling the trust gap that is currently stalling a lot of real-world deployments."</p> <p>Highlight capabilities, meanwhile, include the MCP server and Data Products for Analytics, Leone continued.</p> <p>"You need those curated data products to ensure the AI isn't just guessing, and the MCP server is what finally lets that trusted intelligence travel into the apps people actually use," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the agentic experience now available, once of Qlik's product development priorities is advancing the feature set by adding more specialized agents and broadening its MCP server's capabilities to connect to more data sources, according to Clarke.</p> <p>Other focal points include improving its Open Lakehouse to better support high data volumes and <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">streaming data</a>, and further connecting its data integration and analytics capabilities to provide trusted foundation for AI.</p> <p>While Qlik's agentic experience includes an MCP server to connect agents with data sources, it does not feature a framework such as <a target="_blank" href="https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/" rel="noopener">Agent2Agent Protocol</a> that helps govern how agents interact with one another. &nbsp;Menninger noted that it is important for Qlik and its peers to add such capabilities.</p> <p>"Most vendors lack strong multi-agent orchestration and coordination capabilities," he said. "MCP is a starting point, but agent-to-agent protocols are also needed as well as the ability to orchestrate the various agents and their activities."</p> <p>Leone, meanwhile, suggested that Qlik demonstrate that its governance capabilities not only enable agents to respond to user prompts, but also safely <a href="https://www.techtarget.com/searchenterpriseai/news/366617713/Autonomous-AI-agents-on-the-rise">act on their own</a> so that customers can improve their efficiency by turning over time-consuming tasks to trusted agents.</p> <p>"The next frontier for them is proving that these agents can safely take autonomous action," he said. "We're seeing agents that can answer questions, but the real value unlocks when those agents can confidently fix a problem without a human double-checking every step. If Qlik can prove their governance makes that level of automation safe, they solve a massive operational bottleneck."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The vendor's latest capabilities, including an insight-generating agent and an MCP server, show that it is evolving to keep pace with current trends in data and analytics. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a373894778.jpg https://www.techtarget.com/searchbusinessanalytics/news/366638938/Qlik-launches-agentic-experience-to-fuel-AI-powered-analysis Tue, 10 Feb 2026 08:30:00 GMT <p>Aerospike on Monday launched native Dynamic Data Masking, a new feature that customers can easily apply to protect personally identifiable information.</p> <p>Personally identifiable information (PII) is information about an individual that could reveal their identity which, if exposed, could violate regulations such as the <a href="https://www.techtarget.com/searchsecurity/tip/Use-this-CCPA-compliance-checklist-to-get-up-to-speed">California Consumer Privacy Act</a> and the European Union's <a href="https://www.techtarget.com/searchdatabackup/feature/Principles-of-the-GDPR-explained">General Data Protection Regulation</a>.</p> <p>While it's imperative that enterprises protect PII when running data workloads, many databases don't have native features that protect PII. Instead, they require system administrators to create masked views, aggregation pipelines and other configurations that require ongoing, complex oversight to hide individual indicators.</p> <p>Popular databases such as Microsoft SQL Server and Oracle Database do come with native dynamic data masking. However, the feature is less common with <a href="https://www.techtarget.com/searchdatamanagement/definition/NoSQL-Not-Only-SQL">NoSQL</a> databases such as Aerospike, Amazon Dynamo DB, Couchbase, MongoDB and Redis.</p> <p>Now, Aerospike, a NoSQL database vendor based in Mountain View, Calif., is aiming to stand apart from <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">its database competitors</a> by simplifying the protection of PII in a move Holger Mueller, an analyst at Constellation Research, called significant for the vendor's customers.</p> <p>"It makes things easier -- in case you need to mask -- which is a clumsy and sensitive process you have to do manually otherwise," he said.</p> <p>Aerospike's native Dynamic Data Masking is available as part of the vendor's latest Aerospike Database 8 update, which also includes support for <a href="https://www.techtarget.com/searchitoperations/definition/YAML-YAML-Aint-Markup-Language">YAML</a>-based server configuration and improved data recovery capabilities, among other new features. System administrators can deploy native Dynamic Data Masking by applying a rule to mask data for all users and machines other than those granted specific privileges to view and work with PII.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It makes things easier -- in case you need to mask -- which is a clumsy and sensitive process you have to do manually otherwise. </figure> <figcaption> <strong>Holger Mueller</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Once the rule is applied, PII protection is automatically enforced at the database layer so that developers and engineers can build analytics and <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">AI tools</a> without having to configure PII protection at the application level.</p> <p>Meanwhile, the impetus for developing native Dynamic Data Masking came from customer feedback, according to Srini Srinivasan, Aerospike's co-founder and chief technology officer.</p> <p>"Aerospike has many fintech and banking customers who have always cared deeply about protecting PII, and for those customers, it's now even easier for them with native support," he said. "But now, almost all applications have a PII or payment component. Everything is digital. Dynamic data masking applies in far more places, and it needs to be easier to deploy and manage centrally."</p> <p>Beyond its value for users, native Dynamic Data Masking could help Aerospike stand apart from other NoSQL database providers, according to Mueller.</p> <p>Among NoSQL databases, Mueller noted that only Microsoft's <a href="https://www.techtarget.com/searchbusinessanalytics/news/366624233/New-Microsoft-database-analytics-tools-target-agentic-AI">Azure Cosmos DB</a> offers native dynamic data masking capabilities. Those capabilities, introduced in November 2025, are in preview rather than generally available.</p> <p>"It is a new feature [among NoSQL databases]," he said, noting that otherwise "it is a manual coding effort, so it's much better to have this as a product feature."</p> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the latest Aerospike Database 8 update now available, <a href="https://www.techtarget.com/searchdatamanagement/news/366578850/Aerospike-raises-100M-to-fuel-database-innovation-for-GenAI">the vendor's product development plans</a> are focused on adding capabilities that enable customers to develop AI applications, according to Srinivasan.</p> <p>"There's a lot of work and features coming in Aerospike in this and other areas to make it even faster to create a proof of concept, see the value, and go from start to scale on Aerospike," he said, noting that more than half of Aerospike workloads are now related to AI and machine learning.</p> <p>In addition, <a href="https://www.techtarget.com/searchdatamanagement/news/366629235/Aerospike-update-aims-to-improve-database-performance">database performance</a>, scale, stability and cost optimization are also points of emphasis.</p> <p>Aerospike's focus on AI is appropriate, according to Mueller.</p> <p>While Aerospike does provide vector search and storage capabilities, with enterprises continuing to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">invest heavily in developing AI tools</a>, he suggested that the vendor add more capabilities to its database that support AI development. In addition, he noted that Aerospike could provide AI capabilities of its own to make its database easier and faster to use.</p> <p>"It is AI time, so [Aerospike could add] out of the box guardrails, [improved] vector support and use AI to run the database administration tasks," Mueller said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> By adding native protection of personally identifiable information, the vendor is simplifying system administration while simultaneously pushing the NoSQL market. https://cdn.ttgtmedia.com/rms/onlineimages/security_g1192070289.jpg https://www.techtarget.com/searchdatamanagement/news/366638953/PII-masking-a-differentiator-for-Aerospikes-NoSQL-database Mon, 09 Feb 2026 09:22:00 GMT <p>Contact center software has existed since the dawn of digital contact centers decades ago. But, in recent years, the contact center software industry has changed significantly.</p> <p>New technologies, such as generative AI, have spawned powerful and innovative contact center features. Hyperscalers, too, like Microsoft and Amazon, have entered the space, hoping to use their command of adjacent markets to claim a slice of the contact center software ecosystem.</p> <p>All these developments prompt a re-evaluation of <a href="https://www.techtarget.com/searchcustomerexperience/feature/The-ultimate-guide-to-contact-center-modernization">modern contact center platform options</a>. Below, we identify the leading contact center platforms and summarize their key features and drawbacks so businesses can make informed decisions when evaluating these products.</p> <p>In developing this list, we examined research and independent user reviews from leading analyst firms and buyer intelligence platforms. Based on this analysis, we created an unranked list of the top 19 contact center platforms. The list is in alphabetical order.</p> <p>The software providers range from new players to more established vendors. While they all deliver <a href="https://www.techtarget.com/searchcustomerexperience/How-to-choose-a-contact-center-software-system">core contact center software capabilities</a>, they vary in areas like major features, pricing, AI capabilities, scalability and integrations.</p> <section class="section main-article-chapter" data-menu-title="1. 8x8"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. 8x8</h2> <p>Founded in 1987, 8x8 has built up its contact center platform over many years, largely through acquisitions. What began as a basic voice calling tool has evolved into a full-fledged platform for multi-channel customer interaction.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Analytics.</b> Detailed analytics and reporting provide real-time feedback on customer interactions.</li> <li><b>Intelligent call routing.</b> Interactive voice response and customized call routing help to personalize the <a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-experience-CX">customer experience</a> (CX).</li> <li><b>Extensive CRM integration.</b> Integrations with popular CRM platforms make it easy to use CRM data during customer interactions.</li> </ul> <h3>Scalability</h3> <p>8x8's cloud-based hosting model allows the platform's software to scale easily. Flexible licensing also helps enable scalability from a purchasing standpoint.</p> <h3>Integrations</h3> <p>8x8 integrates by default with major CRM and communications platforms like Salesforce, HubSpot and Microsoft Teams. An API enables custom integrations.</p> <h3>Pricing</h3> <p>Pricing varies widely depending on feature selection, and 8x8 offers custom quotes rather than publishing pricing details publicly. As a baseline, however, pricing generally starts around $20 per user per month, although it can extend above $100 per user per month for feature-rich plans.</p> <p>8x8 is most notable for its affordable pricing for basic plans and easy integration with external platforms.</p> </section> <section class="section main-article-chapter" data-menu-title="2. Amazon Connect"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. Amazon Connect</h2> <p>Introduced in 2017, Amazon Connect offers a centralized hub from which <a href="https://www.techtarget.com/searchcustomerexperience/answer/5-ways-to-improve-call-center-agent-performance?Offer=ab_MeteredFormCopyEoc_var3">contact center agents</a> can engage with customers across multiple channels, including voice, chat and messaging. It also integrates with other Amazon products and services. In 2023, Amazon Connect incorporated several AI-based capabilities, such as support for creating virtual assistants.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Centralized interface.</b> Contact center agents can handle interactions via voice, chat, email and text through a centralized channel.</li> <li><b>No-code flow builder.</b> To configure workflows for different types of interactions or customer needs, businesses can use a visual workflow builder.</li> <li><b>AI-driven automation.</b> Partly via integrations with other Amazon services -- such as Lex, which powers AI chatbots -- Amazon Connect enables the automation of some interactions using AI. For example, users can use Amazon Q in Connect to deploy GenAI chatbots. AI features can also automatically route requests to agents.</li> </ul> <h3>Scalability</h3> <p>As a platform hosted across multiple regions in the AWS cloud, Connect is a highly scalable and <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-high-availability-vs-fault-tolerance-in-AWS?Offer=ab_MeteredFormCopyEoc_var3">fault-tolerant service</a>. It can support a virtually unlimited volume of agents or interactions.</p> <h3>Integrations</h3> <p>Connect integrates most closely with other services within the Amazon cloud. However, it supports limited integrations with external platforms, such as Salesforce and Zendesk, which businesses can use to look up or import data during customer interactions.</p> <h3>Pricing</h3> <p>Connect pricing is based mostly on volume usage. It starts at around $0.018 per minute for voice calls and $0.004 per chat message. Additional fees apply for using optional features, like Amazon Q.</p> <p>Amazon Connect is most notable for hyperscale-level scalability and availability, as well as tight integration with other Amazon services.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f.png 1280w" alt="Integrating AI in contact center software" height="355" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>AI and generative AI integration is remaking contact center software. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="3. Avaya"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. Avaya</h2> <p>Traditionally, Avaya focused its contact center software on <a href="https://www.techtarget.com/searchcustomerexperience/tip/On-premises-vs-cloud-contact-center-Whats-the-difference">on-premises hosting models</a>. However, it has expanded into cloud-based options that support public and private cloud deployments. Avaya provides all the core capabilities that businesses expect from a modern contact center platform as well as certain innovative features like AI-based virtual assistants.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Flexible deployment options.</b> Avaya offers on-premises and cloud-based contact center products. The on-prem offering may be an advantage for organizations that, due to <a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-compliance-checklist-for-hybrid-workforces">compliance or privacy concerns</a>, can't or don't want to store contact center data on third-party infrastructure.</li> <li><b>Process optimization.</b> Native features assist with the optimization of tasks such as scheduling and agent training.</li> <li><b>Real-time reporting.</b> Continuous analytics further assist with the identification of opportunities to optimize.</li> </ul> <h3>Scalability</h3> <p>While the scalability of Avaya's on-premises offering is limited by the scope of the host infrastructure, its cloud-based platform can scale virtually without limit.</p> <h3>Integrations</h3> <p>Avaya integrates with popular CRM platforms like Salesforce, ServiceNow and Microsoft Dynamics 365. Custom integrations are available through an API.</p> <h3>Pricing</h3> <p>The cost of Avaya starts at $20 per user per month for the Core plan. The highest-cost plan is priced at $35 per user per month. These prices reflect a 20% discount for a yearly contractual commitment.</p> <p>Avaya is most notable for its on-premises deployment option and competitive pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="4. Cisco Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Cisco Contact Center</h2> <p>Although Cisco is best known for its networking and communications tools, it has also invested significantly in the contact center space. Its Contact Center product employs Webex, a meeting and collaboration application, as the foundation for omnichannel customer interactions.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Security.