Security, cost benefits drive the rise of bringing AI to data

Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Tue, 13 Oct 2026 06:26:04 GMT https://www.techtarget.com/searchenterpriseai editor@techtarget.com Pipelines that port data into large language models are going the way of the dinosaur when it comes to AI development. Instead, bringing AI models to data in the secure, stable environments where data is created and stored is proving to be more effective, efficient and safe. AI needs <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">relevant data and business logic</a> -- and lots of it -- to be accurate. In the initial rush to build generative AI tools after OpenAI's November 2022 launch of ChatGPT, developers constructed complex <a href="https://www.techtarget.com/ai/definition/What-is-retrieval-augmented-generation-RAG-in-AI">retrieval-augmented generation</a> (RAG) pipelines to find relevant context, including unstructured data, and deliver it to large language models (LLMs). Those pipelines involved exporting data into <a href="https://www.techtarget.com/data-technologies/feature/Vector-search-now-a-critical-component-of-GenAI-development">specialized vector databases</a>, connecting vector databases to LLMs so the models could discover data through similarity searches, and enabling the LLMs to pull in relevant data to inform their outputs. Amid the excitement, the security risks of moving data to public models and high cost of using LLMs were secondary to the potential business benefits of GenAI, according to Sanjeev Mohan, founder and principal analyst at SanjMo. "The best models were only available as hosted APIs, so data had to travel to them," he told TechTarget. "Data platforms lacked … vector search at the time, and urgency won. Teams wanted GenAI demos fast, and copying data into a vector database was the quickest path. Governance came second." However, a confluence of factors led enterprises to reconsider how they built AI workflows. RAG pipelines were not effectively delivering the relevant context required to make AI tools accurate enough to put into production, the <a href="https://www.techtarget.com/ai/feature/2025-will-be-the-year-of-AI-agents">rise of autonomous agents</a> necessitated not only higher degrees of accuracy but also real-time data, and many data platform providers were building vector database capabilities into their broader data management offerings. Meanwhile, with enterprises getting <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">little return on their AI investments</a>, cost control and security concerns became more prominent. Consequently, expensive and risky ETL pipelines are being phased out in favor of bringing AI to the data instead. "The market evolved in new directions," Kevin Petrie, an analyst at BARC U.S., told TechTarget, citing the evolution of data platforms, growing ability of LLMs to navigate unstructured data without vectorized similarity searches and rising security and regulatory concerns. "All these forces make it more appealing for organizations to bring AI to their data." <section class="section main-article-chapter" data-menu-title="Removing the pipelines, preserving context"> <h2 class="section-title">Removing the pipelines, preserving context</h2> Bringing AI to data means locating capabilities such as model inferencing, vector search and semantic logic within databases and other data management platforms. Critically, by keeping data where it gets created and stored, all of the governance, lineage and business context that gets applied to data in its native environment through tools such as <a href="https://www.techtarget.com/data-technologies/feature/15-top-data-catalog-software-tools-to-consider-using-in-2026">data catalogs</a> and <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic layers</a> remains intact when it is joined with models to build agents and other AI tools. "Bringing AI to data means running AI models and agents where a company's most important information is already stored," Craig Wiley, vice president of AI product at Databricks, told TechTarget. "By operating within a governed environment with existing security and access policies and lineage intact, you remove the need for any data migration or pipelines." By contrast, when data gets copied into models through external pipelines, versions of data need to be created, and governance and business logic get lost along the way, he continued. As a result, developers and engineers need to reconstruct governance as security in each new environment, which is both time-consuming and costly. In addition, Wiley noted that when data, <a href="https://www.techtarget.com/data-technologies/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol servers</a> and AI models are all in different environments with different governance and discovery controls, agents and users accrue <a href="https://www.techtarget.com/it-strategy/feature/Tokenmaxxing-How-CIOs-can-extract-maximum-value-from-AI-tokens">spent tokens</a> and wasted time searching for the right context. "By centralizing discovery, access control and monitoring in a single platform, they accelerate development and reduce time and token costs," he said. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Once companies started asking hard questions about how to deploy AI on their customer data, at scale, without violating their own compliance policies, they realized the 'send your data out' approach wasn't going to work. </figure> <figcaption> Pavan PothukuchiHead of AI platform, Snowflake </figcaption> </div> </blockquote> Databricks is now one of the many data management providers that enables users to bring AI to their data by turning its Unity Catalog a control plane for data, machine learning and AI and <a href="https://www.techtarget.com/data-technologies/news/366631961/Databricks-partners-with-OpenAI-to-boost-AI-development">making models</a> such as OpenAI's GPT-5 natively available in its platform alongside data. Rival Snowflake similarly allows users to bring AI to their data, with Cortex AI serving as a central location and models from Anthropic and Google <a href="https://www.techtarget.com/data-technologies/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">natively integrated</a>. Among others, hyperscalers AWS, Google Cloud and Microsoft enable customers to bring AI to their data, as do platform vendors including Cloudera, MongoDB and Teradata. Snowflake head of AI platform Pavan Pothukuchi noted that a critical advantage of bringing AI to data is that the context AI tools require remains intact, which improves the likelihood of an agent or other AI application <a target="_blank" href="https://www.evidentlyai.com/blog/ai-hallucinations-examples" rel="noopener">avoiding hallucinations</a> and performing as intended. "With this architecture, AI can work directly with a company’s unique business information without requiring them to build a separate data environment for each AI application," he said. The rush to build GenAI tools in 2023 resulted in enterprises first exporting data to vector databases and LLMs, which worked well in demos and experiments, Pothukuchi continued. But when it came time to put the applications into production, these projects no longer met most enterprise standards. "Once companies started asking hard questions about how to deploy AI on their customer data, at scale, without violating their own compliance policies, they realized the 'send your data out' approach wasn't going to work," Pothukuchi said. "Building real, production-grade AI systems on proprietary data requires governance, access control and auditability, which can't easily be replicated in new tools." </section> <section class="section main-article-chapter" data-menu-title="Savings and security"> <h2 class="section-title">Savings and security</h2> While preserving context is one benefit of bringing AI to data, <a href="https://www.techtarget.com/ai/tip/AI-inference-cost-optimization-An-enterprise-guide">lower development costs</a> and improved security are others. Moving data into AI models requires building custom storage, governance and ETL pipelines for every AI application. In addition, it racks up <a href="https://www.techtarget.com/data-technologies/definition/What-is-data-egress-How-it-works-and-how-to-manage-costs">data egress</a> fees and generally requires using platforms from multiple providers. "Multiplying that across the multiple AI tools and model providers that most enterprises use, costs compound fast," Wiley said. But by locating vector search, model inferencing and semantic logic within a unified data management environment, those costs get eliminated, according to William McKnight, president of McKnight Consulting. "Bringing AI to data is significantly less expensive," he told TechTarget. In fact, McKnight noted that benchmark tests conducted by his firm showed that bringing AI to data cut the three-year <a href="https://www.techtarget.com/it-infrastructure/definition/What-is-total-cost-of-ownership-TCO">total cost of ownership</a> of AI tools in half when compared with fragmented pipelines that required data egress and ETL workloads. In addition, testing showed that bringing AI to data accelerated development-to-production timelines by over 300%, reduced development complexity by 67%, lowered maintenance complexity by 38% and cut engineering personnel costs in half. "The cost difference comes down to infrastructure that enterprises don't have to build," Pothukuchi said. "Moving data to AI means standing up pipelines, managing ETL jobs, paying cloud egress fees, and maintaining duplicate storage and access control policies. All of that is ongoing. … When AI runs where the data already sits, those costs disappear." While costs disappear, <a href="https://www.techtarget.com/cybersecurity/tip/Guide-to-AI-data-pipeline-security-and-resilience">security improves</a>. Drastically. "Each time AI models or agents consume data, you must maintain careful security controls such as encryption, masking and identity management to ensure you don't expose personally identifiable information (PII) to unauthorized users," Petrie said. "It is easier to maintain such controls when you bring AI to the data rather than vice versa." In fact, while moving data across systems risks exposure at every step since governance has to be repeatedly re-applied, there is 100% perimeter containment when AI is brought to data in a governed data management environment, according to McKnight. "Extracting proprietary data payloads across network boundaries expands the attack surface, exposes sensitive assets to potential breaches, forces fragmented multi-hop security configurations and compromises strict data sovereignty and regulatory compliance mandates," he said. And just as benchmark testing showed the cost savings of bringing AI to data, tests conducted by McKnight Consulting quantify the security benefits of keeping data in place when developing and managing AI tools. Integrated, in-place platforms scored four times higher in <a href="https://www.techtarget.com/it-infrastructure/DellTechnologiesStorage/Is-your-data-security-strategy-ready-for-AI">data security</a> than those that require data movement across external cloud endpoints, according to McKnight. "Keeping enterprise datasets within native, governed database boundaries with automatic row-level security and encryption directly addresses the governance concerns that … data leaders cite as their primary barrier to AI adoption while eliminating the risks of data leaks, intercepted network payloads and regulatory non-compliance," he said. </section> <section class="section main-article-chapter" data-menu-title="Not the be-all-end-all"> <h2 class="section-title">Not the be-all-end-all</h2> Although bringing AI to data is a substantial improvement over exporting data to AI models, it does not reduce <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">all the problems</a> preventing many enterprises from moving AI pilots into production. Even when contextually relevant data and AI models are natively integrated in a secure data management environment, serving agents and other AI applications with appropriate context is challenging, according to Mohan. He pointed out that AI tools need context from many systems, and even if some of those systems are on one platform, others likely aren't. To enable AI tools to <a href="https://www.techtarget.com/data-technologies/news/366646162/Potential-consequences-are-severe-when-AI-agents-lack-context">access context</a> spread across systems and platforms without moving data, federated access through governed interfaces such as MCP and zero-copy data sharing are required. "The winning architecture is one stack with data as the foundation and AI on top," Mohan said. "The real question isn't where AI runs. It's whether it has trusted context when it does." Pothukuchi similarly noted that while bringing AI to data improves context retrieval, it is imperative that enterprises not only make their data available to AI models but ensure that their data is consistent across all systems so it can be understood by agents and models. If two departments within the same organizations define a word such as "revenue" in different ways, an agent won't know which definition is applicable and will produce <a target="_blank" href="https://www.linkedin.com/pulse/why-ai-still-confidently-wrong-rob-hornby-59sxe/" rel="noopener">confident, but wrong, outputs</a>. "Bringing a model next to their data is incredibly valuable, but proximity isn't enough," Pothukuchi said. "Companies also have to make their business knowledge legible to AI, things like how metrics are defined, what the exceptions are, and which data sources are authoritative for which questions." Meanwhile, an AI development and management system's architecture doesn't matter <a href="https://www.techtarget.com/data-technologies/feature/Successful-AI-deployment-rests-on-strong-data-foundation">if the underlying data</a> isn't correctly prepared, according to McKnight. "Bringing AI directly to data solves the architectural tax, pipeline latency, and cloud egress costs, but bringing AI to dirty or uncurated data introduces a far more dangerous problem," he said. Consequently, human oversight to prevent AI from acting on bad data and <a href="https://www.techtarget.com/data-technologies/opinion/Data-observability-for-AI-helps-curb-poor-model-performance">data observability</a> to ensure data quality are also critical aspects of any AI workflow. "The human-in-the-loop control plane and data observability strategy is important," McKnight said. "As enterprise AI evolves from looking up documents taking autonomous action, purely technical prompt engineering loses its competitive advantage to domain expertise and continuous governance." 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. </section> Natively integrating models where data lives has proven safer, more cost efficient and effective than porting data into LLMs when building agents and other AI tools. https://cdn.ttgtmedia.com/rms/onlineimages/code_g136298313.jpg https://www.techtarget.com/data-technologies/news/366651778/Security-cost-benefits-drive-the-rise-of-bringing-AI-to-data Fri, 09 Oct 2026 08:00:00 GMT Security, cost benefits drive the rise of bringing AI to data Trust -- or lack thereof -- is one of the main reasons many enterprise AI initiatives never make it past the pilot stage and into production. According to a recent report from SAS and IDC, organizations that engender trust in their AI tools are 15 times more likely to get a strong return on their investments in AI development than those unable to build trust in their AI capabilities. SAS is tackling the onerous and intimidating task of building trust in AI via the launch of AI Navigator. The governance suite, made generally available on the Microsoft Marketplace on Oct. 5, is designed to help SAS customers <a href="https://www.techtarget.com/enterprise-software/feature/AI-could-earn-trust-in-transactional-work-first">build trust</a> in their agents and other AI tools. The new platform enables customers to apply all internal policies and <a href="https://www.techtarget.com/it-strategy/news/366646236/How-CIOs-can-navigate-federal-state-AI-regulation-uncertainty">external regulations</a> to AI tools, features a unified view of an enterprise's entire swath of AI capabilities -- including an inventory of all AI assets and where they are being used -- displays where there is governance risk and sends alerts when such risks are uncovered, and provides policy packs from SAS partners so users can get started with prebuilt capabilities. "AI Navigator is meaningful to SAS customers," Michael Ni, an analyst at Constellation Research, told TechTarget. "SAS is giving customers a central way to inventory models and agents, connect them to business use cases and apply policy across a fragmented AI estate. The question is whether SAS can take the next step and connect that governance to decisions and actions AI systems are actually making." However, because AI Navigator is designed to give an overall view of an enterprise's AI estate rather than provide <a href="https://www.techtarget.com/data-technologies/feature/How-agentic-AI-amplifies-data-management-challenges">fine-grained controls</a> over AI applications -- which SAS offers with other tools -- it alone does not distinguish SAS from other AI governance platforms, he added. <section class="section main-article-chapter" data-menu-title="The trust factor"> <h2 class="section-title">The trust factor</h2> When agents and other AI applications <a target="_blank" href="https://hbr.org/sponsored/2024/09/ai-has-a-trust-problem-heres-how-to-fix-it" rel="noopener">aren't trusted</a>, they go unused. Transparency is a significant aspect of trust. When users can see how AI tools go about making decisions, they're able to trust those decisions more than when there is little insight into the machinations behind the AI tool. SAS and IDC found that 97% of users will override AI-generated decisions when they can't see how the decision was made. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> SAS is giving customers a central way to inventory models and agents, connect them to business use cases and apply policy across a fragmented AI estate. The question is whether SAS can take the next step and connect that governance to decisions and actions AI systems are actually making. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> According to ISG analyst David Menninger, 74% of respondents to his firm's research survey reported that governance, security and privacy concerns are preventing them from <a href="https://www.techtarget.com/ai/feature/10-AI-business-use-cases-that-produce-measurable-ROI">getting value from their AI investments</a>. "AI governance continues to be one of the biggest challenges in getting AI deployments into production," Menninger told TechTarget. "Tools such as SAS AI Navigator can help organizations address these issues." SAS built AI Navigator to enable users to govern and view the entirety of their AI estate so they can not only ensure compliance and accuracy but also <a href="https://www.techtarget.com/searchenterpriseai/opinion/Building-governance-for-machine-speed-The-path-to-trusted-AI-autonomy">build trust in their AI capabilities</a>. "We heard … concerns from customers struggling to inventory AI assets, manage shadow AI, ensure AI use satisfied their internal policies as well as emerging regulations, and establish clear accountability across increasingly complex AI ecosystems," Reggie Townsend, SAS's vice president of AI ethics, governance and social impact, told TechTarget. Beyond customer feedback, SAS's experiences developing and managing its own AI tools provided the impetus for creating AI Navigator, he continued. "The idea for SAS AI Navigator started internally," Townsend said. "As we built and deployed more AI ourselves, we realized we needed a way to scale governance of that usage, not just the AI itself." While a means of viewing an entire <a href="https://www.techtarget.com/ai/essentialguide/Enterprise-AI-accelerates-as-calls-for-a-slowdown-grow">enterprise AI</a> ecosystem, AI Navigator complements pre-existing SAS AI governance capabilities rather than replace them, according to Townsend. "SAS AI Navigator is the lightweight AI system of record for executives who need to understand how AI is used inside their organizations, and ultimately if it's generating expected returns in a risk tolerant way," Townsend said. "Think of SAS AI Navigator as the entry point that provides visibility into how AI models and agents affect business use cases." SAS Viya, <a href="https://www.techtarget.com/data-technologies/news/366642499/Latest-SAS-capabilities-focus-on-fostering-reliable-AI">the vendor's primary platform</a> for data management and analytics, includes capabilities for observing and operating AI applications. In addition, SAS offers specialized tools such as Governance Manager, Model Risk Management and Governance and Compliance Manager for intricate governance of the AI workflow. Like Ni, Menninger added that while AI Navigator is beneficial for <a href="https://www.techtarget.com/data-technologies/feature/British-rowing-team-employs-SAS-analytics-to-prep-for-Olympics">SAS users</a>, it is not a competitive differentiator. "Our analysis of AI software providers earlier in the year indicated that, while governance capabilities were improving, many vendors were still lacking in capabilities," he said. "There have been multiple announcements and improvements during the course of the year, including this announcement, which puts SAS on par with other leaders." </section> <section class="section main-article-chapter" data-menu-title="Engineered to engender conviction"> <h2 class="section-title">Engineered to engender conviction</h2> Given that AI Navigator is designed to provide an overview of an enterprise's AI estate rather than <a href="https://www.techtarget.com/ai/tip/The-best-AI-governance-tools-and-platforms-in-2026">intricate AI governance capabilities</a>, it appears appropriately built for its intended purpose, according to Ni. "From what SAS has shown, AI Navigator is logically put together for the job it is trying to do," he said. Of particular value: the AI governance suite enables users to manage <a href="https://www.techtarget.com/ai/feature/10-AI-business-use-cases-that-produce-measurable-ROI">business use cases</a> rather than just models, Ni continued. "AI risk depends on where and how it is used," he said. "SAS then connects that use case to the models and agents behind it, the people responsible for it and the policies that apply." Menninger similarly noted that that AI Navigator is logically constructed to enable enterprises to track and oversee their AI applications. However, what both AI Navigator from SAS and the <a href="https://www.techtarget.com/data-technologies/news/366651267/Governance-teams-now-emphasize-data-AI-enablement">AI governance tools</a> from competing vendors still need to address is how to oversee AI tools built in other vendor's environments. "AI is evolving toward an amalgamation of capabilities across an entire enterprise IT estate involving many different applications," Menninger said. "The next frontier will be building and supporting a broad ecosystem of enterprise applications." 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. </section> AI Navigator is designed to foster widespread AI adoption by providing transparency into agents' actions and a comprehensive view of an enterprise's entire AI network. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a205627811.jpg https://www.techtarget.com/data-technologies/news/366652036/SAS-launches-new-governance-suite-to-bolster-customer-trust-in-AI Thu, 08 Oct 2026 06:51:00 GMT SAS launches new governance suite to bolster customer trust in AI Precisely is taking a two-pronged approach to fostering AI development with the launch of prebuilt AI components and introduction of a unified data management platform. The vendor's AI Studio, made generally available on Tuesday, is a feature set of pre-built agents, applications and prompts designed to save developers some of <a href="https://www.techtarget.com/enterprise-software/news/366619611/Data-training-complexities-make-DIY-AI-agents-difficult">the complex work</a> that makes it difficult to develop AI tools. Meanwhile, the Precisely Platform, now in preview and planned for GA in early 2027, is a data management suite that unifies capabilities such as data integration and governance on a single foundation to provide <a href="https://www.techtarget.com/data-technologies/feature/Successful-AI-deployment-rests-on-strong-data-foundation">a trusted data layer</a> for AI. AI Studio is a collection of assets designed to give developers and engineers a starting point from which to build agents and other AI tools so don't they have to start AI initiatives from a blank slate. Included are tools aimed at helping users improve their data such as those that simplify configuring <a href="https://www.techtarget.com/data-technologies/definition/data-replication">data replication</a> pipelines and launching governance workflows. Others, meanwhile, build on AI-ready data such as preconfigured agents. The Precisely Platform provides a trusted data foundation on which developers can build AI tools. At its own foundation is a single metadata and <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic layer</a> on top of which capabilities such as data governance, integration and quality along with <a href="https://www.techtarget.com/data-technologies/definition/What-is-master-data-management-MDM">master data management</a> are unified so that customers can improve the readiness of their data for AI without having to migrate it into a single location. In addition, the Precisely Platform features Model Context Protocol servers to connect agents with relevant business logic, data quality and governance scores so users can determine whether data is ready for AI, and built-in data enrichment and location intelligence capabilities. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Precisely is differentiating itself through the combination of unified data management with ready-made AI assets rather than just providing one or the other. Precisely's approach of tightly integrating both layers on a single metadata and semantic foundation … is relatively distinctive. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, noted that the new capabilities are significant for Precisely customers because, in tandem, the prebuilt components and data foundation address some of the problems that plague AI experiments. "These offerings enable users to accelerate development from months to days while ensuring data quality, governance, and enrichment are baked into the foundation rather than bolted on afterward," he told TechTarget. Precisely competitors such as <a href="https://www.techtarget.com/data-technologies/news/366645666/Alation-launches-OS-for-building-governing-AI-agents">Alation</a>, <a href="https://www.techtarget.com/data-technologies/news/366580432/Collibra-launches-AI-Governance-unveils-GenAI-capabilities">Collibra</a> and <a href="https://www.techtarget.com/data-technologies/news/366643437/Informatica-update-aims-to-provide-trust-foundation-for-AI">Informatica</a> also provide data quality and governance capabilities. In addition, vendors including DataRobot and H2O.ai offer prebuilt tools for acceleration AI development. Combining the two, however, could distinguish Precisely among data quality and governance vendors, according to Catanzano. "Precisely is differentiating itself through the combination of unified data management with ready-made AI assets rather than just providing one or the other," he said. "Precisely's approach of tightly integrating both layers on a single metadata and semantic foundation … is relatively distinctive." <section class="section main-article-chapter" data-menu-title="Attacking AI development barriers"> <h2 class="section-title">Attacking AI development barriers</h2> Myriad problems <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">prevent AI projects</a> from moving past the pilot stage, among them the complexity of development and lack of <a href="https://www.techtarget.com/ai/tip/The-role-of-trusted-data-in-building-reliable-effective-AI">a properly prepared data foundation</a>. In conversations with its customers, Precisely found that their customers mentioned how complex and time-consuming it can be to build AI tools, and that <a href="https://www.techtarget.com/data-technologies/opinion/Five-pressures-that-break-AI-programs-after-the-pilot">fragmented data</a> is making AI initiatives risky. "[The products] were driven by two consistent themes we heard from customers," Matt Waxman, the vendor's chief product officer, told TechTarget. With customers consistently bringing up development intricacies and data management difficulties as barriers to AI development, AI Studio and the Precisely Platform are central to Precisely's future product development initiatives as well, he continued. "Our focus is helping organizations make their data AI-ready and easier to use across AI, analytics and operations," Waxman said. "That starts with continuing to expand our unified data management capabilities. We also plan to grow the ecosystem of agents, apps and skills … so organizations can accelerate AI adoption." Like Catanzano, Mike Leone, an analyst at Moor Insights & Strategy noted that the new capabilities are valuable for Precisely users. "Especially for companies already running several Precisely products," he told TechTarget. "Those customers can set a rule or policy once and have it applied across everything from data integration to master data. And AI Studio gives their developers agents and apps to start from, built to work with the AI assistants they already use." The prebuilt agents and unified data management capabilities are becoming common among data platform vendors and hyperscale cloud providers, he continued. However, features such as <a href="https://www.techtarget.com/data-technologies/news/252486088/Geospatial-data-a-means-for-deeper-understanding">location intelligence</a> as part of a unified governance platform help Precisely stand apart from its peers. "Precisely stands apart because it sells its own address, property and location data so that a developer can build an agent that pulls in details like a property's flood risk, which a company usually wouldn't have on its own," Leone. </section> <section class="section main-article-chapter" data-menu-title="Correctly constructed capabilities"> <h2 class="section-title">Correctly constructed capabilities</h2> Precisely's combination of prebuilt tools and a unified data foundation appear logically built to help customers have more success as they try to move AI pilots into production, according to Leone. In particular, he noted that the new capabilities seem set up to enable users to operationalize structured data such as customer, address and product records. However, to better serve its customers, Precisely could add capabilities that allow developers to more easily <a href="https://www.techtarget.com/data-technologies/feature/Unstructured-data-needed-but-often-untapped-for-agentic-AI">include unstructured data</a> when serving AI tools. "Ideally, Precisely extends its quality and governance scores to the documents agents read, like contracts and manuals," Leone said. Catanzano likewise noted that while appropriately constructed for their intended purpose, there are <a href="https://www.techtarget.com/it-infrastructure/definition/What-are-ModelOps-model-operations-analytics-models">model operations</a> tools that Precisely could add to AI Studio and the Precisely Platform to make the new capabilities more effective. "To more fully enable users, Precisely could consider adding native model performance tracking, drift detection and automated retraining workflows that close the loop between AI deployment and the data quality issues that emerge in production," he said. "More sophisticated cost optimization tools for managing data operations at scale would also strengthen the offering." 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. </section> The vendor's AI Studio and unified data management suite are designed to simplify development on top of a trusted data foundation https://cdn.ttgtmedia.com/rms/onlineimages/iot_g871472636.jpg https://www.techtarget.com/data-technologies/news/366651681/New-Precisely-capabilities-address-common-AI-development-hurdles Tue, 06 Oct 2026 09:00:00 GMT New Precisely capabilities address common AI development hurdles Golden Analytics is betting on AI-native BI. The upstart business intelligence vendor was founded in April by former Tableau and Amplitude product guru <a href="https://www.techtarget.com/data-technologies/news/252525112/More-automation-embedded-analytics-part-of-Tableau-plans">Francois Ajenstat</a>, who is also the startup's CEO. Its AI-powered platform is still in public beta and early access. But with $21 million to date in venture capital funding, investors including <a href="https://www.techtarget.com/data-technologies/news/252498374/Mark-Nelson-named-new-CEO-of-Tableau">former Tableau CEO Mark Nelson</a> and former Alteryx president George Mathew, and an initial list of close to 20 paying customers that features a Fortune 50 insurance company, general availability is expected in the coming weeks, Ajenstat told TechTarget. Once its platform gets released, Golden Analytics will be entering a BI market dominated by Microsoft's Power BI platform and <a href="https://www.techtarget.com/data-technologies/feature/20-top-business-intelligence-tools-to-know-about-in-2025">entrenched vendors</a> such as Qlik, SAS, Strategy, Tableau and ThoughtSpot. However, a fully AI-powered platform rather than one built for a different era with added-on AI capabilities could establish Golden Analytics as a viable new option and allow it the time it needs to grow to eventually challenge for significant market share, according to David Menninger, an analyst at ISG. "That may be enough," he told TechTarget. "If they can become known as the AI-native analytics company, the next challenge would not be further differentiation but further development of existing capabilities that users have come to expect. That way they would have a larger addressable market." Donald Farmer, founder and principal of TreeHive Strategy, similarly noted that there is potential for <a href="https://www.techtarget.com/data-technologies/news/366649201/What-AI-native-analytics-startups-offer-that-BI-vendors-dont">an AI-native BI vendor</a> to succeed despite the preponderance of established analytics specialists. "[With] too many incumbents … the AI is little more than a