</b> Cisco Contact Center goes <a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-security-best-practices-to-protect-customer-data">above and beyond in the security realm</a>, offering advanced capabilities like endpoint hardening and data masking.</li> <li><b>Enterprise scalability.</b> While the product can work for small businesses, it's designed especially for large-scale, enterprise-grade communications.</li> <li><b>Customer sentiment analysis.</b> The platform uses AI to assess customer reactions to interactions.</li> </ul> <h3>Scalability</h3> <p>Cisco Contact Center scales especially well for large enterprises.</p> <h3>Integrations</h3> <p>Cisco Contact Center integrates tightly with other Cisco tools, particularly the Webex and Jabber communication apps. In fact, to some extent, the contact center service depends on these integrations with other Cisco tools. Integrations are also available for major CRM and IT ticketing platforms.</p> <h3>Pricing</h3> <p>Cisco doesn't publish pricing details for its contact center service, and costs vary depending on features and usage. As a rough baseline, expect to pay anywhere in the range of $30 to $200 per user per month.</p> <p>Cisco Contact Center is most notable for its security features and enterprise-grade scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="5. CloudTalk"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. CloudTalk</h2> <p>CloudTalk is most notable for its heavy focus on <a target="_blank" href="https://www.cloudtalk.io/blog/call-center-analytics-guide/" rel="noopener">automation and analytics features</a> designed to streamline contact center performance and increase operations efficiency. It also offers innovative AI-powered features, such as topic extraction, which automatically monitors conversational topics.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Agent collaboration.</b> In addition to supporting multi-channel customer engagement, CloudTalk offers native features for agent collaboration, like internal call conferencing and shared workspaces.</li> <li><b>Advanced analytics.</b> CloudTalk offers particularly detailed reporting on engagement metrics and agent performance.</li> <li><b>Extensive integrations.</b> The platform provides a broad range of integrations that include major CRM platforms and communication and automation tools like Slack and Zapier.</li> </ul> <h3>Scalability</h3> <p>As a cloud-based offering, CloudTalk works well at virtually any scale. Flexible pricing terms also enable easy scalability.</p> <h3>Integrations</h3> <p>As noted above, CloudTalk integrates out-of-the-box with a particularly wide range of external platforms. It also provides an API for custom integrations.</p> <h3>Pricing</h3> <p>CloudTalk pricing starts around $25 per user per month. The most feature-rich plan costs about $50 per user per month.</p> <p>CloudTalk is notable for its advanced analytics and broad integrations.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Content Guru"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Content Guru</h2> <p>Launched in 2005, Content Guru offers a contact center and <a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-engagement">customer engagement</a> service tailored for verticals that require high availability and security, like government and finance. Although the service can be and is used by all types of businesses.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-powered automation.</b> Content Guru makes extensive use of AI to automate tasks like call routing. It also supports AI-powered virtual agents.</li> <li><b>Workforce management.</b> Native capabilities assist with scheduling contact center agents and managing workflows.</li> <li><b>Video call support.</b> Supports customer engagement via video as well as more conventional channels, such as voice and text.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment enables easy scalability up and down.</p> <h3>Integrations</h3> <p>Content Guru integrates with major CRM platforms out-of-the-box, and an API is available for developing custom integrations.</p> <h3>Pricing</h3> <p>Content Guru pricing varies based on total agent count, type and feature availability. It starts at $22 per digital-only agent per month. Voice agents cost at least $70 per month.</p> <p>Content Guru is most notable for AI-powered automation and workflow optimization capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="7. Dialpad"> <h2 class="section-title"><i class="icon" data-icon="1"></i>7. Dialpad</h2> <p>Dialpad initially focused on providing internal communications software for businesses and added contact center software capabilities in 2018. Dialpad is most notable for its extensive investment in AI-based capabilities, such as AI-driven voice analysis and call summaries, as well as AI-powered <a href="https://www.techtarget.com/searchcustomerexperience/definition/virtual-agent">virtual agents</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI capabilities.</b> Dialpad makes especially extensive use of AI to provide capabilities like real-time transcription and sentiment analysis.</li> <li><b>Collaboration.</b> Built-in chat, file sharing and other collaboration tools help agents communicate.</li> <li><b>Broad integrations.</b> Dialpad integrates with external productivity and collaboration platforms like Google Workspace and Microsoft Teams in addition to CRM tools.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment and multiple pricing plans make Dialpad easy to scale for businesses of virtually all sizes.</p> <h3>Integrations</h3> <p>As mentioned, Dialpad is notable for integrating with popular CRM platforms, like Salesforce and Zendesk, and productivity and collaboration suites, like Google Workspace and Microsoft Teams. Customers can also build custom workflows.</p> <h3>Pricing</h3> <p>Dialpad pricing starts at $15 per user per month for the Standard plan. The Pro plan is $25 per user per month. An Enterprise plan is also available.</p> <p>Dialpad is most notable for advanced AI features, extensive integrations and competitive entry-level pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="8. Five9"> <h2 class="section-title"><i class="icon" data-icon="1"></i>8. Five9</h2> <p>Five9 provides a fully cloud-based call and contact center platform. It also places special emphasis on transparency and security for businesses concerned with <a href="https://www.techtarget.com/searchcustomerexperience/answer/How-do-companies-protect-customer-data">protecting sensitive customer data</a> or meeting strict compliance mandates related to customer calls.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Dynamic routing.</b> Five9 offers a particularly powerful routing tool that can route calls based on a variety of factors, such as priority level, agent expertise and geographical location.</li> <li><b>Workforce management.</b> Built-in capabilities, including forecasting and automated scheduling, assist with agent workforce management.</li> <li><b>AI capabilities.</b> Five9 includes advanced AI features such as speech recognition and predictive dialing.</li> </ul> <h3>Scalability</h3> <p>Five9 is highly scalable because of its cloud-based deployment model and its flexible pricing terms and plans, which cater to a wide range of business sizes.</p> <h3>Integrations</h3> <p>Five9 integrates with CRM platforms as well as popular IT management suites, like ServiceNow.</p> <h3>Pricing</h3> <p>Five9 doesn't publish full pricing details of all its plans, but its most basic plan starts at $119 per user per month. Its Core plan, which has more features, is $159 per user per month.</p> <p>Five9 is most notable for especially efficient and flexible call routing capabilities and advanced AI features.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f.png 1280w" alt="Business goals for contact center software" height="260" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Today's contact center software must satisfy several business goals. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="9. Genesys"> <h2 class="section-title"><i class="icon" data-icon="1"></i>9. Genesys</h2> <p>Founded in 1990, Genesys has spent decades building a feature-rich contact center and customer engagement platform. The company caters especially to medium-size and large businesses.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>On-premises option.</b> An on-premises deployment option is available, as well as a <a href="https://www.techtarget.com/searchcloudcomputing/tip/Evaluate-on-premises-vs-cloud-computing-pros-and-cons">cloud-based offering</a>.</li> <li><b>Virtual agents.</b> AI capabilities include virtual agents.</li> <li><b>Collaboration.</b> Internal screen sharing and conferencing capabilities help agents collaborate.</li> </ul> <h3>Scalability</h3> <p>Genesys can operate on any scale, but it focuses especially on deployments for midsize and enterprise customers.</p> <h3>Integrations</h3> <p>Out-of-the-box integrations focus mostly on CRM platforms. An API is available for custom integrations.</p> <h3>Pricing</h3> <p>Costs start at $75 per user per month and range up to $240 per user per month.</p> <p>Genesys is most notable for its on-premises deployment option and extensive collaboration capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="10. Google Cloud Contact Center as a Service"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10. Google Cloud Contact Center as a Service</h2> <p>Google Cloud Contact Center as a Service (CCaaS) -- also referred to as Google's Contact Center AI Platform (CCAI Platform) -- is among the newer cloud-based contact center products and is focused on AI capabilities such as virtual agents. Behind the scenes, however, Google's contact center offering is powered largely by UJET, an independent contact center platform known for its analytics features and <a href="https://www.techtarget.com/searchcustomerexperience/tip/Contact-center-back-end-integrations-drive-revenue-growth">integration with CRM systems</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI capabilities.</b> Advanced AI capabilities include chatbots and virtual agents.</li> <li><b>Speech recognition.</b> AI also enables real-time speech transcription and sentiment analysis.</li> <li><b>Google Cloud integrations.</b> Google's contact center integrates tightly with other Google Cloud services.</li> </ul> <h3>Scalability</h3> <p>Although designed especially for large enterprise customers, Google Cloud's CCaaS can also support smaller teams.</p> <h3>Integrations</h3> <p>The contact center integrates most seamlessly with other Google Cloud products and services, as well as popular CRMs like Salesforce. An API is available for developing custom integrations.</p> <h3>Pricing</h3> <p>Pricing is mostly a pay-as-you-go model and starts at around $0.06 per chat session and $0.05 per voice minute. Some capabilities cost extra, like Conversational Insights, which provides engagement analytics.</p> <p>The CCAI Platform is most notable for its close integration with Google Cloud services and enterprise-grade scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="11. Microsoft Dynamics 365 Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>11. Microsoft Dynamics 365 Contact Center</h2> <p>Microsoft developed the Microsoft Dynamics contact center platform in-house and released it in July 2024. Microsoft emphasizes self-service on a customer-preferred channel as well as <a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-monitoring">monitoring and reporting features to improve operational efficiency</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Workforce management.</b> Built-in tools assist with agent scheduling and performance assessment.</li> <li><b>Microsoft integrations.</b> Dynamics 365 Contact Center connects to other Microsoft tools and platforms, like Teams, Outlook and Power BI.</li> <li><b>AI features.</b> Dynamics 365 Contact Center uses GenAI services hosted on the Microsoft Azure cloud to enable virtual agents and chatbots.</li> </ul> <h3>Scalability</h3> <p>As a hyperscale-based service, Dynamics 365 offers immense scalability from an infrastructure perspective. That said, its pricing models are flexible enough to accommodate the needs of smaller teams as well.</p> <h3>Integrations</h3> <p>The contact center service integrates most tightly with other Microsoft products, as well as popular CRM platforms. Custom integrations are possible through an API.</p> <h3>Pricing</h3> <p>Costs begin at $95 per user per month. A free trial is also available.</p> <p>Dynamics 365 Contact Center is most notable for integration with other Microsoft products, which facilitates integrating contact center capabilities into broader Microsoft software suites.</p> </section> <section class="section main-article-chapter" data-menu-title="12. Nextiva"> <h2 class="section-title"><i class="icon" data-icon="1"></i>12. Nextiva</h2> <p>Nextiva offers all the key features that businesses need to operate an effective contact center, such as<a href="https://www.techtarget.com/whatis/definition/skill-based-routing-SBR"> skills-based call routing</a> and advanced call management. Nextiva has invested in AI-based capabilities and places special emphasis on platform reliability and a fast response to service requests from its customers.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Intelligent call routing.</b> Nextiva provides highly flexible and efficient call routing capabilities based on criteria defined by users.</li> <li><b>AI capabilities.</b> The platform uses AI to generate call summaries. An AI answering feature is also available.</li> <li><b>High availability.</b> Nextiva's platform is cloud-based, and the company focuses on achieving particularly high availability through a multi-site hosting model.</li> </ul> <h3>Scalability</h3> <p>Multi-site hosting and flexible pricing plans enable a high degree of scalability.</p> <h3>Integrations</h3> <p>Nextiva connects to major CRM platforms. An API supports custom integrations.</p> <h3>Pricing</h3> <p>Costs start at $15 per user per month, and increase to $75 per user per month for more features geared toward small businesses. Larger enterprise plans are also available.</p> <p>Nextiva is most notable for reliability and affordable entry-level pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="13. NiCE CXone Mpower"> <h2 class="section-title"><i class="icon" data-icon="1"></i>13. NiCE CXone Mpower</h2> <p>Launched in 2024, CXone Mpower from NiCE is one of the newest contact center platforms on our list. The company promotes CXone Mpower as a "CX-aware" service because it uses AI to inject <a target="_blank" href="https://www.linkedin.com/pulse/transform-customer-experiences-real-time-using-contextual-goyal-hlw8c/" rel="noopener">context into customer interactions</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI integrations.</b> The platform makes extensive use of AI to help optimize workflows and generate context for customer integrations.</li> <li><b>Chatbots and virtual agents.</b> AI also supports chatbots and virtual agents within CXone Mpower.</li> <li><b>Scalability.</b> The platform is particularly notable for its ability to cater to customers of all types and sizes, from small businesses to large enterprises.</li> </ul> <h3>Scalability</h3> <p>As noted above, CXone Mpower is an especially scalable service due to its cloud-based hosting model and the ease of accommodating increased customers or communication channels.</p> <h3>Integrations</h3> <p>Core integrations support major CRM platforms. Custom integrations are possible through an API.</p> <h3>Pricing</h3> <p>Costs range from $110 to $249 per user per month.</p> <p>NiCE CXone Mpower is most notable for AI-enhanced efficiency capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="14. RingCentral"> <h2 class="section-title"><i class="icon" data-icon="1"></i>14. RingCentral</h2> <p>Founded in 1999, RingCentral originally specialized in on-premises phone connectivity. Since then, it has expanded into a broad set of business communication and collaboration services, including a contact center platform.&nbsp;</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Mobile app for agents.</b> A mobile app allows agents to engage with customers from virtually any location.</li> <li><b>Collaboration.</b> Internal video calling, team messaging and file sharing help agents collaborate.</li> <li><b>Analytics.</b> RingCentral supports both real-time and historical reporting on agent performance and service levels.</li> </ul> <h3>Scalability</h3> <p>A cloud-based deployment model enables a high degree of scalability.</p> <h3>Integrations</h3> <p>RingCentral integrates with major CRM platforms as well as certain business productivity suites, such as Google Workspace.</p> <h3>Pricing</h3> <p>RingCentral's RingCX product features a Standard plan at $65 per user per month. The Professional plan is $95 per user per month, and the Elite plan is $145 per user per month. An enterprise package is also available.</p> <p>RingCentral is most notable for its agent <a href="https://www.techtarget.com/searchcustomerexperience/tip/7-reasons-why-businesses-need-mobile-apps">mobile app option</a>, collaboration features and scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="15. Salesforce Service Cloud Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>15. Salesforce Service Cloud Contact Center</h2> <p>Although Salesforce is best known for CRM, its Service Cloud platform includes a contact center offering to pull customer data into contact center engagements and tightly integrate with the Salesforce product ecosystem.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI features.</b> Using Salesforce's Einstein AI tools, Service Cloud uses AI to automate tasks like routing.</li> <li><b>Custom chatbots.</b> Businesses can also use Einstein AI to configure custom AI chatbots to serve as virtual agents.</li> <li><b>Knowledge management.</b> Built-in knowledge management capabilities aim to accelerate the rate at which agents can solve customer requests.</li> </ul> <h3>Scalability</h3> <p>Service Cloud can support businesses of all sizes, but it's geared especially toward large, enterprise-scale customers.</p> <h3>Integrations</h3> <p>Salesforce contact center integrates most tightly with other Salesforce products but also provides core integration with certain third-party platforms, such as Zendesk and HubSpot.</p> <h3>Pricing</h3> <p>Salesforce offers one pricing plan, at $150 per user per month, for its contact center software.</p> <p>The Salesforce contact center is most notable for enterprise-grade scalability and extensive Salesforce integrations.</p> </section> <section class="section main-article-chapter" data-menu-title="16. Talkdesk"> <h2 class="section-title"><i class="icon" data-icon="1"></i>16. Talkdesk</h2> <p>Talkdesk promotes its CX automation via its AI multi-agent workflows and AI-first <a href="https://www.techtarget.com/searchcustomerexperience/tip/5-customer-journey-phases-for-businesses-to-understand">customer journey</a>. Talkdesk also emphasizes its capabilities across several vertical industries. The product -- dubbed Customer Experience Automation, or CXA -- is known for its ease of use, intuitive interface and call routing capabilities.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Virtual agents.</b> Talkdesk offers GenAI-powered virtual agents to automate customer interactions.</li> <li><b>No-code workflow management.</b> A visual interface enables workflow configuration and modifications.</li> <li><b>Hybrid cloud deployment option.</b> While Talkdesk can't run fully on-premises, a hybrid deployment model is available that allows businesses to route communications through on-prem telephony infrastructure, which can be advantageous from a privacy and compliance standpoint.</li> </ul> <h3>Scalability</h3> <p>A flexible deployment architecture enables a high degree of scalability, making Talkdesk appropriate for small businesses and large enterprises.</p> <h3>Integrations</h3> <p>Integrations focus mostly on CRM platforms, but Google Workspace is also supported, and a custom integration API is available.</p> <h3>Pricing</h3> <p>Costs range from $85 to $225 per user per month.</p> <p>Talkdesk is most notable for its feature-rich virtual agents and hybrid deployment option.</p> </section> <section class="section main-article-chapter" data-menu-title="17. Vonage Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>17. Vonage Contact Center</h2> <p>Vonage Contact Center's natively built features, including AI-powered virtual assistants, rely on integrations with external platforms, particularly Salesforce, to power some of its capabilities and access customer data. Vonage also emphasizes <a target="_blank" href="https://www.vonage.com/resources/articles/video-contact-center/" rel="noopener">video-based customer engagement</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-based sentiment analysis.</b> Vonage uses AI to evaluate customer interactions across multiple channels, including voice, text and social media.</li> <li><b>Virtual agents.</b> AI also powers virtual agents, which businesses can configure to perform a range of custom tasks.</li> <li><b>Business continuity.</b> Vonage offers business continuity and disaster recovery features, such as emergency call routing options.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment provides a high degree of scalability.</p> <h3>Integrations</h3> <p>Integrations focus mostly on CRM platforms, with an API available for custom integrations.</p> <h3>Pricing</h3> <p>Vonage does not list pricing information on its website specifically for its contact center plans, which include a Priority plan, Premium plan and add-on options. It offers volume-based API pricing with rates at $0.00809 per SMS and $0.01446 per minute for voice calls. Additional capabilities, like anti-fraud features and customer identification, cost extra.</p> <p>Vonage is most notable for omnichannel sentiment analysis, affordable volume-based pricing and business continuity features.</p> </section> <section class="section main-article-chapter" data-menu-title="18. Zendesk Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>18. Zendesk Contact Center</h2> <p>Although primarily a CRM platform, Zendesk also provides a dedicated contact center offering. The company first entered the call center space in 2011, but it completed a major overhaul of its customer communications and engagement platform in 2025, which now features cutting-edge AI capabilities.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-powered automation.</b> Zendesk contact center makes extensive use of AI to automate virtually all core tasks, from routing to agent response.</li> <li><b>Chatbots.</b> AI-powered chatbots can perform custom tasks.</li> <li><b>Knowledge management.</b> Native knowledge management tools assist agents in finding the information they need to address customer requests.</li> </ul> <h3>Scalability</h3> <p>Zendesk contact center can support businesses of all sizes, but it caters especially to midsize and enterprise organizations.</p> <h3>Integrations</h3> <p>Core integrations support other Zendesk products and other popular CRMs, including Salesforce and HubSpot, as well as communications platforms like Slack.</p> <h3>Pricing</h3> <p>Price plans start at $19 per user per month. The Suite Enterprise plan is $169 per user per month for enterprise-grade capabilities. Other plans are priced at $55 and $115 per user per month.</p> <p>Zendesk is most notable for its AI capabilities and a broad set of pricing options.</p> </section> <section class="section main-article-chapter" data-menu-title="19. Zoom Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>19. Zoom Contact Center</h2> <p>Best known for its teleconferencing software, Zoom launched a contact center platform originally called Video Engagement Center and rebranded as Zoom Contact Center. The platform offers all core contact center software features with a focus on video-based customer meetings, while supporting other communications media over multiple channels.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI agent assist.</b> AI capabilities help guide human agents by suggesting actions and providing information during customer interactions.</li> <li><b>Virtual agents.</b> Fully independent, AI-powered agents are also available for engaging customers.</li> <li><b>Video support.</b> Zoom Contact Center supports customer engagement via video as well as more traditional channels.</li> </ul> <h3>Scalability</h3> <p>Zoom Contact Center provides a high degree of scalability due to its cloud-based deployment model, although its pricing plans are geared mainly toward midsize and larger organizations.</p> <h3>Integrations</h3> <p>Zoom Contact Center integrates with popular CRM platforms as well as other Zoom software.</p> <h3>Pricing</h3> <p>Pricing ranges from $69 to $149 per user per month.</p> <p>Zoom is most notable for its video calling support and AI capabilities that can assist human agents as well as power autonomous virtual agents.</p> <p>Clearly, the contact center market is crowded with many options for contact center buyers and C-suite decision-makers. Many of the platforms have similar and overlapping features, especially around AI capabilities, integrations with adjacent products and scalability performance. Contact center buyers need to evaluate these platforms carefully to find the right one for their organization.</p> <p><b>Editor's note:</b>&nbsp;<i>This article was updated to reflect recent developments in contact center platforms and the market in general.</i></p> <p><i>Chris Tozzi is an adjunct research adviser at IDC as well as an adviser for Fixate IO and a professor of IT and society at a polytechnic university in upstate New York.</i></p> </section> By now, many contact center software providers offer similar features. But large and small enterprises should consider some key differences among vendors. https://cdn.ttgtmedia.com/rms/onlineimages/chatbot_g1206801125.jpg https://www.techtarget.com/searchcustomerexperience/tip/Top-10-contact-center-platforms Thu, 05 Feb 2026 12:00:00 GMT <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{178}" paraid="485118795"><span xml:lang="EN-US" data-contrast="auto">Databricks Lakebase is now generally available, eight months after the PostgreSQL database purposed for AI development was first unveiled in public preview.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{184}" paraid="1686772772"><span xml:lang="EN-US" data-contrast="auto">Lakebase, which was launched on AWS on Feb. 3, is the result of Databricks' $1 billion </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition"><span xml:lang="EN-US" data-contrast="none">acquisition of Neon</span></a><span xml:lang="EN-US" data-contrast="auto">, a cloud-based database vendor providing a platform built on the open-source PostgreSQL format, in May 2025. Databricks has since rebranded Neon's capabilities, and now Databricks has integrated them with </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366560094/Databricks-puts-AI-at-core-of-new-Data-Intelligence-Platform"><span xml:lang="EN-US" data-contrast="none">its Data Intelligence Platform</span></a><span xml:lang="EN-US" data-contrast="auto"> to provide customers with an operational database in conjunction with its data lakehouse.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{200}" paraid="560365120"><span xml:lang="EN-US" data-contrast="auto">Beyond integration with Databricks' broader platform, Lakebase fosters AI development by separating compute from storage, unlike many PostgreSQL databases that couple them together. By separating the processing power for queries from the power needed for storage, Lakebase eliminates competition between the two for memory resources and the resulting resource management tasks that can slow development initiatives.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{206}" paraid="1946560941"><span xml:lang="EN-US" data-contrast="auto">In addition, Lakebase features autoscaling to help users </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/tip/Implement-AI-driven-cloud-cost-optimization-to-reduce-waste"><span xml:lang="EN-US" data-contrast="none">control the cost</span></a><span xml:lang="EN-US" data-contrast="auto"> of building agents and other AI applications, and unified governance through Databricks' Unity Catalog, among other capabilities.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{225}" paraid="729396782"><span xml:lang="EN-US" data-contrast="auto">Given that Lakebase better integrates PostgreSQL workloads with the broader Databricks platform, it is a significant addition for the vendor's customers, according to Devin Pratt, an analyst at IDC.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{231}" paraid="2001649299"><span xml:lang="EN-US" data-contrast="auto">"The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{237}" paraid="586289825"><span xml:lang="EN-US" data-contrast="auto">William McKnight, president of McKnight Consulting, similarly noted that Lakebase's value lies in its integration with other Databricks capabilities, reducing the need for </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/definition/data-egress"><span xml:lang="EN-US" data-contrast="none">data egress</span></a><span xml:lang="EN-US" data-contrast="auto"> pipelines between the database and other tools.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{1}" paraid="1002527863"><span xml:lang="EN-US" data-contrast="auto">"This architectural shift minimizes fragile pipelines by co-locating transactional workloads with heavy analytics under a single governance model," he said. "It effectively removes the 'architectural tax' that has historically separated live apps from data lakes."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Prowess of PostgreSQL</h2> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{13}" paraid="1406101758"><span xml:lang="EN-US" data-contrast="auto">Based in San Francisco and one of the pioneers of the data lakehouse architecture for storing data, Databricks, like many data management vendors, has added AI development capabilities over the past few years in response to </span><a rel="noreferrer noopener" target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai"><span xml:lang="EN-US" data-contrast="none">rising customer interest</span></a><span xml:lang="EN-US" data-contrast="auto"> in building AI tools that call on an enterprise's proprietary data to understand its unique operations.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication. </figure> <figcaption> <strong>Devin Pratt </strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{24}" paraid="1459340638"><span xml:lang="EN-US" data-contrast="auto">Because PostgreSQL databases are more flexible than many other databases, PostgreSQL is now the most popular database format, according to the </span><a rel="noreferrer noopener" target="_blank" href="https://survey.stackoverflow.co/2024/technology"><span xml:lang="EN-US" data-contrast="none">2024 Developer Survey</span></a><span xml:lang="EN-US" data-contrast="auto"> by Stack Overflow.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{35}" paraid="332370681"><span xml:lang="EN-US" data-contrast="auto">Versatility -- handling geospatial, time series, JSON and vector database workloads -- and flexibility are two of the main reasons PostgreSQL databases are now more popular than fellow open-source MySQL databases and databases provided by vendors such as Microsoft, MongoDB and Redis.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{41}" paraid="491134092"><span xml:lang="EN-US" data-contrast="auto">With PostgreSQL so popular, and its adaptability enabling users to run workloads that aid AI development, hyperscale cloud vendors AWS, Google, IBM, Microsoft and Oracle all offer PostgreSQL databases that can be used with their AI development tools. Now, more specialized data management vendors are doing the same.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{49}" paraid="633463368"><span xml:lang="EN-US" data-contrast="auto">Three weeks after Databricks acquired Neon, rival </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database"><span xml:lang="EN-US" data-contrast="none">Snowflake purchased Crunchy Data</span></a><span xml:lang="EN-US" data-contrast="auto"> to add a PostgreSQL database. Then in October 2025, </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite"><span xml:lang="EN-US" data-contrast="none">Redpanda acquired Oxla</span></a><span xml:lang="EN-US" data-contrast="auto"> to likewise add a PostgreSQL database.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{65}" paraid="437426485"><span xml:lang="EN-US" data-contrast="auto">"PostgreSQL has evolved into the great consolidator of the modern data stack by transforming from a traditional relational database into a unified, multi-model engine capable of powering the agentic AI era," McKnight said. "By natively integrating vector search with structured business data, it eliminates the need for fragmented point solutions, reducing development complexity."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{71}" paraid="1452036302"><span xml:lang="EN-US" data-contrast="auto">In addition, pricing is a factor in PostgreSQL's growing popularity, McKnight continued, noting that PostgreSQL databases often cost less than databases from </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/definition/hyperscale-cloud"><span xml:lang="EN-US" data-contrast="none">hyperscale cloud</span></a><span xml:lang="EN-US" data-contrast="auto"> vendors.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{82}" paraid="1610933445"><span xml:lang="EN-US" data-contrast="auto">"As enterprises pivot toward Sovereign AI to maintain data gravity and avoid public cloud lock-in, PostgreSQL has become the strategic foundation for organizations that want a secure, high-performance platform to manage the transactions and vectors required for modern AI at scale," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{88}" paraid="919164422"><span xml:lang="EN-US" data-contrast="auto">Although PostgreSQL databases are gaining popularity as more enterprises invest in AI development, Databricks' Lakebase and </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership"><span xml:lang="EN-US" data-contrast="none">Snowflake Postgres</span></a><span xml:lang="EN-US" data-contrast="auto"> are differentiated from standalone PostgreSQL databases by their integration with broader data management and AI development platforms, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{99}" paraid="1693840052"><span xml:lang="EN-US" data-contrast="auto">Both reduce the need to move data between systems, which can increase development costs and potentially expose data to breaches, and both enable hybrid transactional and analytical workflows that are relevant for AI and real-time analytics workloads.