side-panel assistant translating text to basic SQL," he told TechTarget. <section class="section main-article-chapter" data-menu-title="The Golden (Analytics) touch"> <h2 class="section-title">The Golden (Analytics) touch</h2> As the broader market attempts to shift toward AI, Golden Analytics is already there. Being AI-native vs. featuring AI-powered capabilities is akin to the difference between being cloud-based and on premises, according to Ajenstat. "When your organization was on premises, you could lift and shift that into the cloud and it would still run, but you weren't taking advantage of all the capabilities of the cloud," he said. Platforms such as Power BI, Qlik, Tableau and ThoughtSpot all feature AI capabilities within their platforms whereas the entire Golden Analytics suite is fueled by AI. Large language models power most functions within its platform, and <a target="_blank" href="https://artificialanalysis.ai/leaderboards/models" rel="noopener">as LLMs get more intelligent</a>, so do the applications it feeds. "It's a different way of approaching it," Ajenstat said. Vendors that pre-date the AI era could theoretically re-architect their platforms to make them AI-native, putting LLMs behind each of their functions to make AI the underlying engine. But over the course of more than a decade in most instances, and many decades in the cases of providers such as SAS and Strategy, longstanding BI vendors have built sprawling platforms featuring a multitude of individual tools. To re-architect each one and do a complete platform overhaul would be a years-long process. "It would require a significant amount of work to re-platform the whole thing," Ajenstat said. "It's not that it isn't possible, but we're talking about years of rethinking, and it is a complete rethink because the way LLMs work is very different. … It would be a massive change. The front end isn't what changes. It's everything about how you manage [the platform] that changes." However, being AI-native won't be an advantage forever, according to Menninger. While Microsoft, Qlik and Tableau -- among others -- may not be able to completely overhaul their platforms, they will eventually build enough AI-native capabilities that being completely AI-native becomes a negligible advantage, much as being <a href="https://www.techtarget.com/data-technologies/tip/7-top-cloud-based-analytics-tools-for-enterprise-use">cloud-native</a> stopped being a competitive edge as previously on-premises platforms were augmented with cloud-native capabilities. "AI-native as competitive advantage will have a half-life of perhaps a couple of years," Menninger said. "Existing providers are all adding AI capabilities, and like we saw with cloud, at some point it will no longer be a differentiator. The question is how much market share Golden Analytics can grab while it has an advantage and how long will that advantage carry over once there is parity in capabilities." Meanwhile, Farmer noted that examples of vendors already adding meaningful AI-native capabilities beyond natural language-based analysis include GoodData and ThoughtSpot, which enable developers to embed <a href="https://www.techtarget.com/data-technologies/tip/How-to-verify-the-value-of-AI-powered-business-analytics">AI-powered analytics</a> within applications. </section> <section class="section main-article-chapter" data-menu-title="Platform innovation"> <h2 class="section-title">Platform innovation</h2> Beyond delivering an AI-native version of a traditional BI platform, Golden Analytics is attempting to distinguish itself by offering innovative capabilities that widen the potential pool of BI users. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The last generation has made it easier, but still, we haven't gotten the adoption that we expected. Now, by making data … smarter by default, we're taking away the mechanics that made data really hard and enabling people to focus on the questions they have that data can answer </figure> <figcaption> Francois AjenstatFounder and CEO, Golden Analytics </figcaption> </div> </blockquote> "The last generation has made it easier, but still, we haven't gotten the adoption that we expected," Ajenstat said. "Now, by making data … smarter by default, we're taking away the mechanics that made data really hard and enabling people to focus on the questions they have that data can answer." With Golden Analytics, users can create dashboards with two clicks, according to Ajenstat. The platform's AI capabilities understand what data is available and build the dashboard on its own. Similarly, users don't have to search for insights. AI is constantly analyzing data and surfaces <a href="https://www.techtarget.com/data-technologies/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">meaningful information</a> for users as soon as they log in. "When people are bolting on AI, sometimes it's glitchy," Ajenstat said. "It doesn't work the way it's expected. It was the same workflow as before but made a little bit easier. What we're trying to do is reimagine the entire workflow to eliminate [previous work] and tell you the other things you could do." One feature that is designed to differentiate Golden Analytics is its slider of autonomy, which is essentially a control setting -- a sliding scale -- that enables human users to determine how much work they want AI to <a href="https://www.techtarget.com/enterprise-software/feature/Industrial-AI-agents-are-not-autonomous-equipment">perform autonomously</a> and how much they want to do themselves. The benefit, according to Ajenstat, is a combination of speed fueled by AI and familiarity fostered by human involvement. "It is telling that [Golden Analytics] calls it the 'slider of autonomy' and not the 'slider of automation,'" Farmer said, noting that the feature reflects insight into how enterprises want to use AI rather than how it's assumed enterprises want to use AI. "Ajenstat understands deeply what the community is looking for." </section> <section class="section main-article-chapter" data-menu-title="Room for new blood"> <h2 class="section-title">Room for new blood</h2> While the <a target="_blank" href="https://www.appsruntheworld.com/top-10-analytics-and-bi-software-vendors-and-market-forecast/" rel="noopener">worldwide market for BI</a> has increased steadily in recent years and is expected to continue growing, the vendors with the largest market share have remained relatively static over the past decade with mainstays Power BI, Qlik, SAS and Tableau among the most popular platforms. Not since ThoughtSpot in 2014 and Domo in 2015 emerged from stealth amid the emergence of new paradigms such as the cloud and <a href="https://www.techtarget.com/data-technologies/tip/8-top-self-service-analytics-tools">self-service analytics</a> provided new opportunities have startup BI specialists made significant inroads in the analytics market. "There was a lot going on in that period," Farmer said. Now, AI's emergence is providing a similar opening for startups to make their mark, according to Menninger. "AI is an industry-wide paradigm shift," Menninger said. "Whenever there is such a fundamental shift, it creates opportunities for new competitors who can move more quickly and more completely to the new paradigm." Beyond a rare opening, Ajenstat's understanding of the data and analytics market and his renown within it may give Golden Analytics a competitive advantage, according to Farmer. In addition, board members and investors such as Nelson and Mathew give the new venture a level of legitimacy. "Ajenstat's product leadership history and connection to the community is almost unparalleled," he said. "Add in those deeply connected colleagues and it's a powerful counterpoint to the marketing power of Microsoft or Salesforce, who can outspend Golden, but have much less credibility with the community." Menninger likewise noted that Ajenstat's experience and the market knowledge of those backing Golden Analytics could <a target="_blank" href="https://www.wilburlabs.com/blueprints/why-startups-fail" rel="noopener">help the startup quickly compete</a>. "The battle for market share will be critical to their success," he said. "Celebrity marketing is certainly helpful because it brings immediate awareness. In this case, the celebrities are directly from the industry and have deep knowledge of what has worked and what has been missing. While it doesn't guarantee success, it sure increases the likelihood." Still, with <a target="_blank" href="https://www.youtube.com/watch?v=j94hs2odgk0&t=24s" rel="noopener">10% of tech startups surviving 10 years</a>, long-term success could be a struggle for Golden Analytics. However, Menninger noted that Golden Analytics does have the required foundation for success. "If you look at those that succeed, they have a unique product or service, strong founders and top-notch investors," he said. "Golden Analytics appears to have all three." 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. </section> The startup, founded by former Tableau executive Francois Ajenstat, aims to compete with established providers with a fully AI-powered platform rather than bolted on AI tools. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20learning_g1186820873.jpg https://www.techtarget.com/data-technologies/news/366651573/Startup-Golden-Analytics-aims-to-shine-with-AI-native-BI Fri, 02 Oct 2026 07:50:00 GMT Startup Golden Analytics aims to shine with AI-native BI A mere eight days after its acquisition by Progress Software closed, Domo on Wednesday launched a platform update highlighted by an updated version of Magic ETL, the vendor's no-code tool for data preparation. The new features in Magic ETL are designed to enable data teams to more efficiently build, monitor and fix <a href="https://www.techtarget.com/searchenterpriseai/tip/Tools-and-techniques-for-optimizing-AI-data-pipelines">data pipelines</a>. In addition, Domo's platform update includes new capabilities in AI Chat, the vendor's <a href="https://www.techtarget.com/data-technologies/feature/AI-in-business-intelligence-How-to-manage-it-effectively">natural language interface</a> for querying data, that allow users to analyze multiple datasets and documents simultaneously. "Trusted AI starts in the pipeline," Devin Pratt, an analyst at IDC, told TechTarget. "This release helps teams see what is happening and fix it before it reaches the dashboard." Capabilities such as AI-powered data analysis and AI-assisted data preparation are now common among data and analytics vendors, he continued. But where Domo is separating itself from competing vendors is by combining AI-powered analysis with <a href="https://www.techtarget.com/data-technologies/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">concrete governance</a> and embedding AI directly into ETL workspaces. "Conversational analytics is table stakes now," Pratt said. "Domo's edge is wrapping it in governance and pushing AI into the ETL canvas itself." Staying the course There are no immediate plans to alter Domo's product development roadmap or change the cadence of its updates following Progress' <a href="https://www.techtarget.com/data-technologies/news/366646124/Domo-acquired-for-400M-sells-to-Progress-Software">$400 million acquisition</a> of Domo's technology, according to Jason Longhurst, Progress' senior director of product marketing. "Providing consistency in delivery and honoring recent commitments is important to Progress," he told TechTarget. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Trusted AI starts in the pipeline. This release helps teams see what is happening and fix it before it reaches the dashboard. </figure> <figcaption> Devin PrattAnalyst, IDC </figcaption> </div> </blockquote> Meanwhile, a combination of customer feedback and the need to keep pace with broader market trends drove Domo to develop the new features included in its latest platform update, Longhurst continued. "Customer feedback was the main motivation for the updates we are making to Magic ETL," he said. Conversely, Longhurst noted that <a href="https://www.techtarget.com/data-technologies/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol</a> (MCP) toolkits that enable authorized AI assistants to perform operational work across Domo's environment -- a feature not yet generally available -- are being developed in response to where the vendor sees the market headed. Domo first launched Magic ETL in 2017 before introducing a no-code version of the <a href="https://www.techtarget.com/data-technologies/feature/How-to-prepare-enterprise-data-for-advanced-AI">data preparation</a> tool in 2021. Now, Magic ETL includes an expanded ability to see and address data pipeline problems, heatmaps that surface jobs with significant runtime effects and versioning to provide <a href="https://www.techtarget.com/it-infrastructure/definition/read-only">read-only</a> access to pipeline configurations so developers can compare current versions of pipelines with earlier ones. In addition, the Magic ETL update features redesigned canvas to improve navigating large workflows, an <a href="https://www.techtarget.com/enterprise-software/tip/How-AI-inferred-sentiment-analysis-unlocks-customer-insights">AI-powered Sentiment Analysis</a> Tile to evaluate text from customer surveys and support tickets, and an AI-powered Classification Tile that automates categorizing unstructured text. "The latest Domo updates deliver several improvements that are individually incremental but add up to significant improvements to the overall user experience for developing and managing data pipelines," Matt Aslett, an analyst at ISG, told TechTarget, adding that the Magic ETL and AI Chat updates represent the vendor delivering some of the capabilities it unveiled during its user conference in March. Beyond new Magic ETL and AI Chat capabilities, among a spate of other new capabilities, Domo's platform update features the MCP toolkits, an MCP trigger in Data Workflows that tracks when an agent or AI assistant begins a process and support for Snowflake Semantic Views so customers can use <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> stored in Snowflake semantic models without having to copy data. "The announcements reinforce that … Domo should be thought of as providing much more than a business intelligence platform, with additional capabilities addressing data preparation and integration, as well as business workflow execution," Aslett said. Regarding whether the platform update includes any hints about Progress' long-term plan for Domo, Pratt noted that the substantive nature of the release hints that Progress is committed to keeping Domo competitive.  "A first release with this much substance is the clearest sign yet that Progress is committed to Domo," Pratt said.  Next for Domo to do As Domo <a href="https://www.mascience.com/interviews/why-is-m-a-integration-so-hard">becomes part</a> of Progress' broader platform, Longhurst identified three areas where Progress could focus: Enabling customers to build data and AI tools on top of trusted data, improving the speed at which customers can act on insights and ensuring that <a href="https://www.techtarget.com/ai/tip/The-best-AI-governance-tools-and-platforms-in-2026">AI is appropriately governed</a>. "Customers are seeking help with making AI more practical and accountable," he said. "You'll continue to see us focus on simplifying data preparation, enabling more intuitive analytics experiences and providing the governance capabilities required to deploy AI at enterprise scale to support their needs." Longhurst also said it's a priority to move MCP toolkits and support for Snowflake's <a href="https://www.techtarget.com/data-technologies/feature/Why-enterprise-AI-depends-on-the-semantic-layer">semantic models</a> into public release. And that is critical, according to Pratt. He noted that while the new Magic ETL and AI Chat capabilities now generally available are significant, the features that might ultimately be the most valuable are the ones that are still in the testing stages. "The best of this release is still in preview," Pratt said. "Getting it into customers' hands is the obvious next move." 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. While the vendor's capabilities are integrated into Progress Software's broader offering, new capabilities demonstrate that Domo remains competitive. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1289411982.jpg https://www.techtarget.com/data-technologies/news/366651502/Magic-ETL-update-marks-first-Domo-platform-release-since-acquisition Wed, 30 Sep 2026 14:28:00 GMT Magic ETL update marks first Domo platform release since acquisition Big data is a critical resource for companies. Using it effectively produces significant business benefits: increased operational efficiency, better insight into customers and market trends, more successful AI applications and more. Analytics has moved beyond traditional approaches based on slowly updated data warehouses containing structured data for BI and reporting. Cloud-scale data platforms <a href="https://www.techtarget.com/data-technologies/feature/Big-data-integration-techniques-and-best-practices-to-adopt">integrate large volumes of diverse data</a> in data lakes or lakehouses, often at high velocity. These big data systems enable advanced analytics and AI applications, including machine learning, <a href="https://www.techtarget.com/data-technologies/opinion/Real-time-data-streaming-for-AI-invest-where-it-matters">real-time analytics</a> and agentic AI. This has transformed analytics from a primarily C-suite function to a <a href="https://www.techtarget.com/data-technologies/feature/How-to-build-an-effective-big-data-strategy">more strategic process</a> that delivers data-driven insights to executives, managers, analysts and frontline workers throughout an organization. Now, <a href="https://www.techtarget.com/ai/news/366650875/Accelerating-AI-adoption-and-governance-amid-an-AI-slowdown">as AI adoption accelerates</a>, companies that invest in big data applications are even better positioned to adapt to business changes, manage risk and gain a competitive advantage over rivals that fail to modernize their data management and analytics capabilities. The 10 use cases described below are common ways companies <a href="https://www.techtarget.com/data-technologies/feature/8-benefits-of-using-big-data-for-businesses">derive business value from big data</a> across different industries. <section class="section main-article-chapter" data-menu-title="1. 360-degree view of customers"> <h2 class="section-title">1. 360-degree view of customers</h2> Organizations commonly generate vast amounts of customer data across multiple channels. Websites, mobile apps, social networks, connected devices, contact centers and in-store systems all contribute to an expanding data trail. By integrating and analyzing this data, companies gain a more complete understanding of customer behavior, preferences, intent and issues, often updated in near real time. They use those insights to boost customer acquisition and engagement through targeted marketing and personalized recommendations, websites and services, all based on individual interests and behavior patterns. Organizations can also spot trends and problems earlier and identify why customers leave, enabling stronger retention strategies. AI amplifies these capabilities by <a href="https://www.techtarget.com/data-technologies/feature/AI-in-business-intelligence-How-to-manage-it-effectively">surfacing complex patterns in datasets</a> and creating optimized promotional content and customer communications. The business payoff of a 360-degree customer view built on big data: increased sales, stronger brand loyalty and higher customer satisfaction. <h3>Industry examples</h3> Amazon, Walmart and other retailers use advanced analytics and AI powered by customer data to personalize marketing and UX, enhance customer service, optimize product offerings and improve website and brick-and-mortar store layouts. Insights into how customers navigate websites and stores, the products they buy, items removed from online shopping carts and product or customer service issues inform the entire retail operation. Streaming services such as Netflix, Disney+ and Amazon Prime Video rely on AI models that analyze behavioral data and dynamically adapt viewing recommendations to drive increased customer engagement. Financial services firms also analyze customer data to personalize product offerings and marketing campaigns, while manufacturers use it to prioritize product development plans. </section> <section class="section main-article-chapter" data-menu-title="2. Demand forecasting and price optimization"> <h2 class="section-title">2. Demand forecasting and price optimization</h2> Companies can detect business patterns and emerging trends early by analyzing data on sales, inventories, supply chains, customer behavior, pricing, promotions and marketing. This helps them improve demand forecasts, optimize pricing and respond more effectively to business changes and market volatility. Additional inputs on economic conditions, seasonal patterns, external events, weather forecasts and competitors further enhance forecasting accuracy and pricing precision. <h3>Industry examples</h3> Retailers and manufacturers use big data to drive more informed decisions as they manage fluctuating demand, pricing pressures and supply chain disruptions. For example: <ul class="default-list"> <li>Walmart <a target="_blank" href="https://tech.walmart.com/content/walmart-global-tech/en_us/blog/post/inside-the-network-powering-walmart.html" rel="noopener">generates</a> about 10 petabytes of data every day from customer visits, purchases and interactions, both in stores and online. The retailer processes the data in real time to enable dynamic inventory management and demand planning and to optimize product selection, pricing and promotions.</li> <li>Amazon runs advanced mathematical models to forecast demand in real time for the hundreds of millions of products sold on its websites worldwide. It uses the forecasts to optimize product purchases from suppliers and determine where to stock products across its hundreds of warehouses to meet delivery promises.</li> <li>Manufacturers use demand forecasts created with big data to develop more accurate production plans. Identifying potential production shortfalls enables them to proactively adjust manufacturing schedules and increase purchases of components and raw materials in time to avoid stockouts and lost business opportunities.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="3. Operational efficiency"> <h2 class="section-title">3. Operational efficiency</h2> Big data plays a central role in efforts to increase operational efficiency. Organizations use it to identify inefficiencies, bottlenecks and redundant processes, enabling them to improve processes, eliminate wasted work and allocate resources more effectively. This can be done faster than with periodic reports: real-time analytics, interactive data visualizations and AI-driven automation empower business managers to respond to operational issues as they arise. <h3>Industry examples</h3> Increasing operational efficiency is a common use case for big data across industries. The following are some notable examples: <ul class="default-list"> <li>Manufacturing. The companies that previously made up General Electric -- now separated into standalone aerospace, energy and healthcare businesses -- analyze sensor and operational data from their manufacturing facilities to optimize production plans, reduce equipment downtime and improve asset utilization.</li> <li>Logistics and transportation. UPS continuously analyzes traffic data, vehicle telemetry, weather conditions and package volumes to optimize routing, which improves delivery times, reduces fuel consumption and lowers operating costs.</li> <li>Retail. Target analyzes point-of-sale data and customer traffic patterns across its 2,000-plus stores to predict business volume at different times of the day, enabling it to optimize staffing levels, reduce checkout wait times and avoid unnecessary labor costs. Amazon uses <a href="https://www.techtarget.com/ai/feature/12-top-machine-learning-use-cases-and-business-applications">machine learning and other advanced analytics</a> on order and warehouse operations data to optimize workflows and pick paths in its fulfillment centers worldwide.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="4. Fraud detection and prevention"> <h2 class="section-title">4. Fraud detection and prevention</h2> Businesses that process high volumes of transactions use big data systems to detect and block fraudulent ones. In addition to the transaction data itself, they analyze device, geolocation, account, identity and customer behavior data to flag potential fraudulent activity in real time. Companies also incorporate data from blocklists, credit bureaus, identity verification services and other external sources. Fraud detection and prevention applications now rely on AI and machine learning models that adapt as fraud patterns evolve, rather than on static rules that become less accurate over time and are hard to manage. Using the models helps reduce fraud rates while minimizing false positives that frustrate legitimate customers. <h3>Industry examples</h3> Financial services firms, payment processors and insurers all do real-time processing and analytics on large, complex datasets to identify and mitigate potential fraud issues in transactions. For example, PayPal has developed machine learning models that use more than 500 data points to evaluate billions of transactions each month. The models dynamically generate fraud risk scores along with recommendations on whether payments should be approved, blocked or reviewed. </section> <section class="section main-article-chapter" data-menu-title="5. Threat detection and response"> <h2 class="section-title">5. Threat detection and response</h2> As organizations expand their IT infrastructure across cloud, hybrid and edge environments, the volume and complexity of security-related data have increased dramatically. Big data platforms enable security teams to gather and analyze streams of telemetry data, including network traffic, endpoint activity, application logs, and identity and access management events. Many organizations also augment internal datasets with external threat intelligence. Advanced analytics and AI models help teams correlate cybersecurity signals across systems to detect anomalies, identify threats and prioritize response efforts based on business risk. <h3>Industry examples</h3> Not surprisingly, threat detection and response is a common big data use case across industries. For example, large financial institutions analyze trillions of security events each year to identify cyberthreats, prevent account takeovers and protect customer data from attackers. Healthcare organizations use big data analytics to protect sensitive patient data and meet regulatory requirements for data security and privacy. </section> <section class="section main-article-chapter" data-menu-title="6. Risk identification and management"> <h2 class="section-title">6. Risk identification and management</h2> Anticipating and proactively responding to <a href="https://www.techtarget.com/it-strategy/feature/13-types-of-business-risks-for-companies-to-manage">rapidly evolving risks</a> is critical to business success. Big data platforms enable organizations to ingest and analyze risk-related information from a wide range of internal and external sources, including operational and supplier data, market signals, weather data, geopolitical intelligence and regulatory updates. By synthesizing these inputs, business and risk management leaders can gain earlier visibility into emerging risks, better quantify potential exposure and model different response scenarios, from risk avoidance or mitigation to acceptance. <h3>Industry examples</h3> Supply chain management and logistics companies are particularly adept at using big data to identify and manage the potential risk of supply shortages or excess inventory due to natural disasters, changes in buying behavior and other factors. Financial services firms use risk models driven by big data to assess credit risk, market exposure and operational vulnerabilities, often across global portfolios. Such models have also proven their value across a range of other industries. </section> <section class="section main-article-chapter" data-menu-title="7. AI-enabled unstructured data analysis"> <h2 class="section-title">7. AI-enabled unstructured data analysis</h2> A significant portion of enterprise data is unstructured -- text, images, video and audio. Despite its potential value, unstructured data is often underutilized -- or not used at all, turning it into dark data. But advances in natural language processing (NLP), computer vision and generative AI (GenAI) make it easier for organizations to extract insights from unstructured data at scale for applications such as analyzing customer sentiment, moderating user-generated content and supporting intelligent search in document repositories. <h3>Industry examples</h3> Since <a href="https://www.techtarget.com/data-technologies/feature/18-top-big-data-tools-and-technologies-to-know-about-in-2026">big data technologies</a> first emerged, online platforms such as Amazon, Google, Meta, Netflix and Shopify have analyzed large volumes of unstructured data to underpin content moderation, targeted advertising, personalized recommendations and UX optimization. Now, enterprises in various industries are applying similar analytics techniques. Analyzing call transcripts, chat sessions and support tickets in contact centers helps financial services firms, telecom companies, healthcare providers and other organizations identify common issues, detect customer frustration and <a href="https://www.techtarget.com/enterprise-software/tip/Customer-interaction-analytics-spurs-better-business-results">improve both service quality and overall CX</a>. In addition, healthcare organizations increasingly use AI-driven analysis of clinical notes, medical images and physician reports to improve medical diagnoses, support clinical decision-making and track population health trends. Financial services firms also apply NLP models to recorded customer calls and written communications to support compliance monitoring and detect potential misconduct. In law firms and other enterprises with vast document repositories, AI-powered search and summarization tools help employees find relevant information faster and reduce manual review efforts. </section> <section class="section main-article-chapter" data-menu-title="8. AI and intelligent automation applications"> <h2 class="section-title">8. AI and intelligent automation applications</h2> Machine learning, automated decision-making, GenAI and agentic AI systems all depend on large, diverse and continuously updated datasets to function effectively. Organizations are increasingly using big data platforms to support AI chatbots and agents that accelerate information retrieval and insight generation, augment human decision-making and streamline complex workflows. Building partly on the ability to access and analyze previously dark data, the AI applications often draw on <a href="https://www.techtarget.com/data-technologies/tip/Integrate-structured-and-unstructured-data-for-better-AI-outputs">both structured and unstructured data</a> from across the enterprise and, in many cases, external sources. <h3>Industry examples</h3> Clearly, this has become a dynamic -- and transformative -- use case across industries. Machine learning applications such as predictive analytics, anomaly detection and pattern recognition have been widely implemented by businesses. Using these foundational capabilities, banks and investment firms are notable examples of organizations that have invested heavily in data platforms to support AI-driven analytics and decision-making. These systems automate credit approval decisions, customer service interactions and detection of anomalous transactions or trading behavior. Insurers similarly use intelligent automation in the underwriting process, including dynamic policy pricing. Manufacturers are also applying AI at scale, using data from sensors and production systems to optimize production processes, improve quality control and automate decision-making in factories. </section> <section class="section main-article-chapter" data-menu-title="9. AI governance and model lifecycle management"> <h2 class="section-title">9. AI governance and model lifecycle management</h2> As AI adoption accelerates, <a href="https://www.techtarget.com/it-strategy/feature/Agentic-AI-in-practice-Lessons-from-real-deployments">deploying AI models and agents</a> is only the beginning. Ensuring that AI systems are trustworthy, explainable and aligned with corporate policies and regulatory requirements is now a big challenge for organizations. Big data platforms play a central role in enabling <a href="https://www.techtarget.com/data-technologies/opinion/How-AI-powered-governance-enables-scalable-AI-deployment">scalable AI governance</a> and model lifecycle management. They provide the foundation for collecting, storing and analyzing AI outputs, audit logs and feedback loops to monitor and control the behavior of AI systems. <h3>Industry examples</h3> Organizations across industries use big data environments to detect bias, fairness issues and unintended outcomes in AI systems and to track model performance, drift and degradation. For example, financial services institutions apply big data analytics to monitor AI models used in credit scoring, fraud detection and risk assessment for accuracy, explainability and regulatory compliance. Healthcare and life sciences organizations do the same to govern AI models used in research, clinical trials and manufacturing. </section> <section class="section main-article-chapter" data-menu-title="10. Predictive maintenance"> <h2 class="section-title">10. Predictive maintenance</h2> Predictive maintenance is one of the most mature and high-ROI big data applications, particularly in asset-intensive industries where unplanned equipment downtime can result in significant financial loss, safety risks and production or service disruptions. Sensor readings, machine logs and other types of operational data can provide early warning signs of equipment degradation or failure, enabling organizations to take preventive action before problems occur. <h3>Industry examples</h3> Along with manufacturers, the energy and transportation industries are particularly susceptible to equipment-related outages and have invested in <a href="https://www.techtarget.com/enterprise-software/tip/5-steps-for-implementing-predictive-maintenance">predictive maintenance systems</a> powered by big data. For example, Shell uses big data analytics and AI to monitor more than 13,000 pieces of equipment globally and detect early indicators of potential failures or performance degradation that could affect its drilling, pipeline and refinery operations. The oil and gas company is now also deploying AI agents to automate root cause analysis and remediation of maintenance issues. Deutsche Bahn, Germany's national railway company, similarly uses AI-driven analytics to anticipate maintenance needs on trains, switches and signaling systems for reduced service disruptions and optimized maintenance scheduling. Ron Schmelzer is the founder of Scalebrate, a platform that provides content, a community and tools to help "microteams" and solopreneurs scale their business. He also hosts the Small Team Big Scale Podcast. </section> Here are 10 ways companies use big data to improve operations, better understand customers, effectively deploy AI systems and gain other business benefits. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1197646065.jpg https://www.techtarget.com/data-technologies/feature/9-big-data-use-cases-for-businesses-and-industry-examples Wed, 30 Sep 2026 11:35:00 GMT 10 common big data use cases for data leaders to enable MongoDB on Tuesday unveiled new capabilities that advance the company's evolution from database specialist to data management and AI platform provider, including a memory and governance layer for AI application and management and an architecture for MongoDB Atlas. Atlas Agent Engine, currently in public preview, is a unified memory and governance layer that is designed to equip users with a single environment for building and managing AI applications, so developers don't have to piece together <a href="https://www.techtarget.com/data-technologies/feature/How-to-build-an-all-purpose-big-data-pipeline-architecture">complex pipelines</a> featuring tools from various vendors. Meanwhile, MongoDB 9.0, now generally available, is the latest version of the vendor's database engine, and Atlas Infinite, now in public preview, is a new architecture for MongoDB Atlas that <a href="https://www.techtarget.com/it-infrastructure/tip/A-primer-on-disaggregated-storage">separates compute and storage</a> to substantially improve performance. With MongoDB 9.0 the database foundation, Atlas Infinite enabling greater scale and Atlas Agent Engine enabling users to deploy agents on top, the new capabilities are logically built to advance MongoDB's evolution, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget. "The new capabilities appear well-constructed to support MongoDB's positioning as an intelligent data platform for the AI era, particularly because they address the full stack from foundational performance through elastic scaling to agent execution and governance," he told TechTarget. However, MongoDB's new capabilities do not include certain key elements of AI-native development and management such as built-in model <a href="https://www.techtarget.com/ai/definition/What-is-fine-tuning-in-machine-learning-and-AI">fine-tuning</a>, automated <a href="https://www.techtarget.com/data-technologies/definition/What-is-feature-engineering">feature engineering</a> or support for emerging AI technologies beyond agents. "Competitors might offer [such capabilities] to differentiate themselves as purpose-built AI data platforms rather than traditional databases adapted for AI workloads," Catanzano said. One day before revealing the new capabilities, MongoDB CEO Chirantan "CJ" Desai stepped down to become chief enterprise platform officer at Meta. Dev Ittycheria, who served as the vendor's president and CEO from 2014 to 2025, was named interim president and CEO. MongoDB's <a target="_blank" href="https://www.google.com/search?q=mongod+stock&sca_esv=6f88a670a7218a83&sxsrf=APpeQnukyqgcFnbhrgPF09BHlzzz67agrQ%3A1790680024197&source=hp&ei=2Ju7aq3vCcj_ptQPue2F4A0&iflsig=ABILxe8AAAAAarup6OIW1UVNH7Lk9q_nCAGSgRkLJeAS&ved=0ahUKEwitu_z10pOXAxXIv4kEHbl2AdwQ4dUDCC4&uact=5&oq=mongod+stock&gs_lp=Egdnd3Mtd2l6Igxtb25nb2Qgc3RvY2syDBAAGIAEGAoYCxixAzIPEAAYgAQYChgLGLEDGIMBMgkQABiABBgKGAsyDBAAGIAEGAoYCxi0BzIJEAAYgAQYChgLMgkQABiABBgKGAsyCRAAGIAEGAoYCzIJEAAYgAQYChgLMgkQABiABBgKGAsyCRAAGIAEGAoYC0i1F1AAWN0TcAB4AJABAJgBXKABmAeqAQIxMrgBA8gBAPgBAZgCDKACuAfCAgQQIxgnwgIKEAAYgAQYigUYQ8ICEBAuGIAEGIoFGEMYxwEY0QPCAgoQLhiABBiKBRhDwgIKECMY8AUYyQIYJ8ICDRAAGIAEGIoFGEMYsQPCAggQLhiABBixA8ICBRAAGIAEwgINEC4YgAQYxwEY0QMYCsICBxAAGIAEGAqYAwCSBwIxMqAH4G6yBwIxMrgHuAfCBwYwLjExLjHIBxOACAE&sclient=gws-wiz" rel="noopener">stock fell</a> over 70 points from Friday's closing price of $410.44 per share to under $340 following the news. From database to data platform MongoDB's evolution echoes a broader industry trend. In addition to MongoDB, Couchbase and Redis have expanded beyond their database roots to become broader platforms for managing data and AI, while platform vendors including Databricks and Snowflake have made AI development and management focal point in addition to managing data. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The new capabilities appear well-constructed to support MongoDB's positioning as an intelligent data platform for the AI era, particularly because they address the full stack from foundational performance through elastic scaling to agent execution and governance. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> However, while remaining in step with the market, and despite widespread competition, MongoDB is somewhat distinguished by its longstanding operational, document-native database capabilities and <a href="https://www.techtarget.com/data-technologies/news/252515720/Gartner-Augmented-analytics-ecosystem-for-BI-now-key">emphasis on interoperability</a> with other systems, according to William McKnight, founder and president of McKnight Consulting. "MongoDB faces immense competition [but] positions itself as an open, operational runtime that consolidates memory, retrieval and governance without introducing architecture sprawl," he told TechTarget. So far in 2026, MongoDB has <a href="https://www.techtarget.com/data-technologies/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">introduced new models</a> to improve data retrieval for agents, <a href="https://www.techtarget.com/data-technologies/news/366642768/MongoDB-adds-new-vector-performance-capabilities-to-aid-AI">improved vector search and storage</a> capabilities, and added numerous tools such as a Model Context Protocol server aimed at aiding connecting operational data with AI development tools. Now, with the launch of MongoDB 9.0 and introductions of Atlas Agent Engine and Atlas Infinite, the vendor aims to enable its users to construct a unified AI workflow. "I've had countless customer conversations, and lately they all come back to same theme," Ben Cefalo, MongoDB's chief product officer of core products, said on Sept. 26 during a virtual press conference. "Customers are building applications that are more demanding than anything we've ever seen before." The new features are designed to work in conjunction with one another to enable users to build, deploy and manage those demanding AI applications, he continued. "We're not bolting AI capabilities onto a legacy system," Cefalo said. "We built out from a database that was designed for this change." Atlas Agent Engine is designed to address some of <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">the problems</a> that prevent enterprises from putting agents into production, including an absence of governance, agents that lack memory capabilities and vendor lock-in to one model or AI framework. New features include data retrieval powered by MongoDB's Voyage AI embedding and reranking models, built-in memory so agents get more accurate over time, <a href="https://www.techtarget.com/it-strategy/feature/The-AI-agent-governance-gap-How-CIOs-can-gain-control">prebuilt governance</a> that oversees every action agents take and an open design that runs on any cloud or self-managed environment. "Moving AI agents from proof-of-concept into production is a major bottleneck in the enterprise. Atlas Agent Engine addresses this by consolidating execution, persistent memory and governance directly into MongoDB Atlas," McKnight said. Of particular value to McKnight: Atlas Agent Engine enables users to deploy enterprise-grade retrieval and state management directly where their operational data already by natively integrating Voyage AI models as well as allowing for flexible data and AI frameworks. He noted that the new capabilities improve MongoDB's architecture for managing the entire data and AI lifecycle by addressing key problems that many enterprises face. However, McKnight added that because MongoDB remains focused on real-time operational execution rather than broader AI lifecycle capabilities such as native model hosting and <a href="https://www.techtarget.com/ai/tip/When-to-use-prompt-engineering-vs-fine-tuning">fine-tuning</a>, it is not yet a full-featured platform for AI. "It remains to be seen if partner integrations with Voyage AI and Frontier Labs meet the needs of the enterprise," McKnight said. MongoDB 9.0 is designed to improve performance so that users can scale AI workloads without sacrificing efficiency. In tests conducted by MongoDB, version 9.0 of its database engine led to faster queries and throughput when compared to the previous iteration of MongoDB's database engine. In addition, the new version features add security measures and <a href="https://www.computerweekly.com/feature/Why-AI-is-forcing-enterprises-to-rethink-observability">observability capabilities</a>. Meanwhile, Atlas Infinite represents a new architecture for the MongoDB platform, further addressing performance by separating compute and storage. In tests conducted by MongoDB, throughput nearly doubled when compared to workloads using Atlas Core, which is the vendor's existing cloud database service. "MongoDB 9.0 and Atlas Infinite are both significant additions, with MongoDB 9.0 delivering substantial performance improvements [and] Atlas Infinite addressing a different but equally important need by providing extreme elasticity that can absorb unpredictable demand spikes in real time," Catanzano said. And though <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">competing vendors</a> could distinguish themselves from MongoDB by including advanced AI-native development and management features, MongoDB is similarly finding ways to stand apart, he continued. "MongoDB appears to be differentiating itself through its open, multi-model approach that avoids vendor lock-in and its focus on unified governance and memory for production agents, whereas many competitors either lock customers into their own AI stack or require more manual integration work," Catanzano said. The road ahead for MongoDB As MongoDB makes product development plans for the remainder of 2026, it could best serve existing users and appeal to potential new ones by adding <a href="https://www.techtarget.com/ai/feature/Key-enterprise-strategies-for-AI-agent-observability">agent observability</a> and evaluation tools to Atlas so developers can trace <a href="https://www.techtarget.com/ai/tip/Managing-drift-in-AI-models-and-data">context drift</a>, according to McKnight. In addition, expanding Atlas' storage and processing capabilities as well as adding new search tools would be beneficial, he continued. "MongoDB needs to expand Atlas's capabilities to store and process a variety of tensors, matrices and scalars for high-dimensionality data, [and] enhancing MongoDB Search with greater search variety such as native spellcheck and search recommendations would strengthen its competitive showing," McKnight said. Catanzano, meanwhile, suggested that MongoDB reduce burdens places on development teams by adding AI-powered optimization capabilities that address <a href="https://www.techtarget.com/it-infrastructure/tip/The-main-types-of-database-testing-explained">database performance</a> and resource allocation based on their organization's specific application patterns. "Additionally, deeper native integrations with emerging AI development paradigms such as automated reasoning systems, multi-agent orchestration frameworks, and real-time model performance monitoring would help MongoDB move from being a platform that supports AI applications to one that actively accelerates AI development," he said. 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. A memory and governance layer and an updated database engine further the vendor's progress toward becoming a platform provider for data and AI while helping it remain competitive. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1019737194.jpg https://www.techtarget.com/data-technologies/news/366651436/Evolving-MongoDB-targets-managing-agents-performance-for-AI Tue, 29 Sep 2026 08:30:00 GMT Evolving MongoDB targets managing agents, performance for AI Microsoft continues to convert Fabric, its combined data management and analytics platform, into a foundation for AI. On Tuesday at the European Microsoft Fabric + SQL Community Conference, a user conference in Barcelona, Microsoft revealed an integration between Fabric IQ -- a <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">context layer</a> within Microsoft IQ that serves AI with consistent <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- and Microsoft Copilot. In addition, Microsoft introduced IQ sharing so users can share governed data and context across their organization and with third parties, and capabilities in Fabric Apps that allow builders to connect their applications directly to Fabric warehouses, SQL databases, lakehouses and Power BI semantic models without duplicating. "Microsoft is turning Fabric from a data and analytics platform into an operating foundation for enterprise AI," Michael Ni, an analyst at Constellation Research, told TechTarget. "Power BI, Fabric IQ, real-time intelligence, agents, applications and observability are increasingly running against the same governed context." It remains to be seen whether Fabric IQ and a new <a href="https://www.techtarget.com/data-technologies/tip/Data-ontologies-are-foundational-for-usable-AI-outputs">ontology layer</a> can truly become an enterprise context layer, he continued. However, if Microsoft can integrate platforms such as Power BI and Copilot into one operating environment, it could help differentiate the tech giant from other platform vendors aiming to provide context layers for AI. "Microsoft is not alone in trying to become the context layer for enterprise AI," Ni said, naming Databricks and Snowflake among Microsoft's competitors. "Microsoft's superpower will be its distribution. It can connect Power BI, OneLake, SQL, Copilot and Fabric into one operating environment building from solutions that … already contain businesses metrics and business definitions." Becoming the Fabric for AI Over the past year, <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">the dominant trend</a> among data management vendors has been facilitating the delivery of contextually relevant business logic to agents. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Microsoft is turning Fabric from a data and analytics platform into an operating foundation for enterprise AI. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> AWS, Databricks, Google Cloud, Snowflake and Teradata have all introduced tools designed to serve agents and other AI applications with context, as have more specialized data management and analytics providers such as Alteryx, Informatica, Tableau and ThoughtSpot. Microsoft is likewise making <a href="https://www.techtarget.com/data-technologies/news/366646162/Potential-consequences-are-severe-when-AI-agents-lack-context">context for AI</a> a key tenet of its data management strategy. "Everything is centered around the concept of context," Amir Netz, Microsoft's chief technology officer for Azure Data, told TechTarget. "Context allows organization to make agents the same way they expect employees to work. That's what makes agents aware of the special sauce every organization has." The nature of the enterprise is changing, he continued. Once entirely human, the workforce is going to become a hybrid mix of humans and agents. Microsoft's aim with features such as Fabric IQ and Copilot are designed to enable <a target="_blank" href="https://www.linkedin.com/pulse/four-key-requirements-building-reliable-agents-quinton-wall-93sdc/" rel="noopener">agents to become as reliable</a> as their human counterparts. "You do that by curating context," Netz said. "Fabric IQ is about providing that dimension of context that explains to the agent what is going on right now in the business. It's almost like virtual reality for agents. We have to take what's happening in the real world, record it and re-create reality for agents." Beyond the integration between Fabric IQ and Copilot, IQ Sharing and new Fabric Apps capabilities, Microsoft introduced Observability in Fabric to better enable visibility into customers' Fabric estates, updates to databases that underpin AI workflows and a data engineering agent, among a spate of other new capabilities. "The biggest thing holding back AI at work is context, meaning how well the AI understands a specific company's business," Mike Leone, an analyst at Moor Insights & Strategy, told TechTarget. "These announcements put the business knowledge companies already built in Power BI and Fabric to work across Copilot, apps and agents." From a competitive standpoint, the individual new features are largely in line with what peers are providing. However, Leone noted that the <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic models</a> that Microsoft customers have been building for years in Power BI give the hyperscaler an advantage when it comes to connecting agents with context. "Most big data platforms are building semantic layers and agents right now, so Microsoft is in step with its peers on direction," he said. "But … Microsoft knows semantics as well as anyone, since they've managed more than 20 million customer semantic models. Another way Microsoft pulls ahead is by connecting Fabric to Copilot. Very few data platforms also own the AI assistant people use at work." Meanwhile, a combination of customer feedback and Microsoft's vision led to the spate of new features being revealed in Barcelona, according to Netz. Customer interactions enabled Microsoft to understand where <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">enterprises were having trouble</a> as they tried to move AI agents into production, while the tech giant's vision translated the feedback into new capabilities. "When you get feedback, what you have to really listen to is what problems are out there, and [figure out] what the customer can't even imagine that we can bring in," Netz said. The future of Fabric's evolution As Microsoft plans product development for the final months of 2026, building a complete platform for <a href="https://www.microsoft.com/en-us/ai/frontier-transformation">its Frontier Transformation strategy</a> -- evolving toward a hybrid human/agent workforce -- is its focus, according to Netz. Toward that end, it will continue to improve the front-end user experience through Microsoft Copilot, the Microsoft IQ suite to provide context, and Microsoft 365 to <a href="https://www.techtarget.com/ai/feature/How-to-ensure-AI-transparency-explainability-and-trust">address trust</a>, security and governance. "It's about helping organizations, end-to-end, on their transformation journey," Netz said. "It's something [every enterprise] has to go through. … We need to help them because AI is not easy." Ni advised Microsoft to push the interoperability of Fabric IQ, making it easier for enterprises to use Fabric IQ to connect data and AI estates spread across multiple platforms. "Making semantics, ontology and governed context easy to consume from third-party tools would make Fabric more valuable to existing customers and lower the barrier for organizations that aren't all-in on Microsoft.," he said. "They seem to be heading in this direction, but to easily be part of an enterprise context layer when Microsoft doesn't own the entire stack would make Fabric much more interesting." Leone, meanwhile, suggested that Microsoft move pieces of Fabric to Azure Local so that customers can better ensure that they remain compliant with AI regulations and <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-navigate-data-sovereignty-for-AI-compliance">data sovereignty</a> rules by using more of Fabric in their own data center. "A lot of companies … [want to] know that if they have to make a move due to a regulation, Microsoft provides an easy path to do so," he said. "They want the option to bring their data and AI in-house without rebuilding anything." 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. An integration between Fabric IQ and Copilot helps turn Fabric into a context layer for agents and demonstrates the interconnectedness of Microsoft's many platforms. https://cdn.ttgtmedia.com/rms/onlineimages/cloud_g1223481405.jpg https://www.techtarget.com/data-technologies/news/366651515/Microsoft-transforming-Fabric-into-AIs-foundational-layer Tue, 29 Sep 2026 02:30:00 GMT Microsoft transforming Fabric into AI's foundational layer Teradata aims to turn its Tera environment into an agentic AI-powered colleague for data management, insight generation and orchestrating agents. First <a href="https://www.techtarget.com/data-technologies/news/366642649/Teradatas-latest-targets-putting-agentic-AI-into-production">introduced in May</a> and made generally available in July, Tera enables users to interact with data and AI agents using natural language. Now, with capabilities including Tera Context Engine to enable agents to call on data across disparate data platforms, Tera Harness to automatically route workflows to the appropriate next step, and Agent Skills that feature task-specific AI tools built on Teradata's years of domain expertise, Teradata is introducing features that advance Tera beyond <a href="https://www.techtarget.com/data-technologies/tip/How-much-does-conversational-analytics-cost-in-2026">a natural-language interface</a>. All are scheduled for general availability by the end of the year. Collectively, the new tools introduced on Tuesday are designed to provide a single workspace for business users and trained experts alike to analyze data, build applications, oversee infrastructures and automate business workflows. As a result, they are valuable additions for Teradata customers, according to Stephen Catanzano, an analyst at Omdia, a division of TechTarget. "The Tera transformation addresses the important enterprise challenge of fragmented tools and skills gaps while introducing vendor-neutral capabilities that work across existing data environments, not just Teradata systems," he told TechTarget. In addition, the task-specific agents and vendor-neutral capabilities that enable users to work with data and AI beyond <a href="https://www.techtarget.com/data-technologies/news/366639802/Teradata-updates-vector-indexing-suite-to-aid-AI-development">Teradata's environment</a> to include competing platforms provide competitive differentiation, Catanzano continued. "Teradata differentiates through its vendor-neutral positioning, [which sets] it apart from cloud platform providers like Snowflake and Databricks whose AI capabilities favor their own ecosystems," he said. "The depth of enterprise data management expertise and prebuilt industry knowledge represents decades of domain expertise that newer entrants cannot easily replicate." <section class="section main-article-chapter" data-menu-title="Firming up Tera"> <h2 class="section-title">Firming up Tera</h2> Many enterprises are <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">still struggling</a> to move AI initiatives into production. Reasons they're often failing in the pilot stage include <a href="https://www.techtarget.com/data-technologies/feature/Successful-AI-deployment-rests-on-strong-data-foundation">insufficient data foundations</a> and inadequate data and AI infrastructures. In addition, data management platforms that don't work well together and a dearth of <a href="https://www.techtarget.com/ai/tip/How-to-build-AI-skills-across-your-workforce">workers with the skills</a> to build and use AI tools contribute to the low success rate of AI projects. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The Tera transformation addresses the important enterprise challenge of fragmented tools and skills gaps while introducing vendor-neutral capabilities that work across existing data environments, not just Teradata systems. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> The new Tera capabilities are designed to help Teradata customers overcome some of these barriers. In particular, they address platforms that aren't interoperable with its vendor-neutral Context Engine and the skills gap with Agent Skills. "I'd describe the earlier Tera as a capable assistant. It could help you do things, but you were still doing most of the driving," Sumeet Arora, Teradata's chief product officer, told TechTarget. "[Now, it] can hold an objective, figure out what needs to happen next, coordinate across different tools and data sources, and carry the work through to a governed outcome without someone babysitting every step." To <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">serve agents with appropriate context</a> from disparate data sources, the Tera Context Engine includes a context graph that connects business logic, neurosymbolic models that combine <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic mapping</a> with Industry Knowledge Models and governance capabilities such as validation controls. Tera Harness, which is built on a Go-native engine and the <a href="https://www.techtarget.com/it-infrastructure/definition/gRPC">gRPC</a> call framework, routes workflows by loading relevant memory and pre-planning work, monitoring progress and recovering from failures rather than abandoning tasks, and applying limits to unproductive iterations so AI tools don't get stuck in a failure loop that drives up costs. Finally, Agent Skills includes a set of Platform Agents for managing the Teradata environment and a different set of Analytics Agents for working with data across platforms, both of which Tera automatically routes to the correct models and tools. Like Catanzano, Devin Pratt, an analyst at IDC, called out the significance of enabling organizations to <a href="https://www.techtarget.com/data-technologies/news/366646162/Potential-consequences-are-severe-when-AI-agents-lack-context">connect agents with context</a> stored across data platforms. "Enterprises already know they can prototype an agent," he told TechTarget, noting that his firm's research shows that fragmented, hard-to-access data is frequently cited as a reason organizations struggle to put agents into production. "What they are paying for is one that survives contact with production, and that means building it where the governed data already lives." Numerous vendors provide the same individual layers Teradata is planning to deliver once the new Tera capabilities are GA, Pratt continued, specifically naming AWS, Databricks, Google Cloud, Microsoft and Snowflake. However, Teradata's <a href="https://www.techtarget.com/it-strategy/opinion/Rethink-the-AI-stack-Why-lean-open-architecture-is-the-answer">open framework for AI</a> is a distinguishing characteristic. "The building blocks are everywhere now," he said. "What is rare is running governed agents directly on top of the mission-critical data that never leaves the building." </section> <section class="section main-article-chapter" data-menu-title="Missing links"> <h2 class="section-title">Missing links</h2> Tera Context, Tera Harness and Agent Skills are logically designed to carry out Teradata's aim of turning Tera from a natural language assistant into a system of tools that can autonomously work alongside business users. However, despite Teradata Harness including <a href="https://www.computerweekly.com/feature/Why-AI-is-forcing-enterprises-to-rethink-observability">observability</a> into how agents complete their work and capabilities that enable agents to recover from failures, Catanzano noted that what's missing is a feature that enables humans to correct and teach Tera. "The three capabilities are well-architected for the agentic coworker vision, but there appears to be a gap in bidirectional learning mechanisms where users can naturally correct and teach Tera when outputs miss the mark or business context shifts," Catanzano said. "A more robust human-in-the-loop framework with contextual learning would strengthen the coworker positioning beyond task execution." Pratt likewise noted that Teradata's three new Tera capabilities logically cover the AI lifecycle and further the vendor's aim of turning Tera into a coworker. Nevertheless, he says, Teradata could more fully deliver on its goal by adding testing, rollback when bugs or errors occur and versioning capabilities. In addition, <a href="https://www.techtarget.com/enterprise-software/feature/The-hidden-cost-of-AI-automation-Preserving-organizational-expertise">more human involvement</a> would be beneficial. "The lifecycle is in the right shape," he said. "The next layer to build out is the control that lets an enterprise trust an agent with real responsibility." </section> <section class="section main-article-chapter" data-menu-title="Up next: more Agent Skills, more customer progress"> <h2 class="section-title">Up next: more Agent Skills, more customer progress</h2> As Teradata plots its next product development initiatives, helping customers move more of their AI pilots into production by providing AI services is a priority, according to Arora. "A huge amount of enterprise AI investment is still sitting in experimentation," he said. "One of our biggest priorities is helping our customers graduate from 'we tried this and it was interesting,' to 'this is running in production and creating measurable value.' That means … helping people identify where governed context will make a difference to a business outcome and getting there faster." In addition, Teradata is focused on adding more Agent Skills to aid organizations that <a href="https://www.techtarget.com/enterprise-software/feature/The-AI-skills-gap-is-getting-worse-Why-and-what-to-do">don't have enough employees</a> with the experience to build and use AI tools, Arora added. "The agent skills we're shipping at GA are a starting point, not a ceiling," he said. Pratt noted that the biggest step Teradata needs to take next is to prove that the new capabilities work as intended. That includes showing how named customers are using Tera to run critical AI workloads. Catanzano, meanwhile, suggested that Teradata expand Tera by adding industry-specific agent skills that accelerate AI adoption and making Tera a means of orchestrating workflows that spread across different <a href="https://www.techtarget.com/ai/feature/AI-engineer-vs-data-scientist-Whats-the-difference">data and AI personas</a> within organizations. "Teradata should expand Tera into cross-functional workflow orchestration that facilitates handoffs between business analysts, data engineers and data scientists who speak different languages and work in different tools," he said. 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. </section> Teradata is introducing features that advance Tera beyond a natural-language interface, including vendor-neutral capabilities that work across existing data environments. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1197646065.jpg https://www.techtarget.com/data-technologies/news/366650846/With-new-context-engine-Teradata-aims-to-be-an-AI-colleague Tue, 22 Sep 2026 12:18:00 GMT With new context engine, Teradata aims to be an AI colleague Meet the streamhouse. Created in response to the demand for AI agents to be served with the context they require to perform properly in production, this portmanteau of <a href="https://www.techtarget.com/data-technologies/opinion/Real-time-data-streaming-for-AI-invest-where-it-matters">streaming data</a> and <a href="https://www.techtarget.com/data-technologies/news/366545117/Lakehouse-architecture-the-best-fit-for-modern-data-needs">data lakehouse</a> builds on the data lakehouse foundation to address AI agents' need for continuous, real-time data. Aiven, Confluent, Redpanda, StreamNative and Ververica recently established the Streamhouse Working Group and introduced an open definition of the new streamhouse architecture and a list of its key attributes. Lakehouse architectures enable users to store and analyze large volumes of historical data. Streamhouse architectures similarly enable access to relevant data. But where streamhouses differ from lakehouses is in the data timeframe: rather than house historical data, streamhouses simplify and standardize serving real-time data spread across myriad systems to agents so they're informed by <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">the fresh context</a> that keeps agentic workflows current and reliable. "Publishing a standardized architecture matters, even if only because it lays down a shared language and a common starting point for the industry," Donald Farmer, founder and principal of TreeHive Strategy, told TechTarget. "The streamhouse definition provides a vendor-neutral blueprint." Kevin Petrie, an analyst at BARC U.S., similarly noted that defining a new architecture -- including naming supporting technologies such as change data capture, open table formats and data catalogs -- is valuable for the data and AI industries. However, he noted that <a target="_blank" href="https://streamhouse.com/definition/" rel="noopener">the working definition</a> of a streamhouse is nascent and specifics such as how the architecture supports other open standards are scarce. "I fully support the concept and believe this working group calls out the right supporting technologies," he told TechTarget. "With that said, the Streamhouse website documentation is pretty sparse so far and would benefit from a more detailed reference architecture based on customer implementations." <section class="section main-article-chapter" data-menu-title="A need for something new"> <h2 class="section-title">A need for something new</h2> Agents are designed to make decisions and execute business processes such as supply chain management and fraud detection as operational events take place. To perform as intended in production, agents require not only high-quality data that can be trusted, but also fresh, governed business context delivered continuously and on a production scale. Without streaming data, they lack <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">real-time context</a> and can miss the triggers they need to carry out business processes as intended. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Publishing a standardized architecture matters, even if only because it lays down a shared language and a common starting point for the industry. The streamhouse definition provides a vendor-neutral blueprint. </figure> <figcaption> Donald FarmerFounder and principal, TreeHive Strategy </figcaption> </div> </blockquote> However, enterprise data is often distributed across a morass of SaaS applications and stored in databases, data warehouses, data lakes and data lakehouses on multiple clouds. As a result, building pipelines that connect data spread across so many systems and delivering it to agents in real-time -- with the appropriate governance -- is too complex for some organizations, according to Farmer. "The ad-hoc plumbing most enterprises had in place just wasn't good enough," he said. A new way of serving agents the <a href="https://www.techtarget.com/data-technologies/feature/6-top-business-benefits-of-real-time-data-analytics">real-time data</a> they require is therefore needed, Farmer continued. "The streamhouse model specifically engineers streams for production environments, adding the necessary transformation and governance that ad-hoc messaging pipelines have not delivered," he said. "It also enables organizations to avoid the friction of consolidating all enterprise data into a single system before use." Petrie pointed out that while streaming data is important for agents, as enterprises race to develop and deploy autonomous AI networks, not enough emphasis has been placed on how agents access real-time data. "Amidst the rush to embrace agents, lakehouses, and documents, I think a lot of organizations overlooked the need to provide their agents with a standard, open method of accessing data on a real-time basis," he said. Streaming architectures such as <a target="_blank" href="https://kafka.apache.org/" rel="noopener">Apache Kafka</a>, which is the technology <a href="https://www.techtarget.com/data-technologies/news/366643312/Latest-from-Confluent-streamlines-use-of-streaming-for-AI">Confluent</a> is built on, offer one way of doing so, Petrie continued. The streamhouse is now another. "Nearly all agents require real-time access to data, and streaming is an ideal way to provide real-time access and processing of tabular or semi-structured logs," Petrie said. Aiven, Confluent, Redpanda, StreamNative and Ververica define a streamhouse as an architecture that enables enterprises to continuously capture, govern and serve <a target="_blank" href="https://mitsloan.mit.edu/ideas-made-to-matter/4-capabilities-a-real-time-business" rel="noopener">the current state of their operations</a> to agents and other applications so they can analyze and act upon the information in real time. Key attributes of the streamhouse architecture include the following: <ul type="disc" class="default-list"> <li>Continuous capture, processing and delivery of data so it is made available in real time as events occur rather than delivered via <a href="https://www.techtarget.com/it-infrastructure/definition/What-is-a-batch-file">batch file</a> processing.</li> <li>Production-grade engineering so that business-critical applications, including agents, and key analyses can depend on continuous delivery.</li> <li>Decentralization so agents can <a href="https://www.techtarget.com/searchdatacenter/opinion/Distributed-computing-The-infrastructure-shift-AI-demands">access data where it lives</a> rather than wait for it to be packaged and consolidated in a single location.</li> </ul> Alex Gallego, co-founder and CEO of <a href="https://www.techtarget.com/data-technologies/news/366640882/Redpanda-launches-streaming-engine-optimized-for-AI">Redpanda</a>, noted that the vendors came together to define the streamhouse after observing customers originate from different starting points to build agent workflows that ultimately looked similar, each featuring real-time data and production-grade engineering that connect decentralized data estates. "The data can't be hours old, and the system can't depend on a batch job catching up before an agent makes a decision," Gallego told TechTarget. "We were all hearing versions of that from customers. When we compared notes, it was clear this … was the market converging on the same problem. That convergence is what made a shared, vendor-neutral definition worth publishing." While the key attributes named by the Streamhouse Working Group are valuable, Farmer noted that more capabilities are also critical to developing an effective pipeline to serve agents with streaming data. In particular, governance, security and interoperability that enable streamhouses to exchange data with lakehouses using open table formats such as <a href="https://iceberg.apache.org/">Apache Iceberg</a> and <a href="https://www.techtarget.com/data-technologies/news/366542953/Databricks-introduces-Delta-Lake-30-to-help-unify-data">Delta Lake</a> are also vital. "If an agent is taking autonomous action, the data feeding it must be strictly audited, lineage-tracked, and access-controlled," Farmer said.  Petrie noted that the Streamhouse Working Group's correctly emphasizes certain governance capabilities. However, like Farmer, he advised the consortium to add more specifics regarding how streamhouses can keep streaming data secure and <a href="https://www.techtarget.com/ai/opinion/How-to-manage-the-gap-between-enterprise-AI-use-and-AI-regulation">compliant with evolving regulations</a>. "The working group rightly names governance as a must-have characteristic, but I think users need more detail about how best to meet specific requirements for identity and access management, personally identifiable information obfuscation, and protection of intellectual property," he said. "They also need more information about how catalogs and meta stores can organize the supporting metadata." </section> <section class="section main-article-chapter" data-menu-title="The streamhouse needs additions"> <h2 class="section-title">The streamhouse needs additions</h2> After publishing the initial definition of a streamhouse, the Streamhouse Working Group plans to maintain the definition and alter it as technology evolves, according to Gallego. In addition, the group is working to improve the standards and practices that allow organizations to build streamhouses using both <a href="https://www.techtarget.com/ai/feature/Open-source-AI-What-it-means-for-enterprise-innovation">open-source</a> and proprietary tooling and opening participation in the Streamhouse Working Group to additional members. "Streamhouse is open by design," Gallego said. "No one company owns the definition, and we welcome anyone building in this space to contribute and be part of it. That's better for customers -- they get choice, competition, and innovation instead of another category controlled by one vendor." Gallego acknowledged that the streamhouse architecture addresses only part of the problem that prevents organizations from putting agents in production. While the architecture provides the data infrastructure, a <a href="https://www.techtarget.com/data-technologies/feature/Why-AI-forces-security-first-governance">governance and security</a> layer is still needed to control what agents access. An additional missing piece that Farmer advised the working group to broach is how streamhouses interface with the memory systems of agents, including <a href="https://www.techtarget.com/data-technologies/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector databases</a> and <a href="https://www.techtarget.com/ai/definition/What-is-retrieval-augmented-generation-RAG-in-AI">retrieval-augmented generation</a> systems, so data is prepared in real time. "Transporting the data is only half the battle," he said. "Formatting and indexing the streaming data so an LLM can query it -- in the milliseconds needed for real-time -- is a critical hurdle they do not address." 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. </section> A group of vendors is joining forces to define an architecture for fueling AI with real-time context to aid enterprises struggling to feed agents the streaming data they need. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a264431831.jpg https://www.techtarget.com/data-technologies/news/366650920/Streamhouse-A-new-way-to-serve-AI-with-real-time-context Tue, 22 Sep 2026 08:00:00 GMT Streamhouse: A new way to serve AI with real-time context The latest tools from Tableau show that the vendor is transforming its platform from a place for traditional BI to one for agentic AI-powered analytics. Tableau's aim is to enable business users to observe, understand and act on data in real time, according to <a href="https://www.techtarget.com/data-technologies/feature/New-Tableau-leader-talks-vendors-evolution-in-era-of-AI">general manager Mark Recher</a>, who spoke during the Tableau Keynote address at parent company Salesforce's annual <a href="https://www.techtarget.com/it-strategy/news/366650263/Salesforce-AI-agent-governance-development-key-themes-at-Dreamforce">Dreamforce user conference</a>. Toward that end, Tableau's bedrocks for enabling enterprises to build and benefit from agentic AI-fueled analytics capabilities include empowering each user to be an application developer, AI-powered analytics that surface and deliver insights to users in their workflows rather than forcing them to visit a BI environment, and enabling users to create <a href="https://www.techtarget.com/data-technologies/feature/8-proactive-steps-to-build-trusted-data-for-analytics-and-AI">trusted data foundations</a> for their data and AI products. New features designed to provide users with those bedrocks include Tableau Studio where analysts can vibe code via natural language to create applications, Proactive Intelligence to deliver insights within user workflows, and Tableau Knowledge to provide <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">a context layer</a> for AI and analytics to draw upon. "They're a logical evolution of the platform," Mike Leone, an analyst at Moor Insights & Strategy, told TechTarget. "Back in May, Tableau introduced its knowledge layer, which keeps a company's business definitions in one place. [Application development] and Proactive Intelligence draw on those definitions." However, rather than distinguish Tableau from competitors such as Microsoft Power BI, Qlik and ThoughtSpot, the new features show that Tableau is undergoing the same repositioning as other analytics specialists <a href="https://www.techtarget.com/data-technologies/news/366645161/Tableau-Qlik-in-flux-What-it-means-for-BI-users-amid-AI-shift">adjusting to the burgeoning era of AI</a>. "Tableau continues to be a leader, but the whole market is moving in this direction," he said. "They're building a lot of the same pieces, from semantic layers and agents to connections into AI assistants. One of Tableau's biggest advantages is Salesforce, which is so deeply embedded in businesses that Tableau can go from spotting a problem to acting on it without switching systems." <section class="section main-article-chapter" data-menu-title="Evolving from BI to AI"> <h2 class="section-title">Evolving from BI to AI</h2> With AI now enabling almost any business user to query and analyze their organization's data using natural language, Tableau and other traditional BI vendors have had to <a href="https://www.techtarget.com/data-technologies/feature/Tableau-in-transition-as-AI-forces-BI-vendors-to-evolve">transform to remain relevant</a> to their customers. All, in their own ways by repurposing existing capabilities and adding new ones, have prioritized providing users with a trusted foundation for analytics and AI as a means of staying essential. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Tableau continues to be a leader, but the whole market is moving in this direction. They're building a lot of the same pieces, from semantic layers and agents to connections into AI assistants. </figure> <figcaption> Mike LeoneAnalyst, Moor Insights & Strategy </figcaption> </div> </blockquote> Tableau is doing so by making its longstanding semantic layering capabilities -- including the data products and business logic organizations have used semantic models to build -- a crucial part of Tableau Knowledge, which it <a href="https://www.techtarget.com/data-technologies/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">first unveiled in May</a>. The additional new capabilities Tableau is touting, all of which are scheduled for general availability by the end of October, are designed to work with the knowledge layer to form a complete agentic analytics platform, according to Recher. "You become an agentic analytics enterprise by taking probabilistic intelligence, and the speed and power of that, and marry that with your trusted data foundation," he said at Dreamforce. Meanwhile, the new features show that the vendor is meeting the needs <a href="https://www.techtarget.com/data-technologies/feature/Baseballs-Rangers-seek-analytics-edge-with-Tableau">its customers</a> as they change by enabling them to transform their analytics operations into an AI-powered workflow, according to William McKnight, president of McKnight Consulting. In addition to Tableau Studio, Proactive Intelligence and Tableau Knowledge, Tableau introduced Data Apps, which extends Studio's application development capabilities to third-party environments such as ChatGPT and Claude. "Tableau's role is expanding from end-user dashboarding into headless, embedded, and conversational analytics infrastructure," McKnight told TechTarget. "The new capabilities mark a significant addition by empowering non-technical users to build micro-applications through natural language while deploying an autonomous 'always-on analyst' that pushes proactive recommendations into operational tools." Like Leone, he added that while Tableau is in line with <a href="https://www.techtarget.com/data-technologies/news/366641671/Latest-Qlik-tools-target-helping-users-achieve-AI-goals">its competition</a> as BI vendors race to build agentic analytics platforms, its integration with Salesforce is one of the ways Tableau is differentiated.  "Tableau is responding to an industry-wide race toward agentic analytics here but brings some uniqueness with its Salesforce ecosystem integration, its installed user base, and allowing external AI models like Claude to safely access governed metrics," McKnight said. <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/mark_recher_at_dreamforce-f.jpg"> <img data-src="https://www.techtarget.com/rms/onlineimages/mark_recher_at_dreamforce-f_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/mark_recher_at_dreamforce-f_mobile.jpg 960w,https://www.techtarget.com/rms/onlineimages/mark_recher_at_dreamforce-f.jpg 1280w" alt="Mark Recher, general manager of Tableau, speaks during parent company's Salesforce's Dreamforce user conference." data-credit="Eric Avidon/Informa TechTarget" height="315" width="560"> <figcaption> Tableau general manager Mark Recher speaks during the Tableau Keynote at Salesforce's annual Dreamforce user conference. </figcaption> <div class="main-article-image-enlarge"> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Context as a critical element"> <h2 class="section-title">Context as a critical element</h2> While Tableau's aim is to "see, understand and act on data at the speed of thought," according to Recher, whether the capabilities highlighted at Dreamforce truly enable insight generation at that rate remains to be seen. When integrated with other <a href="https://www.techtarget.com/enterprise-software/news/366641853/Salesforce-releases-Agentforce-dev-tools-updates-Agent-Fabric">agentic AI capabilities from Salesforce</a>, the new Tableau features do seem appropriately designed to help enterprises generate insights and take actions based on their data, according to David Menninger, an analyst at ISG. "Coupled with other Salesforce agentic capabilities, these Tableau enhancements help enterprises achieve the goal of seeing, understanding and acting on the data," he told TechTarget. "I'll reserve judgment on the speed of thought comment … but for the information that is available, analyses and actions can certainly be accomplished more quickly and more consistently." Toward that end, Tableau Knowledge is perhaps the most significant of the new features, Menninger continued. "Context will be the most critical element to ensure that agents are making the right decisions and taking the right actions," he said. Like Menninger, Leone noted that the features Tableau touted at Dreamforce advance Tableau's aim of <a href="https://www.techtarget.com/data-technologies/feature/Telecom-giant-uses-Tableau-Pulse-to-drive-analytics-adoption">enabling users</a> better to see, understand and act on data. But Studio, which he called the most valuable of the new features, and Data Apps could lead to complications, he cautioned. "The capabilities that Tableau talked about put them in a good spot to deliver on their whole speed-of-thought mission," Leone said. "The bigger issue is that when anyone can build an app in minutes, companies can end up with piles of near duplicates. And then folks are slowing down because they can't tell which app to trust." Enabling <a href="https://www.techtarget.com/it-infrastructure/definition/system-administrator-sysadmin">system