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{109}" paraid="1248643844"><span xml:lang="EN-US" data-contrast="auto">But whether one proves more effective than the other remains to be seen.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{115}" paraid="1253390927"><span xml:lang="EN-US" data-contrast="auto">"Both are pushing PostgreSQL closer to analytics and AI, and the real differences will come down to platform integration and day-to-day operational experience," Pratt said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{121}" paraid="629852949"><span xml:lang="EN-US" data-contrast="auto">In addition to separation of compute and storage, key features of Lakebase include the following:</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{127}" paraid="1486727951"><span xml:lang="EN-US" data-contrast="auto">Serverless autoscaling that automatically adjusts compute resources to match workload demands, including shutting off when no workloads are running to </span><a rel="noreferrer noopener" target="_blank" href="https://www.computerweekly.com/news/366599472/How-to-stop-AI-costs-from-soaring"><span xml:lang="EN-US" data-contrast="none">eliminate wasted spending</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{138}" paraid="867718339"><span xml:lang="EN-US" data-contrast="auto">Unified governance through the Databricks Unity Catalog, enabling users to manage and secure data across their entire data estate.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="3" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{144}" paraid="1100517225"><span xml:lang="EN-US" data-contrast="auto">Instant database branching so users can quickly create isolated clones of production data to conduct risk-free testing and development work.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="4" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{150}" paraid="348295022"><span xml:lang="EN-US" data-contrast="auto">Point-in-time recovery, a feature that protects against accidental deletions or </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchsoftwarequality/definition/bug"><span xml:lang="EN-US" data-contrast="none">bugs</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="5" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{161}" paraid="117781018"><span xml:lang="EN-US" data-contrast="auto">Sync tables to automatically synchronize operational data and historical lakehouse context without having to build and manage complex pipelines.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{167}" paraid="1834989908"><span xml:lang="EN-US" data-contrast="auto">Collectively, the features that comprise Lakebase are designed to let users run governed, secure operational data workloads directly on Databricks without having to configure connections between their PostgreSQL database and AI development pipeline or move data between systems, according to a Databricks spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{179}" paraid="206944759"><span xml:lang="EN-US" data-contrast="auto">Meanwhile, instant database branching stands out as perhaps Lakebase's most significant feature, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{185}" paraid="1338761809"><span xml:lang="EN-US" data-contrast="auto">"Instant branching improves developer productivity by making it easier to test on production-like data without putting production systems at risk," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{191}" paraid="1538658303"><span xml:lang="EN-US" data-contrast="auto">McKnight, however, highlighted </span><a rel="noreferrer noopener" target="_blank" href="https://www.linkedin.com/pulse/decoupled-storage-compute-paradigm-shift-building-modern-kamdar/"><span xml:lang="EN-US" data-contrast="none">decoupled compute and storage</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{202}" paraid="1922075115"><span xml:lang="EN-US" data-contrast="auto">"This fundamental shift directly addresses the long-standing 'architectural bottleneck' by facilitating serverless autoscaling and limiting resource contention between demanding analytical workloads and live operational applications," he said.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Looking ahead</h2> </div> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{214}" paraid="683076"><span xml:lang="EN-US" data-contrast="auto">With Lakebase now generally available, one of Databricks' focal points is to make it easy to operate a large number of databases at scale, according to the spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{220}" paraid="719851116"><span xml:lang="EN-US" data-contrast="auto">Ease-of-use is a wise focus for Databricks, according to McKnight.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{226}" paraid="595465481"><span xml:lang="EN-US" data-contrast="auto">Databricks has historically appealed to technical experts while rival Snowflake has targeted business users. To broaden its appeal, McKnight advised Databricks to improve Databricks Serverless, a fully managed service that removes infrastructure management tasks, and its Databricks One user interface.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{232}" paraid="922340491"><span xml:lang="EN-US" data-contrast="auto">"By evolving its Serverless and Databricks One initiatives into a true zero-administration environment, Databricks can appeal to business analysts who want the architectural efficiency of a lakehouse without the traditional engineering overhead," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{238}" paraid="673756392"><span xml:lang="EN-US" data-contrast="auto">An additional area of focus could be </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage"><span xml:lang="EN-US" data-contrast="none">cost control</span></a><span xml:lang="EN-US" data-contrast="auto">, McKnight continued.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{249}" paraid="1018231783"><span xml:lang="EN-US" data-contrast="auto">"To neutralize Snowflake, Databricks must … prove that it can provide a lower total cost of ownership while bridging the AI return on investment gap with production-ready, operational templates," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{255}" paraid="1824299946"><span xml:lang="EN-US" data-contrast="auto">Pratt, meanwhile, suggested that Databricks expand efforts to converge operational and analytical workloads to fuel AI initiatives, including providing practical guidance and reference architectures that help customers </span><a rel="noreferrer noopener" target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail"><span xml:lang="EN-US" data-contrast="none">move from pilots to enterprise-wide production</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{11}" paraid="447530315"><span xml:lang="EN-US" data-contrast="auto">"The next chapter is adoption, helping customers turn convergence into production applications that deliver real-time decisions," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{17}" paraid="939591930"><span xml:lang="EN-US" data-contrast="auto">Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:210,&quot;335559740&quot;:276}">&nbsp;</span></p> </div> Resulting from the $1B acquisition of Neon, the database built for AI workloads -- including separate compute and storage -- is now integrated with the vendor's broader platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchdatamanagement/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers Thu, 05 Feb 2026 11:35:00 GMT <p>Pentaho on Wednesday launched its latest platform update, featuring a browser-based version of Pipeline Designer to simplify data integration workloads and a new semantic modeling tool to help customers consistently organize data across their organization.</p> <p>In addition, Version 11 of Pentaho Data Integration and Business Analytics includes new project profiling capabilities to simplify deployments, improved <a href="https://www.techtarget.com/searchdatamanagement/tip/Data-governance-roles-and-responsibilities-Whats-needed">governance and security controls</a> and a modernized user interface.</p> <p>Collectively, while none of the new features represent cutting-edge innovation, Pentaho's platform update addresses customer needs and is therefore valuable, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This is an incremental improvement in some critical areas, most notably ease of use, governance and performance," he said. "Market demands are rising in all three areas as enterprises adopt AI to democratize data consumption and streamline or enhance business processes without incurring significant risk. Pentaho is responding to the right customer priorities."</p> <p>Steven Catanzano, an analyst at Omdia -- a division of Informa TechTarget -- similarly noted that Pentaho's platform update is significant because it targets the growing need for faster, easier and more secure data integration and <a href="https://www.techtarget.com/searchbusinessanalytics/feature/The-future-of-business-intelligence-Top-trends">analytics workflows</a>.</p> <p>"Pentaho Version 11 enables organizations to become more data-driven by simplifying complex processes, reducing operational risks and providing a modern user interface that supports AI readiness," he said. "These enhancements make it easier for enterprises to extract value from their data while meeting the demands of an AI-driven future."</p> <p>Based in Santa Clara, Calif., Pentaho is an independent business unit of <a href="https://www.techtarget.com/searchdatacenter/definition/Hitachi-Vantara-formerly-Hitachi-Data-Systems-or-HDS">Hitachi Vantara</a> that provides <a href="https://www.techtarget.com/searchdatamanagement/news/252467598/Hitachi-Vantara-updates-Pentaho-83-to-expand-DataOps-vision">a platform</a> for data integration and analytics. Competitors include fellow data integration vendors such as Alteryx, Fivetran and Informatica, as well as analytics specialists such as Qlik and Tableau.</p> <section class="section main-article-chapter" data-menu-title="Speed, simplification and security"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Speed, simplification and security</h2> <p>Many enterprises have <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">made AI the focus</a> of their application development initiatives since OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI technology. AI applications, however, require far more data than traditional analytics reports and dashboards to be accurate.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This is an incremental improvement in some critical areas, most notably ease of use, governance and performance. Market demands are rising in all three areas as enterprises adopt AI to democratize data consumption and streamline or enhance business processes without incurring significant risk. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>As a result, the volume and complexity of data workloads is increasing.</p> <p>The capabilities that comprise Pentaho's platform update are designed to help users better manage larger and more elaborate data workloads that provide <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">a foundation for AI development</a>, and were prioritized based on customer feedback, according to Sandeep Prakash, the vendor's vice president of product management.</p> <p>"Version 11 has a good balance of features based on customer requests and elements we know customers will benefit from as they manage heavier data workloads," he said.</p> <p>For example, the new user interface was developed in response to user feedback while the browser-based version of Pipeline Designer is a feature aimed at easing burdens on <a href="https://www.techtarget.com/searchdatamanagement/tip/essential-data-engineer-skills-for-modern-data-environments">data engineering teams</a>, Prakash continued.</p> <p>Pipeline Designer is part of Pentaho Data Integration and is a feature that enables users to create pipelines for tasks such as <a href="https://www.techtarget.com/searchdatamanagement/definition/Extract-Load-Transform-ELT">extract, load and transform</a> (ELT) workflows. The browser-based version simplifies pipeline development by removing local installation requirements -- configurations that need to be set up on local systems -- and includes a new interface for creating jobs to make it more accessible to business users.</p> <p>Project Profile likewise addresses pipeline development. But rather than simplify individual data integration jobs, it enables Pentaho users to group related jobs, transformations and configuration files into <a href="https://www.techtarget.com/searchitoperations/definition/container-containerization-or-container-based-virtualization">containers</a> to reduce deployment complexity and better enable collaboration.</p> <p>While Pipeline Designer and Project Profile simplify building and managing pipelines, Semantic Model Editor is aimed at making it easier to model data. The new tool replaces Schema Workbench and Data Source Wizard with a modernized means of <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">creating and managing semantic models</a> that standardize defining data's characteristics -- its metadata -- to make it easier to discover and operationalize relevant data for a given initiative.</p> <p>Meanwhile, new authentication that integrates with identity providers such as Azure, Google and Okta, and redesigned permission controls both address governance and security.</p> <p>Perhaps the browser-based Pipeline Designer and Semantic Model Editor are the highlight features of Pentaho's platform update given that each simplify complex processes, according to Catanzano.</p> <p>"Pipeline Designer removes the need for local installations and offers a streamlined, intuitive interface, making it easier for distributed teams to collaborate and accelerate pipeline development," he said. "The Semantic Model Editor modernizes the analytics experience by replacing older tools with a cleaner, web-based workflow, ensuring a smoother transition for users while enhancing usability and governance."</p> <p>Petrie, meanwhile, highlighted Project Profile because it helps enterprises standardize data consumption across environments such as <a href="https://www.techtarget.com/searchnetworking/tip/How-multi-cloud-networking-can-ensure-reliability">multiple clouds</a> and on-premises systems.</p> <p>"It gives data and DevOps engineers modular, containerized pipelines that they can reuse on various platforms to speed data readiness and reduce governance risk," he said. "This helps simplify data consumption across hybrid and multi-cloud environments, which is to say most data environments."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With Version 11 of its Data Integration and Business Analytics platform update now available, Pentaho's product development roadmap is focused on helping customers build <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">a trusted data foundation</a> for AI initiatives and providing customers with automation and natural language processing capabilities to improve productivity, according to Prakash.</p> <p>"We see our roadmap aligning with customer needs that fall into two categories -- data for AI, and AI for data," he said. "Over the coming quarters, you'll see us deliver capabilities around AI-enabled discovery, semantic search for data [and] building … agentic workflows."</p> <p>Petrie noted that Pentaho's data integration and analytics capabilities are generally in line with those of its competitors. However, one way the vendor differentiates itself is with data optimization capabilities that help customers identify and archive less-used datasets to <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">reduce costs</a>. Creating messaging that emphasizes Pentaho's unique capabilities would be wise, Petrie advised.</p> <p>"I'd be interested to see Pentaho play this up more in their sales and marketing efforts," he said.</p> <p>Catanzano, meanwhile, suggested that Pentaho could continue serving its current users and perhaps attract new ones by adding features and integrations that better enable customers to <a href="https://www.techtarget.com/searchapparchitecture/opinion/A-hands-on-look-at-AI-agents">develop AI tools</a>.</p> <p>"To continue evolving, Pentaho could expand its AI and machine learning capabilities by integrating with popular AI frameworks or offering pre-built, industry-specific AI models," he said. "This would not only enhance its value for existing users but also attract new customers seeking to accelerate their AI adoption."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The vendor's latest platform update aims to speed, simplify and better govern workloads to help customers build a trusted foundation for AI development. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1226966455.jpg https://www.techtarget.com/searchdatamanagement/news/366638792/Pentaho-update-aids-data-integration-semantic-modeling Wed, 04 Feb 2026 08:30:00 GMT <p>As winter's chill blankets much of the U.S., Snowflake continues to drop new capabilities that simplify developing agents and other advanced applications.</p> <p>On Tuesday during Snowflake Build London, a user event in the United Kingdom, the vendor launched Cortex Code, Semantic View Autopilot and the native integration of <a href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake Postgres</a> in its AI Data Cloud, among a spate of other capabilities.</p> <p>Cortex Code is an agent that enables users to generate code for building pipelines and applications while applying an enterprise's security and governance controls. Semantic View Autopilot is an AI-powered service that automates creating and governing the semantic views that give agents proper context. And Snowflake Postgres is a PostgreSQL database that Snowflake acquired in June 2025.</p> <p>In addition to the new features, Snowflake on Feb. 2 unveiled a $200 million partnership with OpenAI that makes <a href="https://www.techtarget.com/searchenterpriseai/news/366622996/Whats-new-and-not-new-with-OpenAIs-latest-reasoning-models">OpenAI models</a> natively available within Snowflake's Cortex AI development environment. In addition, it includes plans for collaborating to build and deploy customized AI capabilities.</p> <p>Collectively, the partnership and new capabilities are important advances for Snowflake, according to William McKnight, president of McKnight Consulting. In particular, he noted the value of eliminating costly and complex data pipelines by natively embedding Snowflake Postgres in the vendor's AI Data Cloud and Cortex Code's understanding of an enterprise's data environment.</p> <p>"Snowflake, in this trove of announcements, wins&nbsp;the&nbsp;year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform," McKnight said.</p> <p>Sanjeev Mohan, founder and principal of analyst firm SanjMo, similarly called Snowflake's latest slew of features significant for <a href="https://www.techtarget.com/searchdatamanagement/feature/Whoop-pushing-AI-limits-powered-by-Snowflake">the vendor's customers</a>.</p> <p>"Snowflake already innovates fast, and the pace has picked up," he said. "Collectively, they give customers more optionality. And there was a big emphasis on skills, helping users codify their complex processes. That's a big benefit."</p> <p>Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data management vendor that has added AI development capabilities over the past few years in response to surging interest from customers in AI. Build London marks the third event in the last eight months at which Snowflake has unveiled a multitude of new AI capabilities, following <a href="https://www.techtarget.com/searchdatamanagement/news/366625010/AI-tools-highlight-latest-swath-of-Snowflake-capabilities">Summit last June</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366634007/Snowflake-delivers-slew-of-AI-tools-introduces-new-ones">Build last November</a>.</p> <section class="section main-article-chapter" data-menu-title="Driving development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Driving development</h2> <p>Snowflake's aim is to enable customers to create a connected data estate that can be trusted as a foundation for building and deploying AI and analytics applications, according to Christian Kleinerman, the vendor's executive vice president of product who spoke during a virtual press conference on Jan. 28.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Snowflake, in this trove of announcements, wins the year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Despite enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing their investments in AI development</a> and vendors such as Snowflake and rival Databricks attempting to simplify building cutting-edge tools by providing development frameworks, most AI initiatives <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">never make it past experimentation</a> and into production.</p> <p>Poor data foundations and improper alignment with governance policies are among the main reasons that the failure rate remains so high.</p> <p>Each of the three main capabilities Snowflake unveiled on Tuesday are designed to help customers create a connected, trusted data foundation for AI and analytics.</p> <p>AI-powered code generation capabilities are not uncommon. However, tools that align natural language-generated code with governance and security policies from the outset of the development process are uncommon. And when enterprise-grade governance and security policies are applied to code late in development, the AI-generated code often doesn't align with an enterprise's governance and security standards, and the project never makes it past the pilot stage.</p> <p>Domo recently <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">launched App Catalyst</a>, an AI-powered code generator that integrates governance and security from the outset of a project. Now, Snowflake is doing something similar with the release of Cortex Code.</p> <p>Cortex Code, now part of Snowflake's Cortex AI development suite, understands user data, governance and operational semantics to give it context for creating code, and maintains an enterprise's governance and security standards to ensure that the code is enterprise-grade. Using the tool, data and AI teams can create production-ready applications far more efficiently than when they write code on their own.</p> <p>"The most significant announcement we're making at Build is we're introducing Cortex Code," Kleinerman said.</p> <p>While Cortex Code aids AI development by simplifying code generation, Semantic View Autopilot automates the creation of <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">a semantic layer</a> so that <a href="https://www.techtarget.com/whatis/definition/metadata">metadata</a> and metrics are consistent across an organization and data can be discovered and trusted to inform analytics and AI applications. Similarly, running Snowflake Postgres natively within Snowflake's AI Data Cloud rather than externally through an integration advances development by simplifying access to unified transactional and analytical data that informs applications.</p> <p>"I like Semantic View Autopilot," Mohan said. "For non-technical users to create agents with Snowflake Intelligence -- Cortex Code is for techies -- really well, there has to a robust semantic layer. That, to me, is the most important of the new items."</p> <p>McKnight, meanwhile, called out integrating Snowflake Postgres into <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">the AI Data Cloud</a> as perhaps the most valuable of the new features.</p> <p>"Snowflake Postgres [transforms] Snowflake from a purely analytical data warehouse into a transactional and analytical platform," he said. "Snowflake is not the first to bridge this gap, but it's significant because … it opens entirely new use cases, removes the cost and complexity of [extract, transform and load] pipelines, and enables zero-code migration."&nbsp;</p> </section> <section class="section main-article-chapter" data-menu-title="Beyond the big three"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Beyond the big three</h2> <p>In addition to the launches of Cortex Code and Semantic View Autopilot, and the native integration of Snowflake Postgres in the AI Data Cloud, Snowflake's new partnership with OpenAI is a significant move, according to McKnight.</p> <p>Cortex AI enables users to access numerous AI models, including those from AI21 Labs, Anthropic, DeepSeek, Google Cloud, Meta and Mistral AI.</p> <p>Even OpenAI models were available to users before the new partnership between the AI developer and Snowflake. However, they were only available through <a href="https://www.techtarget.com/searchdatamanagement/news/366619812/Snowflake-adds-OpenAI-models-with-Microsoft-integration">Snowflake's integration with Microsoft</a>.</p> <p>Native availability is a direct integration between a model and the architecture of an AI development environment such as Cortex AI, including its access to data and enforcement of governance and security policies. Unlike other methods of connecting models with development environments such API integrations or plug-ins, no complex configurations are required.</p> <p>In addition to OpenAI's models, models from <a href="https://www.techtarget.com/searchdatamanagement/news/366635815/Snowflake-Anthropic-boost-partnership-with-200M-commitment">Anthropic</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">Google Cloud</a>, Meta and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366572276/Snowflake-signals-AI-commitment-with-Mistral-AI-partnership">Mistral AI</a> are natively available in Cortex AI.</p> <p>"Moving OpenAI models natively into Snowflake is a game-changer because it keeps sensitive data entirely within the Snowflake security perimeter, effectively removing the complex governance and data egress obstacles that kill enterprise AI projects," McKnight said. "Instead of complicated engineering, analysts can trigger GPT using simple SQL functions, democratizing high-level AI across the organization."</p> <p>Additional new features Snowflake unveiled during Build London include the following:</p> <ul type="disc" class="default-list"> <li>Expansion of the Snowflake Horizon Catalog to include the open-source Polaris Catalog, which lets customers securely access data in <a target="_blank" href="https://iceberg.apache.org/" rel="noopener">Apache Iceberg</a> tables as well as create, update and manage data stored in Iceberg tables.</li> <li>Open Format Data Sharing to extend Snowflake's zero-ETL capabilities to open table formats Apache Iceberg and <a href="https://www.techtarget.com/searchdatamanagement/news/366542953/Databricks-introduces-Delta-Lake-30-to-help-unify-data">Delta Lake</a>.</li> <li>Snowflake Backups to protect business-critical data from ransomware or disruptions.</li> <li>Updates to Snowflake Notebooks, including an integration with Cortex Code and Experiment Tracking, to make it easy for teams to compare testing results and reproduce top-performing models.</li> <li>Cortex Agent Evaluations so users can trace, measure and audit agent behavior.</li> <li>An integration with Vercel that enables <a href="https://www.techtarget.com/searchcio/feature/Vibe-coding-What-IT-leaders-need-to-know">vibe coding</a> -- AI-assisted code generation using natural language prompts -- to build applications that can be deployed in Snowflake through Snowpark Container Services.</li> <li>An integration with the Brave Search API so users can integrate real-time information from the internet into Snowflake Intelligence, Cortex Code and Cortex Agents to augment an enterprise's proprietary data.</li> <li>New features in Workspaces, Snowflake Notebooks and OpenID Connect aimed at better enabling <a href="https://www.techtarget.com/searchsoftwarequality/tip/Improving-DevOps-collaboration-Challenges-and-tips">collaborative development</a>.</li> </ul> <p>"Some of [the new features] are improvement on existing technologies, but in all instances it's customer-driven innovation," Kleinerman said regarding Snowflake's impetus for developing the capabilities introduced at Build London.</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Two years after making AI development its main priority, Snowflake might have finally caught up to Databricks and other data and AI platform vendors, according to Mohan.</p> <p>After OpenAI's November 2022 launch of ChatGPT significantly improved generative AI technology, Databricks and hyperscale cloud vendors AWS, Google Cloud and Microsoft all quickly reacted. They created environments for customers to build AI tools, including development frameworks and integrations with AI providers such as OpenAI.</p> <p>Snowflake was slower to react, and only fully committed to enabling AI development in February 2024 when <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571855/Snowflake-CEO-Slootman-steps-down-Ramaswamy-takes-over">Sridhar Ramaswamy was named CEO</a>.</p> <p>"They've caught up," Mohan said, noting that Google Cloud similarly had to catch up after being viewed as an innovator of machine learning capabilities with its 2017 release of the Transformer <a href="https://www.techtarget.com/searchenterpriseai/definition/neural-network">neural network</a> architecture.</p> <p>"Google invented the Transformer and then watched the whole world take off with not only OpenAI but Meta with Llama and others," Mohan continued. "But look where Google is now with Gemini. So, it is too early to call winners in AI, and Snowflake has demonstrated that it has caught up after a late start."</p> <p>McKnight likewise noted that with the release of its latest set of features -- particularly the integration of its Polaris and Horizon <a href="https://www.techtarget.com/searchbusinessanalytics/news/252510804/Data-catalogs-fuel-increased-efficiency-speed-to-insight">data catalogs</a> -- Snowflake has fully caught up with its peers.</p> <p>"Snowflake is now arguably ahead of Databricks in its ability to unify transactional applications and analytics, while having simultaneously neutralized the 'lock-in' argument," he said. "By embedding Apache Polaris directly into the Horizon Catalog, Snowflake now offers the same open governance as Databricks' Unity Catalog."</p> <p>Looking ahead to what Snowflake could do next to continue serving its users and perhaps even attract new ones, McKnight named adding <a href="https://www.computerweekly.com/opinion/Better-governance-is-required-for-AI-agents">agent governance capabilities</a> and more cost transparency.</p> <p>"In its highly competitive market, it needs to address agent governance with a layer that governs intent and action, and application-centric costing where instead of seeing costs by warehouse, there is a 'Product View' that bundles the costs of the Postgres instance, the Snowpark Container Services and the Cortex API."</p> <p>Mohan, meanwhile, suggested that Snowflake take steps to unify transactional processing and observational data such as <a href="https://www.techtarget.com/searchcustomerexperience/feature/To-predict-customer-buying-behavior-stop-look-listen-analyze">customer behavior</a>.</p> <p>"I would like them to show how I as a developer can access all my data in a unified manner through a catalog," he said. "Horizon doesn't handle observe data, and I'd like to see all data in one place."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with three decades of experience. He covers analytics and data management.</i></p> </section> New features such as an agent-powered code generator and automated semantic modeling simplify developing cutting-edge applications and improve the vendor's competitive standing. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1287248739.jpg https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership Tue, 03 Feb 2026 03:00:00 GMT <p>Ethical data use is often regarded as a regulatory concern. However, when organizations use data ethically, it can drive business outcomes and determine overall success.</p> <p><a target="_blank" href="https://www.customerexperiencedive.com/news/global-trust-digital-services-consumer-data-privacy-concerns/742877/" rel="noopener">Growing data privacy concerns</a> among customers, employees and even government agencies have pushed organizations to consider the larger ramifications of how they use the data they collect. Organizations that prioritize ethical data use build trust and have a more favorable public image than those that exploit customer data.</p> <p>But the benefits derived from ethical data use go beyond fostering customer trust. Employees, partners, and other key stakeholders will also see the company as trustworthy, giving them a competitive advantage. As such, leadership should create a culture that emphasizes ethical data use and implement strategies to engrain it into all business practices.</p> <section class="section main-article-chapter" data-menu-title="Why ethical data use matters"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why ethical data use matters</h2> <p>Regulatory concerns, such as <a href="https://www.techtarget.com/searchsecurity/tip/State-of-data-privacy-laws">hefty fines, legal action and reputational damage</a>, are often the primary drivers of ethical data handling. However, all organizations -- regulated and non-regulated, large and small -- should prioritize ethical data handling.</p> <p>Data is often an organization's most valuable asset. Although businesses already gain tremendous value from their data, there's ever-mounting pressure to extract even more value. This was the primary driving force behind <a href="https://www.techtarget.com/whatis/feature/A-history-and-timeline-of-big-data">the big data revolution</a>.</p> <p>However, organizations must draw a line between obtaining additional business value and outright exploitation. While important, this isn't just about addressing regulatory requirements, but meeting customer expectations. Recently, consumers have become keenly aware of how their data is collected and sold. As such, organizations face increasing customer demands for transparency and ethical data use.</p> <p>As data privacy has become top of mind for consumers, it stands to reason they will gravitate toward businesses with transparent operations that avoid the temptation to use customer data in an unethical manner. When a business builds trust with its customers, it multiplies. Acquiring more customers means acquiring more data, thus adding value. This is especially true in <a href="https://www.techtarget.com/searchenterpriseai/feature/9-data-quality-issues-that-can-sideline-AI-projects">the age of AI</a>, as it amplifies the benefits of additional data while also increasing the risk of exposure.</p> <p>However, that trust is easily broken. Selling customer data or suffering a data breach can cause massive reputational damage, and as a result, some customers might take their business to a more trustworthy organization. New customers might be harder to attract -- especially in the aftermath -- leading to lost revenue.</p> </section> <section class="section main-article-chapter" data-menu-title="Strategies for ethical data use"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Strategies for ethical data use</h2> <p>While it's easy to advocate for ethical data handling, implementing those practices can be difficult. Keep in mind that data ethics is a continuous process. It requires <a href="https://www.techtarget.com/searchcio/definition/data-governance-policy">data governance policies</a> to evolve as technology changes, along with ongoing monitoring, evaluation, and adaptation.</p> <h3>Within the organization</h3> <p>For any data ethics initiative to succeed, it can't be treated solely as an IT project. If that were the case, there would be no real authority to enforce policy, especially if the organization isn't regulated. Higher-ups in the organization could demand that IT violate the policies it has established.</p> <p>Taking a top-down approach in policy directives prevents this issue. C-suite executives must drive ethical data initiatives and establish organizational policies. The board, meanwhile, must hold decision makers accountable.</p> <p>However, it isn't enough for the C-suite to say that the organization is committed to ethical data handling; they must formalize data governance rules. Documenting specific data usage policies is critical to making them a central part of the operational framework.</p> <p>Employees must also receive training on handling sensitive data, and the training should be extended to everyone who handles data. Depending on the organization's size, it might be prudent to hire a <a target="_blank" href="https://www.informationweek.com/machine-learning-ai/age-of-ai-why-organizations-need-a-chief-ethics-officer" rel="noopener">Data Ethics Officer</a> or even a Data Ethics Team. This person or team should conduct regular audits and assessments to ensure responsible data handling.</p> <h3>Outside the organization</h3> <p>Once documentation and implementation occur, clear communication with customers is important. Businesses must tell their customers exactly how their data is used, even if there's no legal requirement to do so. Customer disclosures should be concise and easy to read, rather than buried in lengthy Terms of Service documents that customers are likely to skip. By releasing a data use disclosure, the business is making a commitment to its customers and must <a href="https://www.techtarget.com/searchcustomerexperience/answer/How-do-companies-protect-customer-data">carefully adhere to its terms</a>. Failing to do so destroys trust and might leave the business legally exposed.</p> <p>Customers aren't the only outside party organizations must consider. They must also subject partners and suppliers to ethical data handling requirements. While it might be impossible for businesses to operate without sharing customer data with suppliers and partners, the organization is ultimately responsible for how partners and suppliers handle it. As such, organizations must require partners and suppliers to adhere to the same strict data governance policies that they themselves follow.</p> <h3>Technological strategies</h3> <p>To prevent unauthorized access and misuse of data, <a href="https://www.techtarget.com/searchsecurity/feature/How-to-create-a-data-security-policy-with-template">implement security controls</a>. Such controls go a long way toward preventing data breaches. When combined with zero-trust principles and least privilege access, such controls can also guard against insider risks. This helps prevent employees from circumventing data controls already in place.</p> <p>A final step the organization can take in its quest for ethical data use is anonymizing data whenever possible. To do so, disassociate raw data from personally identifiable information. This enables the use of sensitive data to train AI models without risking customer privacy, thereby maintaining trust.</p> <p><i>Brien Posey is a former 22-time Microsoft MVP and a commercial astronaut candidate. In his more than 30 years in IT, he has served as a lead network engineer for the U.S. Department of Defense and a network administrator for some of the largest insurance companies in America.</i></p> </section> Enterprises that don't use data ethically have a lot to lose. To maintain their businesses' trustworthiness and value, executives must craft a comprehensive, transparent strategy. https://cdn.ttgtmedia.com/rms/onlineimages/collab_g1213710695.jpg https://www.techtarget.com/searchbusinessanalytics/feature/Why-ethical-use-of-data-is-so-important-to-enterprises Fri, 30 Jan 2026 16:15:00 GMT <p>Alteryx on Wednesday unveiled an expanded partnership with Google Cloud that includes the launch of Live Query for BigQuery, a new feature that enables joint customers to build and run Alteryx workflows in BigQuery.</p> <p>Previously, to use <a href="https://www.techtarget.com/searchdatamanagement/news/366615682/Alteryx-adds-tools-for-cloud-hybrid-analytics-deployments">Alteryx's data integration and preparation capabilities</a>, Alteryx users that store data in BigQuery first had to move data out of BigQuery and into Alteryx. Doing so, they incurred both the cost of <a href="https://www.techtarget.com/searchdatamanagement/definition/data-egress">data egress</a> and the inherent risk of exposure anytime data passes from one platform to another.</p> <p>Live Query for BigQuery eliminates those costs and risks.</p> <p>Beyond security and efficiency, it enables joint Alteryx and Google Cloud customers to better scale workloads given that BigQuery's servers have more capacity than Alteryx's servers, according to Donald Farmer, founder and principal of TreeHive Strategy. As a result, he noted that Live Query for BigQuery is a significant new feature for Alteryx customers using Google Cloud for storing their data.</p> <p>"Live Query for BigQuery is good in many ways because it enables BigQuery-scale analytics, which is way beyond the capacity of any Alteryx server, and Google Cloud customers will like it because it keeps data in-place, secured and managed by Google," Farmer said.</p> <p>In addition to Live Query for BigQuery, Alteryx's expanded partnership includes developing Alteryx One: Google Edition, a version of Alteryx's platform purpose-built for <a href="https://www.computerweekly.com/news/366546394/Pentland-Brands-marshals-Google-BigQuery-to-improve-customer-insight">Google Cloud customers</a> that will be available through the Google Cloud Marketplace.</p> <section class="section main-article-chapter" data-menu-title="Combined capabilities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Combined capabilities</h2> <p>Based in Irvine, Calif., Alteryx is a longtime data management vendor providing a platform for integrating data from disparate sources and preparing it for analytics and AI. In recent years, Alteryx has made automation a key feature of its various tools, and in May 2025 <a href="https://www.techtarget.com/searchdatamanagement/news/366623973/Alteryx-One-launch-aims-to-unify-simplify-vendors-platform">launched Alteryx One</a> to unify previously disparate capabilities and make its suite easier to navigate.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Live Query for BigQuery is good in many ways because it enables BigQuery-scale analytics, which is way beyond the capacity of any Alteryx server, and Google Cloud customers will like it because it keeps data in-place, secured and managed by Google. </figure> <figcaption> <strong>Donald Farmer</strong>Founder and principal, TreeHive Strategy </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>BigQuery, meanwhile, is a fully managed data warehouse and AI platform purpose-built to enable fast <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a> queries and data analysis across massive datasets.</p> <p>The impetus for developing Live Query for BigQuery came from a shared realization between Alteryx and Google Cloud that joint customers want to use Alteryx and BigQuery in conjunction with one another, but that doing so can be too complex for some data teams, according to Ben Canning, Alteryx's chief product officer.</p> <p>Specifically, he noted that users want the power, security and governance of <a href="https://www.techtarget.com/searchbusinessanalytics/news/366599575/Google-Clouds-BigQuery-gets-AI-injection-Looker-to-follow">a centralized platform like BigQuery</a> along with the data preparation and integration capabilities of a vendor such as Alteryx.</p> <p>"Business users clearly see the value of these platforms, but they're often too complex for non-technical teams to fully take advantage of on their own," Canning said. "That means a lot of the business-critical work -- things like data prep, calculations and logic -- still happens outside the platform. … Live Query for BigQuery brings those together."</p> <p>The result is stronger governance and security for <a href="https://www.techtarget.com/searchdatamanagement/feature/Effective-integration-key-to-creating-trusted-data">data integration</a> and preparation workloads with reduced complexity, he continued.</p> <p>"That shared customer need is what really drove this partnership forward," Canning said.&nbsp;</p> <p>Specific benefits of Live Query for BigQuery include the following, according to Alteryx:</p> <ul type="disc" class="default-list"> <li>Preparing data and applying business logic to data using Alteryx directly in BigQuery.</li> <li>Executing and automating governed data integration and preparation workflows at data warehouse scale.</li> <li>Maintaining the <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Data-governance-framework-key-to-analytics-success">centralized governance</a>, security, and performance of Google Cloud.</li> <li>Enabling faster insight generation for data, analytics and AI teams.</li> </ul> <p>Matt Aslett, an analyst at ISG Software Research, noted that the widespread adoption of cloud data platforms such as AWS, Google Cloud, Microsoft, Snowflake and Databricks has led to close integration between analytics and data preparation specialists such as Alteryx and <a href="https://www.techtarget.com/searchcloudcomputing/definition/hyperscale-cloud">hyperscale cloud</a> providers. As a result, Alteryx's alignment with Google Cloud is not the first such partnership to produce capabilities that reduce the need to move data between platforms.</p> <p>In fact, Alteryx offers Live Query capabilities for Databricks and Snowflake. However, Live Query for BigQuery is nevertheless significant given that it expands Alteryx's Live Query offerings to those customers that also use BigQuery for their <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluate-cloud-data-warehouses-based-on-data-outcomes">data warehouse needs</a>, according to Aslett.</p> <p>"Alteryx Live Query for BigQuery will enable Alteryx users with Google BigQuery to … potentially improve performance and reduce cost and complexity," he said.</p> <p>Farmer similarly noted that Live Query for BigQuery isn't unique in the sense that it enables users to push their analytics and data preparation workloads down into their data warehouse. However, the new tool has characteristics such as a drag-and-drop experience for direct queries and optimization for BigQuery that somewhat differentiate it from similar offerings, Farmer added.</p> <p>"I would say only that it is unique in its specifics, rather than its concepts," he said.</p> <p>Meanwhile, despite Live Query for BigQuery's potential value to joint Alteryx and Google Cloud customers, not all users might like the feature, Farmer cautioned.</p> <p>Live Query for BigQuery changes the way users work with data stored in BigQuery, removing it from <a href="https://www.techtarget.com/searchbusinessanalytics/news/366617637/New-Alteryx-CEO-sees-platform-as-the-canvas-for-AI-prep">the Alteryx environment</a> some users might prefer. However, for large workloads, the benefits of working with data where it resides rather than moving it between systems likely outweighs other concerns.</p> <p>"Alteryx had the best user experience to support an iterative workflow of preparation, analysis, re-preparation when something did not look quite right, and re-analysis," Farmer said. "This becomes much more difficult with live queries. But at this scale, it becomes somewhat impractical anyway."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Just as Live Query for Big Query speeds and simplifies data workflows, Alteryx's product development plans for 2026 focus on further enabling customers to manage and analyze data more efficiently, according to Canning.</p> <p>"That means continuing to bring analytics and AI workflows closer to where trusted data lives, expanding in-place execution, and making business logic a governed, reusable asset rather than something buried in spreadsheets or code," he said.</p> <p>In addition, Alteryx is focused on helping customers build AI models and applications using clean, well-prepared data <a href="https://www.techtarget.com/searchdatamanagement/opinion/Generative-AI-shines-spotlight-on-data-governance-and-trust">that can be trusted</a>, Canning continued.</p> <p>Aslett noted that Alteryx has substantially expanded its product portfolio since <a href="https://www.techtarget.com/searchbusinessanalytics/news/366563665/Alteryx-to-be-acquired-by-private-equity-firms-for-44-billion">it was acquired</a> by Clearlake Capital and Insight Partners. In particular, the vendor has improved its offerings for cloud and hybrid environments and added AI-powered tools for data preparation and analytics.</p> <p>Regarding potential next steps, Aslett suggested that after expanding its partnership with Google Cloud, Alteryx do the same with other hyperscale cloud providers.</p> <p>"By offering tight integration with Google Cloud and ease of adoption through the Google Cloud Marketplace, Alteryx lowers barriers to adoption for Google Cloud customers," he said. "As such, I would anticipate similar editions of Alteryx One purpose-built for the likes of AWS, Microsoft Azure, Databricks and Snowflake."</p> <p>Farmer, meanwhile, suggested that Alteryx do more to help customers control costs. According to <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2024-11-05-gartner-says-cios-need-to-overcome-four-emerging-challenges-to-deliver-value-with-artificial-intelligence#:~:text=The%20Cost%20of%20AI%20Can,risk%20as%20security%20or%20hallucinations." rel="noopener">a 2024 Gartner survey</a> of more than 300 CIOs, cost is a prohibitive factor in developing advanced applications. Spending on cloud-native capabilities such as Live Query for BigQuery can quickly add up, so providing a tool that enables customers to predict expenses would be beneficial.</p> <p>"They need deeper cost governance, especially with this Live Query feature because as users run more of these queries cloud costs can spike," Farmer said. "They need a cost estimator that tells a user how much a workflow will cost in BigQuery credits before they hit 'run'. … Alteryx could do something interesting here."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more three decades of experience. He covers analytics and data management.</i></p> </section> Live Query for BigQuery eliminates the need to move data between systems, saving customers from spending on data egress and reducing the risk of security leaks. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1226966455.jpg https://www.techtarget.com/searchdatamanagement/news/366637939/Alteryx-launches-in-warehouse-data-prep-tool-for-BigQuery Wed, 28 Jan 2026 15:33:00 GMT <p>Domo, once an analytics specialist, is evolving with market trends to add capabilities to its platform that simplify developing agents and other cutting-edge AI applications.</p> <p>Toward that end, the vendor on Wednesday launched App Catalyst, a new tool within its AI and Data Products Platform that enables developers and other application builders to use natural language prompts when creating <a href="https://www.techtarget.com/searchitchannel/post/What-low-code-no-code-and-pro-code-mean-for-providers">pro-code tools</a> informed by their Domo data.</p> <p>However, unlike natural language interfaces such as <a href="https://www.techtarget.com/searchcio/feature/Vibe-coding-What-IT-leaders-need-to-know">vibe coding tools</a> that are aimed at quickly generating raw code to fuel AI pilots, App Catalyst helps data and AI teams generate code while also applying data access, security and operational standards at the outset of AI projects. The intent is to give AI initiatives a better chance of having a business impact than when access to data, governance frameworks and security restrictions are applied later in the development process.</p> <p>Given that App Catalyst combines AI-powered code generation with the governance and security enterprises require to use an application, its addition to Domo's platform is significant, according to Mike Leone, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"We're seeing a shift where the hard part is no longer writing code, but more managing it," he said. "For Domo customers, this announcement matters because it … allows customers to move from a rough idea and experimentation to a legitimate, compliant business app without getting bogged down by the usual deployment hurdles."</p> <p>Based in American Fork, Utah, Domo is a cloud-based analytics vendor that, like peers including Qlik and Strategy, has added AI development capabilities since OpenAI's November 2022 launch of ChatGPT and sparked <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">surging interest building AI tools</a>.</p> <section class="section main-article-chapter" data-menu-title="Striving for success"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Striving for success</h2> <p>Despite the emphasis enterprises and platform providers alike are placing on AI development, most AI pilots <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">never make it into production</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> For Domo customers, this announcement matters because it … allows customers to move from a rough idea and experimentation to a legitimate, compliant business app without getting bogged down by the usual deployment hurdles. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Recently, vendors including <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">MongoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366637641/Teradatas-AgentStack-aims-to-simplify-building-managing-AI">Teradata</a> have all introduced capabilities that are designed to improve aspects of the AI development cycle in an attempt to help customers more successfully build AI tools. With App Catalyst now part of its platform, Domo is similarly trying to help customers more successfully build AI tools.</p> <p>Vibe coding, which is AI-assisted development using natural language prompts to create code, has enabled data and AI teams to quickly build prototypes. But it's not meant for creating enterprise-grade AI applications that can be trusted to generate insights and autonomously execute business processes.</p> <p>Fragile code and lack of alignment with enterprise data governance and security policies are among <a target="_blank" href="https://www.linkedin.com/pulse/vibe-coding-creates-nightmare-computer-scientists-fix-yerramsetti-b5pjc/" rel="noopener">vibe coding's shortcomings</a>.</p> <p>App Catalyst is designed to offer the same simplicity as vibe coding, according to Ben Schein, Domo's chief analytics officer and SVP of product development. But instead of solely simplifying coding, App Catalyst integrates code from the outset with enterprise-grade capabilities.</p> <p>"Vibe coding was all over the place, and our team was using it for fun side projects at home," Schein said. "But Domo always had the ability to create pro-code app components for use in low-code apps and dashboards. … So, it was natural to think we could lower that barrier."</p> <p>Key benefits of App Catalyst's addition to the Domo AI and Data Products Platform include quick ideation through natural language prompts, self-service development of pro-code applications, rapid prototyping that enables projects to move go ideas to production and automatic integrations with <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Data-governance-framework-key-to-analytics-success">governance and security policies</a>.</p> <p>Perhaps most valuable to Domo users is the automatic integration with governance and security frameworks, according to Leone, who noted that innovation stalls when applications aren't built with consistent governance standards that can be applied across an organization.</p> <p>"It's about more than just security permissions," he said. "By baking governance in up front, Domo is helping customers solve the compliance headache that typically kills projects before they start."</p> <p>Like Leone, David Menninger, an analyst at ISG Software Research, named integration with governance and security frameworks App Catalyst's most valuable feature, while calling a tool that enables users to create enterprise-grade applications with natural language "significant."</p> <p>"Generative AI has enabled all types of natural language processing and assistance in software products, but natural language app creation is probably one of the most significant improvements," he said. "It's not bounded the way other assistants are bounded. Users are only bounded by their imagination -- and the availability of the data."</p> <p>Regarding the value of <a href="https://www.techtarget.com/searchdatabackup/tip/Enterprise-data-governance-Frameworks-and-best-practices">a governance foundation</a> from the outset, Menninger added that attempting to add governance after an application is built often causes substantial delays.</p> <p>"While it may not be sexy, the governance foundation inherent in the Domo platform is one of the benefits enterprises will appreciate the most," he said.</p> <p>While beneficial for <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domo-platform-a-difference-maker-for-check-guarantee-vendor">Domo users</a>, App Catalyst's AI-powered code generation capabilities are not unique, Leone noted. Many vendors provide tools that perform the same task, including hyperscale cloud vendors AWS, Google Cloud and Microsoft. Integration with governance, security and curated data from the outset, however, is more unique, Leone continued.</p> <p>"Most other options require you to stitch together a database, a separate App Catalyst and a governance layer," he said. "Domo is differentiating by offering that entire stack in one place. … That end-to-end flow is a bit harder to find."</p> <p>Menninger likewise noted that App Catalyst adds something unique to Domo's platform, but predicted&nbsp; that competing vendors will be able to provide similar capabilities.</p> <p>"Nearly all have generative AI assistants that can create dashboards and answer natural language queries," he said. "Some have code generation within notebooks, but few have full app development capabilities."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Just as App Catalyst's addition to Domo's platform adds an AI-powered tool for fueling application development, the vendor's product development plans over the first half of 2026 focus on <a href="https://www.techtarget.com/searchbusinessanalytics/news/366621048/Domo-unveils-agentic-AI-toolkit-to-simplify-development">providing more AI capabilities</a>, according to Schein.</p> <p>"Cloud Integrations and AI remain a strong focus," he said.</p> <p>AI initiatives include continuing to make it easier for customers to develop customized AI-powered agents and chatbots and expanding <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">its Model Context Protocol server</a> to provide users with a framework for connecting agents developed in Domo with other agents and AI platforms, Schein continued.</p> <p>One feature Domo and its peers would be wise to add is <a href="https://www.techtarget.com/searchbusinessanalytics/news/252523947/Scenario-planning-fertile-ground-for-analytics-vendors">a scenario planning tool</a>, according to Menninger. Despite analytics vendors expanding beyond their traditional roots over the past few years, few have added capabilities that enable users to evaluate potential outcomes.</p> <p>"Humans and agents need to evaluate alternative scenarios and that requires planning capabilities," Menninger said. "There are very few vendors that have combined planning with analytics, but I expect we will see more."</p> <p>Leone, meanwhile, suggested that Domo advance App Catalyst beyond being an interactive tool and turn it into <a href="https://www.techtarget.com/searchenterpriseai/news/366617713/Autonomous-AI-agents-on-the-rise">an autonomous agent</a>. He noted that, in general, Domo has proven to be resiliently innovative over the past few years by adding AI development and AI-powered capabilities to its platform. Advancing App Catalyst would further demonstrate Domo's adaptability.</p> <p>"They're already simplifying the build process," Leone said. "There's a massive need for apps that can execute tasks without constant human input. If they can make these AI-generated apps smart enough to trigger their own workflows based on real-time data changes, that will be a gamechanger."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> By combining natural language code generation with enterprise-grade security and governance, the vendor aims to help customers more successfully build cutting-edge applications. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1289411982.jpg https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development Wed, 28 Jan 2026 09:51:00 GMT <p>AI made its move from pilot projects to production use for most enterprises in 2025, but many deployments will lag -- or outright flop -- until legacy big data architectures get a refresh.</p> <p>While enterprises are eager to put AI to work, most still run data environments built for another era. Traditional data warehouse architectures support basic reporting and business intelligence. Twenty years ago, organizations began implementing big data architectures built on data lakes containing diverse data sets to support advanced analytics applications, as well as robotic process automation. But such architectures often lack real-time data access and other modern data technologies. As a result, the promise of AI remains out of reach.</p> <p>More than just new technology is needed. To make the required data modernization a reality, data management leaders must tackle the fundamentals: breaking down data silos that formed either from acquisitions or out of business necessity, streamlining processes that slow data delivery and tightening security and privacy controls so organizations can scale data access with proper protections in place.</p> <p>These moves should be pragmatic rather than wholesale. A phased approach to an upgrade protects systems of record while moving the most important business areas <a href="https://www.techtarget.com/searchenterpriseai/feature/The-future-of-generative-AI-Trends-to-follow">to a more flexible foundation</a>. During this process, organizations must also weigh when to keep data on-premises and when it belongs in the cloud, embed data governance into everyday work and understand what signs of progress look like.</p> <div class="youtube-wrapper"> <iframe width="560" height="315" src="https://www.youtube.com/embed/jH44SfUNpWw?rel=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </div> <section class="section main-article-chapter" data-menu-title="Why AI needs a big data upgrade"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why AI needs a big data upgrade</h2> <p>Organizations worldwide are bullish on the transformative power of AI, believing the technology is essential to stay competitive today and in the future. That belief is driving numerous AI implementations across various enterprise functions.</p> <p>A survey commissioned by data platform software company Cloudera found that in 2024, 88% of enterprises were using AI. A 2025 follow-up reported 96% had integrated AI into their core processes to at least some extent.</p> <p>But a lot of organizations are struggling. "[E]vidence suggests that transformative value [from AI] remains elusive for many companies primarily due to the limitations of the outdated data infrastructures that are powering AI tools," Cloudera wrote in a report on its 2024 survey.</p> <p>To support AI at scale, enterprises are finding they must revamp their big data architecture and operations. Leaders increasingly see a modern data foundation as essential to achieve that goal, said<b> </b>Niranjan Ramsunder, CTO and head of data services at UST, a technology consulting firm.</p> <p>"Data is critical these days. It's central to an organization's success, and a good data architecture and a good data strategy are both critical for an organization to succeed," Ramsunder said.</p> <p>Ramsunder said many organizations did not allocate money for big data infrastructure modernization efforts over the past decade because they did not anticipate seeing a positive <a href="https://www.techtarget.com/searchstorage/opinion/IT-leaders-face-data-infrastructure-gaps-as-AI-workloads-grow">return on these investments</a>. However, AI has changed those ROI perspectives.</p> <p>"Expectations have changed," Ramsunder said, "and modernization has to be done."</p> <p>Others give similar advice.</p> <p>"To stay competitive, enterprises must evolve their data architectures to be more agile, scalable and intelligent, " Noel Yuhanna, vice president and principal analyst at Forrester Research, wrote in a 2025 report. "As these pressures mount, organizations encounter critical challenges that hinder their ability to deliver insights at speed and scale."</p> </section> <section class="section main-article-chapter" data-menu-title="What are the limitations of legacy big data architecture?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What are the limitations of legacy big data architecture?</h2> <p>Making data ready for AI matters. Studies consistently link poor data quality to lost revenue and missed opportunities. Gartner has estimated this issue costs organizations at least $12.9 million annually on average.</p> <p>There are other problems with legacy big data architectures. Another Forrester report published in 2025 said most organizations keep data in separate systems. Much of it is unstructured and lacks essential basics, <a href="https://www.techtarget.com/searchdatamanagement/feature/How-data-lineage-became-a-boardroom-metric">such as metadata, lineage and governance</a>, all of which AI depends on to function.</p> <p>"Without a unified foundation of clean, connected, well-managed data, AI initiatives often remain in the pilot phase and fail to deliver business value at scale," Forrester wrote.</p> <p>These dated big data pipelines were often designed for batch processing, not real‑time AI workloads that pull from many data types and sources. The environments are costly and difficult to manage, weakening data governance and hindering efforts to scale AI projects.</p> <p>In 2025, Gartner projected that through 2026, 60% of AI projects will be abandoned if they aren't supplied with AI-ready data. Legacy big data architecture often struggles to support broad AI deployments or agentic, cross-application workflow automation without substantial upgrades to data quality, governance and real-time integration.</p> <p>Other research pointed to similar challenges. In a 2025 IBM survey of 1,700 CDOs and other senior data and analytics leaders, only 26% said they were confident their organization's data capabilities can support new AI-enabled revenue streams. The researchers found that adopting AI uncovered limits in legacy systems: Data was scattered across tools, common definitions were missing and <a href="https://www.techtarget.com/searchenterpriseai/tip/AI-governance-can-make-or-break-data-monetization">governance relied on outdated policies</a>.</p> <p>The study also showed that AI efforts required additional spending on the underlying data architecture to make it fit for purpose.</p> </section> <section class="section main-article-chapter" data-menu-title="Why modernization is back on the agenda"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why modernization is back on the agenda</h2> <p>The pressure is on enterprise leaders to use AI to launch new services and products, pushing organizations to adapt their big data infrastructure to support those initiatives.</p> <p>"They're looking into what types of data they have and how to put it in a better state to serve the customer better. That's the driving force for modernization for most companies," said Geeta Sandeep Nadella, a senior member of IEEE, an organization of technology professionals that also defines global technical standards.</p> <p>The <a href="https://www.techtarget.com/searchdatacenter/tip/AI-in-the-data-center-Transforming-operations-and-careers">push to upgrade</a> is also driven by uneven adoption across the enterprise, Nadella said. It's common to see a modern data environment in one department or business unit while others -- especially those picked up through mergers or acquisitions -- remain on older systems that now need to be integrated into unified big data architecture.</p> <p>Many data teams modernize to reduce costs, as legacy environments tend to be more expensive to run and maintain. They also seek to simplify the environment and lower exposure to security risks, which are typically higher in aging platforms.</p> <p>Many enterprises also plan to modernize to improve strategic responsiveness, even if they're laggards on AI. Organizations that modernize their big data architecture report stronger returns. In 2020, for example, management consultancy McKinsey found that "high-performing data organizations" were three times more likely to say their data and analytics initiatives had contributed at least 20% to the company's earnings before interest and taxes.<b><u> </u></b></p> <p>"Business agility is something every organization needs to look into these days," Nadella said.</p> <p>What ultimately drives an infrastructure refresh, experts say, is preparing the data layer for AI to ensure that information is available when and where it's needed and that it can be used safely.</p> <p>"AI needs consistent, trusted data to be driving more results. And legacy architectures and platforms haven't kept up with the demands for consistent, trusted real-time data for AI," Yuhanna said in an interview with TechTarget.</p> </section> <section class="section main-article-chapter" data-menu-title="All the layers that form a modern data stack"> <h2 class="section-title"><i class="icon" data-icon="1"></i>All the layers that form a modern data stack</h2> <p>Enterprise big data architecture -- whether modern or legacy -- consists of multiple layers to move and refine data from the source to a usable state. While the labels for these layers vary, modern environments include the following:</p> <ul class="default-list"> <li><b>Data sources</b>. Applications and systems that produce data, such as CRM and ERP systems, logs, sensors, files and connected devices.</li> <li><b>Data ingestion. </b>Batch or streaming processes that move<b> </b>data from sources into the big data platform.</li> <li><b>Storage. </b>On-premises, cloud or hybrid repositories that can store a mix of structured, unstructured and semistructured data -- one of the key elements of big data environments.</li> <li><b>Data processing, integration and transformation.</b> Cleans, validates, standardizes and enriches data to <a href="https://www.techtarget.com/searchdatamanagement/definition/data-transformation">convert it into usable formats through ETL processes or ELT ones</a>, which invert the load and transform steps and are often used in big data systems.</li> <li><b>Data delivery and consumption.</b> Delivers the data for advanced analytics and AI applications, as well as BI, reporting and data visualization uses.</li> </ul> <p>Data pipelines automate the movement of data between layers.</p> <p>Leading organizations design, build and manage data environments with integrated governance, security, privacy and metadata practices, Yuhanna added.</p> <p>Experts say several common technology elements combine for a modern big data architecture.</p> <ul class="default-list"> <li><b>Data mesh. </b>An approach that groups data by function, such as sales and finance, and gives each team ownership of its data, with a level of company-wide governance policies.</li> <li><b>Data fabric.</b> A framework that makes it easy to find and use data across all systems, usually through metadata to automate data discovery, lineage and policy management.</li> <li><b>Data lakes and data lakehouses</b>. Scalable storage for both raw and refined data, with data lakehouses -- which combine elements of data lakes and data warehouses -- becoming an increasingly popular platform.</li> <li><b>Open table formats</b>. Open specifications for storing tables in a data lake or lakehouse so different tools can organize, manage and query these large data sets.</li> <li><b>Vector databases</b>. Systems used to store, manage and search vector embeddings -- the numeric versions of text, images and other unstructured data -- to quickly find close matches in generative AI, machine learning and other applications.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="How medallion architecture clarifies data quality"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How medallion architecture clarifies data quality</h2> <p>Ramsunder said a common way to organize a modern data lakehouse is to <a href="https://www.techtarget.com/searchapparchitecture/tip/Key-considerations-for-data-intensive-architectures">use the medallion architecture,</a> which gives teams a simple layout and a shared understanding of the state of individual data sets. As data moves from one layer to another, its quality and usefulness improve at each step.</p> <p>Medallion architecture collects data into three logical layers:</p> <ul class="default-list"> <li><b>Bronze. </b>This layer holds raw data.</li> <li><b>Silver. </b>This layer<b> </b>cleans and validates data that might be used for small tasks, applying fixes and business rules.</li> <li><b>Gold. </b>This layer publishes curated data that is ready for a wide range of business needs, such as reporting, analytics and machine learning.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="How to future-proof your AI ambitions"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How to future-proof your AI ambitions</h2> <p>As with any IT decision, the starting point in creating an effective big data architecture is business need: design the environment to fit specific needs, then choose the required data technologies.</p> <p>"The real key components depend on what you want to do," Yuhanna said.</p> <p>In practice, many teams prefer cloud services for scale and speed, but some big data workloads still benefit from on‑premises deployments for tighter control or lower latency in specific AI scenarios, Nadella said.</p> <p>To avoid vendor lock-in and extend the life of the big data architecture investments, Yuhanna said organizations <a target="_blank" href="https://www.ibm.com/think/topics/open-standards-vs-open-source-explanation" rel="noopener">should use</a> open standards, modular designs and highly automated offerings. He also recommended a phased approach to modernization so organizations get incremental benefits as they progress.</p> <p>Nadella said it's also important to recognize the work is never done.</p> <p>"It's something that is constantly getting updated," he said. "You have to continually look at yourself and look at the new requirements."</p> <p><i>Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.</i></p> </section> Many enterprises put AI into production in 2025 but found their legacy data stacks stalled progress. See what it takes to modernize big data systems for better AI results. https://cdn.ttgtmedia.com/visuals/searchOracle/data_management_BI/oracle_article_004.jpg https://www.techtarget.com/searchdatamanagement/feature/Building-a-big-data-architecture-Core-components-best-practices Tue, 27 Jan 2026 15:35:00 GMT <p>Snowflake on Tuesday introduced Energy Solutions, a suite of capabilities aimed at making it easier for enterprises in the energy sector to manage data and develop AI applications than when using the vendor's general-purpose platform.</p> <p>In addition to features available to all Snowflake users such as <a href="https://www.techtarget.com/searchdatamanagement/news/366634007/Snowflake-delivers-slew-of-AI-tools-introduces-new-ones">Snowflake Intelligence</a> -- an agent that enables data exploration and analysis using natural langue -- and the vendor's <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">Cortex AI development environment</a>, Energy Solutions includes data and AI governance capabilities, more than 30 partner-developed applications that users can adopt, and industry-specific datasets.</p> <p>Snowflake first launched industry-specific capabilities in 2021 when it released the <a href="https://www.techtarget.com/searchdatamanagement/news/252506699/Snowflake-aims-at-financial-services-with-data-cloud">Financial Services Data Cloud</a>. Since then, it has added eight more industry-specific offerings, including targeted suites for the financial services, manufacturing and technology sectors, among others.</p> <p>Given that packaged capabilities geared toward specific industries make it easier for enterprises to manage their data and develop data-informed applications than general-purpose platforms, Snowflake's Energy Solutions is significant, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"Industry-specific platforms are just better at solving the exact problems a sector faces," he said. "There may be a trend building for AI industry-specific stories. [Energy Solutions] comes with the right tools, integrations and data already built in, so companies don't have to spend as much time customizing things. It's faster, easier, and more effective than trying to make a general-purpose platform fit."</p> <p>Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data management vendor that, like many of its peers, has added AI development capabilities over the past few years. Recently, the vendor <a href="https://www.techtarget.com/searchdatamanagement/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">made Google's Gemini 3 model available</a> in Cortex AI to provide users with a new large language model <a target="_blank" href="https://www.linkedin.com/pulse/how-choose-right-large-language-model-your-business-kgy7f/" rel="noopener">to choose from when building AI tools</a>.</p> <section class="section main-article-chapter" data-menu-title="Energy boost"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Energy boost</h2> <p>As enterprises <a href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind">increasingly invest in developing agents</a> and other AI tools, a strong data foundation is critical. Without discoverable, relevant, high-quality data, AI initiatives are doomed.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Industry-specific platforms are just better at solving the exact problems a sector faces. There may be a trend building for AI industry-specific stories. [Energy Solutions] comes with the right tools, integrations and data already built in, so companies don't have to spend as much time customizing things. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>While Snowflake's general-purpose platform is designed to help organizations effectively manage their data and build data-informed applications, data management and AI development are still complex processes. Industry-specific offerings, while not eliminating complexity, ease some of the difficulties enterprises face when trying to organize billions of data points and develop AI-powered applications that need to be accurate to be trusted.</p> <p>Snowflake is not the only data management vendor to provide targeted versions of their platforms. Rival Databricks similarly provides users in industries such as <a href="https://www.techtarget.com/searchdatamanagement/news/366566672/Databricks-launches-platform-for-the-telecom-industry">telecom</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/252514358/Databricks-extends-data-lakehouse-platform-to-healthcare">healthcare</a> with prepackaged tools geared toward particular sectors. In addition, SAS and SAP are among the vendors offering industry-specific capabilities.</p> <p>While Snowflake has been providing specialized offerings geared toward different sectors for five years, the impetus for developing Energy Solutions came from customer feedback, according to Fred Cohagan, the vendor's global head of energy.</p> <p>"As supply and demand conditions change more frequently and unpredictably, customers tell us they need a secure, governed data foundation that supports real operational workflows -- not just analytics or AI pilots -- so teams can make faster, more reliable decisions," he said. "These new solutions are designed to … create greater value over time as customers expand use cases."</p> <p>Regarding the reason Snowflake chose the energy sector for its latest <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domain-specific-BI-vendors-gain-foothold-with-expertise">targeted offering</a>, the data-intensive nature of the energy industry made it a good candidate, Cohagan continued.</p> <p>"Energy is one of the most data-intensive and mission-critical industries in the world -- it underpins nearly every economy and sector," he said. "There is decades of operational, engineering, and business data spread across systems, which makes energy a natural fit for Snowflake's ability to bring that data together in a governed way so energy companies can apply AI reliably and responsibly at scale."</p> <p>Collectively, the capabilities that comprise Energy Solutions are designed to enable Snowflake customers in the oil and gas, power, and utilities industries to build data foundations by securely connecting data across IT, operational technology and IoT systems. Once integrated and organized, the data foundations can be used to develop trustworthy AI tools, including those aimed at progressing toward more reliable energy solutions and <a target="_blank" href="https://www.bbvacib.com/insights/news/energy-sector-challenges-and-opportunities-in-the-transition-to-sustainability/" rel="noopener">a lower-carbon future</a>.</p> <p>Because industry-specific offerings simplify data management and AI development, Kevin Petrie, an analyst at BARC U.S., like Catanzano lauded their value.</p> <p>"To gain real competitive advantage with AI, organizations must apply advanced models to their proprietary business processes and datasets," he said. "Industry-specific solutions help achieve this by streamlining integration work and enabling AI adopters to get into production faster."</p> <p>Regarding a targeted offering's value to the energy sector, Petrie noted that enterprises in the industry tend to be slower to adopt cutting-edge technologies than those in some other industries. As a result, Energy Solutions will serve as an AI accelerator for Snowflake customers.</p> <p>"The energy sector is not typically an early adopter of new technologies such as AI," Petrie said. "This solution will reduce the level of in-house expertise they need to make AI projects feasible and to reach production."</p> <p>While serving to accelerate AI development in a sector slower than some others to evolve, the individual component of Energy Solutions that will perhaps be most significant toward that end will be its <a href="https://www.techtarget.com/searchdatamanagement/feature/Effective-integration-key-to-creating-trusted-data">consolidation of complex data</a>, according to Catanzano.</p> <p>"The biggest win is how it brings all kinds of data like IT, OT and IoT, into one place and makes it easy to analyze," he said. "That means companies can use AI and advanced tools to make smarter decisions, save money, and improve reliability."</p> <p>Regarding potential differentiation, Catanzano added that, while Energy Solutions is a valuable addition for Snowflake users, other vendors also offer suites targeted at the energy sector. However, the <a href="https://www.techtarget.com/searchdatamanagement/feature/5-data-integration-challenges-and-how-to-overcome-them">data unification capabilities</a> and inclusion of partner-built applications could help Snowflake stand out.</p> <p>"What makes Snowflake different is how it focuses on unifying all the data types and its strong ecosystem of partners that bring extra functionality," Catanzano said. "I haven't seen as much of this with the others."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>As Snowflake makes <a href="https://www.techtarget.com/searchdatamanagement/feature/Customers-pleased-with-Snowflake-plans-for-AI">its product development plans</a>, helping customers reliably operationalize data and AI is the vendor's guiding principle, according to Cohagan.</p> <p>"That includes continued platform innovation and working closely with partners to support real-world operational use cases across industries," he said.</p> <p>Beyond continuing to add more industry-specific offerings and refining those it already provides, Snowflake could serve the needs of current users and potentially attract news ones by providing a broader array of advanced AI tools, according to Catanzano.</p> <p>"Adding more advanced AI tools or tighter integrations with operational systems like SCADA would be smart moves," he said. "Partnering with more startups and industry leaders could also help them stay ahead of the game."</p> <p>Petrie similarly suggested that Snowflake partner with third parties to <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">add AI expertise</a> that can be passed on to customers.</p> <p>"BARC research shows that organizations are looking outside for AI expertise," he said. "In fact, they are more satisfied with the AI contributions of vendors and consultants than they are with their own IT departments. Given this, I'd recommend that Snowflake deepen and extend its partnerships with consulting firms."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Energy Solutions joins the vendor's spate of industry-specific offerings and includes partner-built applications and pre-modeled datasets that accelerate data-driven initiatives. https://cdn.ttgtmedia.com/rms/onlineimages/toolGearArrow_g473747386.jpg https://www.techtarget.com/searchdatamanagement/news/366637773/New-Snowflake-suite-simplifies-data-AI-for-energy-sector Tue, 27 Jan 2026 09:00:00 GMT 60 webmaster@techtarget.com