administrators</a> to certify the data sources used to build applications in Studio and Data Apps could minimize the potential problem, he added. One early adopter of Tableau's evolving platform is <a href="https://www.computerweekly.com/microscope/news/366649309/CrowdStrike-widens-ecosystem-and-emphasises-AI">CrowdStrike</a>, a cybersecurity provider based in Austin, Texas. Tableau's platform has the technology CrowdStrike has used build the dashboards that enable it to oversee its accounts, according to Jacob Schlan, CrowdStrike's vice president of AI, data and analytics. Now, however, CrowdStrike's aim is to provide its employees with a common, trusted data foundation that they can use to not only analyze data but also build assets. "The trusted data foundation is a non-negotiable," Schlan said. "If you're trying to use raw, uncontextualized data and pointing an LLM or your agent at that, you're never going to be happy with your result." Using Tableau, CrowdStrike created <a href="https://www.techtarget.com/data-technologies/feature/The-CDOs-new-role-is-curating-context-for-data-governance">curated datasets</a> that include a context layer, he continued. "That's been the true unlock for us in getting the correct analytics," Schlan said. "It's governed data, the same every time, that allows us to continue on the self-service analytics path." </section> <section class="section main-article-chapter" data-menu-title="Where Tableau could continue to improve"> <h2 class="section-title">Where Tableau could continue to improve</h2> McKnight suggested that the vendor could further serve the needs of its users and stand out from the competition by adding <a href="https://www.techtarget.com/ai/feature/Agentic-governance-must-go-beyond-traditional-IT-practice">more governance capabilities</a> to <a target="_blank" href="https://www.seekr.com/resource/ai-hallucination-examples/" rel="noopener">control agents in action</a>. "To succeed as an agentic analytics platform, Tableau must build native logic arbitration and collision detection to manage conflicting actions when multiple autonomous AI agents query its Knowledge Layer," he said. "It could also integrate real-time data quality and observability guards to prevent bad data from causing automated operational failures." Menninger, meanwhile, advised Tableau to concentrate on improving the knowledge layer that he called the highlight new feature. Agents need knowledge layers, and <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">the more context</a> such capabilities can help deliver, the better agents will perform in production. "The industry needs a comprehensive knowledge layer if agents are going to succeed broadly," Menninger said. "That requires robust metrics definitions that can be shared and exchanged among all the parts of an enterprise’s information architecture." 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. </section> Proactive BI and a natural language-powered app development tool show progress toward an AI-powered platform, while tight integration with Salesforce provides differentiation. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20leaning_g1150854211.jpg https://www.techtarget.com/data-technologies/news/366650876/Evolving-Tableau-touts-tools-to-fuel-AI-powered-analytics Mon, 21 Sep 2026 14:41:00 GMT Evolving Tableau touts tools to fuel AI-powered analytics There is no AI without a strong data foundation. If agents and other AI tools can't access governed, <a href="https://www.techtarget.com/data-technologies/feature/GenAI-demands-greater-emphasis-on-data-quality">high-quality data</a>, they'll deliver inaccurate, untrustworthy results and AI projects will <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">never make it past the pilot stage</a> and into production. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/mohan.sanjeev.jpg" alt="Sanjeev Mohan, founder and principal analyst at SanjMo">Sanjeev Mohan </div> In his new book titled "Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice," veteran analyst Sanjeev Mohan, who headed Gartner's data and analytics research team before founding SanjMo in 2021, lays out the critical nature of a proper data foundation for AI and how organizations can build that bedrock. AI, he states, is fundamentally just the most modern use case for data. For decades, data in the enterprise was kept under wraps by specialists and largely used to inform business intelligence tools such as reports and dashboards. In recent years, the paradigm has evolved. Now, data is earmarked to <a href="https://www.techtarget.com/data-technologies/feature/8-proactive-steps-to-build-trusted-data-for-analytics-and-AI">underpin agents and other AI tools</a>, and it is becoming the infrastructure for widespread AI-driven analysis and process automation. In the coming years, data will be the foundation for technologies such as robotics and quantum computing. But just as BI without reliable data couldn't be used to inform decisions, AI without a strong data foundation can't be trusted to generate insights and run business processes. In a recent interview with TechTarget, Mohan discussed the essential nature of a strong data foundation for AI, what makes a good data foundation and how AI can play a role in preparing data for AI as well as benefitting from it. In addition, he spoke about <a href="https://www.techtarget.com/data-technologies/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeedhttps:/www.techtarget.com/data-technologies/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">data governance's role</a> in the evolving AI workflow, the barriers that still prevent some enterprises from building a strong data foundation and how data for AI will continue to evolve. Editor's note: This Q&A has been edited for clarity and conciseness. Before we get into the details of what makes a good data foundation for AI, tell us why a strong data foundation is so important for AI. Sanjeev Mohan: When I watch vendors demo new capabilities these days, they all open a natural-language interface, they ask a question, and then an answer magically appears. But that's a fallacy. It makes it look like AI has overpowering capabilities that wash over the cesspool of data that [organizations] have collected. Your AI is only as good as your data, which is <a href="https://www.techtarget.com/it-strategy/feature/What-enterprises-are-getting-wrong-about-AI-data-readiness">easier said than done</a>. Data doesn't have to be pristine [for AI], but you at least have to know what you have. Why don't many organizations know what they have and instead have a cesspool of data? Mohan: Data estates are <a target="_blank" href="https://explodingtopics.com/blog/data-generated-per-day" rel="noopener">exploding at their seams</a>. Most organizations today have over 100 SaaS products. All of them collect data, but they don't share it because there is no mechanism [to connect them], and there are no standards to make it consistent across applications. Instead, there are all these SaaS products with local copies of data -- maybe some gets moved into data warehouses -- leading to too many data siloes. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> I'm a very strong believer that if we can fix the data foundation, we can derive the success from AI that has so far eluded us. </figure> <figcaption> Sanjeev MohanFounder and principal analyst, SanjMo </figcaption> </div> </blockquote> In organizations, sales and marketing should be connected at the hip. Instead, sales has its ecosystem of data, and marketing has its ecosystem of data. As a human consumer in a senior position, if I need to know how marketing is leading to sales, I can connect them. But if AI agents are analyzing data, how in the world will an agent know that 'X' in one system is the same as 'Y' in another system? There are no standards, and the two are coded differently. I'm a very strong believer that if we can fix the data foundation, we can derive the success from AI that has so far eluded us. Fixing the data foundation to get value from AI seems obvious, so what are you experiencing when you talk to organizations about doing so? Mohan: If I talk too much about data, people start shaking their heads and say, 'What are you talking to us about data? Talk to us about AI.' When I tell them that AI is a use case of data, some take offense. To them, AI is a whole, gorgeous, brand-new world. But it's a use of data. Tomorrow it will be robotics, then it will be <a href="https://www.techtarget.com/whatis/definition/quantum-computing">quantum computing</a>, and then it will be something we haven't even thought about. All of these things are sitting on top of a substrate, and that substrate is a data foundation. Before the AI era began a few years ago, what did organizations need to do to build a proper data foundation? Mohan: That process was determined by technology. When computers came in during the 1960s, 70s and 80s, memory was very expensive, so when networks arose, they were very slow. Data foundations evolved from that era. It was almost always structured data,it was all <a href="https://www.techtarget.com/it-infrastructure/definition/What-is-a-batch-file">batch file</a> processing, there was no concept of real time and there were a lot of manual checks with humans in the loop. Automation was something only enabled by the cloud. How has AI changed what the data foundation looks like -- how is it different than it was just a few years ago? Mohan: With AI, there is an amazing ability to detect similarities across any data source. Now, I can say, 'Give me information structured sources such as databases and CRM applications and do sentiment analysis on customer support calls for a given customer.' In one query, AI can join audio transcripts, structured data and <a href="https://www.techtarget.com/enterprise-software/tip/How-AI-inferred-sentiment-analysis-unlocks-customer-insights">sentiment analysis</a>. There is data [that informs] AI, and AI [that helps manage] data. AI plays two roles. It fixes the data foundation, and it helps derive new insights from that data. If you were advising an enterprise, how would you tell them to build a data foundation for AI -- what tools do they need from start to finish to have a proper data and AI stack? Mohan: I tell them to first pick one business process that they're trying to turn into an agent, then lay out the data floor from ingestion, through integration and transformation, to observability and reporting. Once they understand their data process, they can look to see where they can insert AI into the process, such as at the ingestion level. And once they understand the process, they can turn their natural language documentation into a skill. The first skill becomes ingestion so they can instruct their AI to ingest, classify, profile and find anomalies. Others are data integration, data quality, and so on, but start with ingestion and automate it, and have a <a href="https://www.techtarget.com/enterprise-software/feature/The-hidden-cost-of-AI-automation-Preserving-organizational-expertise">human in the loop</a> to check if the data was ingested correctly and whether there were duplicates. In AI, the most important thing is evaluations, and what you're doing in this process is creating relevant context for large language models (LLMs) to act on your behalf. What does an LLM acting on a user's behalf look like? Mohan: The final thing is to be able to tell your application something such as, 'Build me a dashboard for SEC filings,' or, 'Do my taxes.' That combines all the other skills. To do my taxes, first the LLM needs to get my 1099s and W2s -- that's ingestion and integration. Then a human needs to cross-check to make sure it was correct. Ingestion is not an outcome. Integration is not an outcome. These are means to an end. AI should be considered a new paradigm, a new way of doing things. In the past, humans built their own reports but were limited. Now, AI can access more information. Where do capabilities such as data governance enforcement fit in now? Mohan: People ask how to go from data governance to <a href="https://www.techtarget.com/ai/tip/The-best-AI-governance-tools-and-platforms-in-2026">AI governance</a>, and I tell them that there is a problem in how they're asking the question. You're not going from one to the other. You're upscaling from data to AI governance. You still need all the lineage, business glossaries and catalogs at the data level. But now, with AI, everything changes. The idea of data governance was to govern source data and build deterministic applications on top that would always run the same way. AI breaks that thinking because <a target="_blank" href="https://www.youtube.com/watch?v=U8kuVAvam50" rel="noopener">AI is probabilistic</a>. Even with highly governed data, AI can misbehave and provide wrong answers. Now, you have to govern the output. That's why humans need to be in the loop. You have to govern your input, which is data governance. You have to govern your output, which is AI governance. And with agents, you have to govern all the things that are happening in a multi-agent systems. Data governance is still needed, but you have to upskill agents so that before something bad happens, it can be stopped. As you interact with organizations, are you seeing that most now have their data in order for AI, or do many still not have proper data foundations? Mohan: It's getting better. What I mean by that is that when I started my career, all the data was proprietary. SQL was the open thing, but everyone had their own flavor, so it wasn't really open. Today, there are open standards throughout the data stack, such as object store with Apache Parquet files and Apache Iceberg as a table format. Even vendors such as Snowflake with proprietary formats <a href="https://www.techtarget.com/data-technologies/news/366641473/Snowflake-broadens-open-source-embrace-ups-Iceberg-support">are embracing Iceberg</a>. That allows users in Snowflake to directly see what's going on in SAP, which is being published to the outside world in Iceberg. Now, you also have standards for semantic layers with <a href="https://www.techtarget.com/data-technologies/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">the Open Semantic Interchange</a>, for agents with <a href="https://www.techtarget.com/data-technologies/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol</a>. We've broken free of proprietary technologies, and for the first time, we are seeing standards span the entire stack. … The data foundation is a living, breathing organism where cells die and new ones generate. What are some of the biggest hurdles preventing organizations from building a data foundation for AI? Mohan: The hurdles that organizations face are in keeping up with technology in a manner in which their investment is safeguarded, and that is very difficult. For example, 15 years ago, we thought <a href="https://www.techtarget.com/data-technologies/definition/Hadoop">Hadoop</a> was a savior. So, now we ask how to know whether agents will be successful. Right now, they're running haywire. Knowing what to do is a big challenge. A second challenge is how to do it. Everyone is <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">talking about context</a>, but how many are talking about how to build that context? Two people building context might have very different opinions about what context is. When you haven't even agreed upon a definition of context, how can you build it. The idea is great, but we don't know how to do it yet. Looking into the future, how will the data foundation evolve, or do you think that AI's current data requirements will remain somewhat static over the next few years? Mohan: I think the data foundation will evolve tremendously. In my book, I introduce a concept which should be the next version of the extract, transform and load process (ETL). I call it ECL -- extract, context, link. ETL was great for batch jobs, for moving data. But why are we moving data? If you tell AI where the data is, where the context is for a query, let the AI go where the data is. You don't want to move data unnecessarily because <a href="https://www.computerweekly.com/news/366649643/Rising-token-bills-force-rethink-of-AI-cost-management">it's expensive</a>, it opens up security holes and there's latency. What changes is that you need access to contextual metadata, and your sources are [unstructured data]. … And you need the context to be linked in a <a href="https://www.techtarget.com/data-technologies/feature/Use-knowledge-graphs-with-databases-to-uncover-new-insights">knowledge graph</a> or some other way. This is how AI changes the ETL paradigm into ECL where context is created in real time. But there is work to be done. All this tooling still has to be developed. A lot of enterprises are still trying to solve that context layer. Beyond figuring out context, is there anything you want enterprises to be able to do to build a data foundation that still isn't possible? Mohan: I was very hopeful that by now, four years into this, there would be an ability for organizations to train their own models, but we haven't gotten there. And so far, it doesn't look close. There are frontier models that are trained on common data, but enterprise data is locked away. That's why we started doing <a href="https://www.techtarget.com/ai/definition/What-is-retrieval-augmented-generation-RAG-in-AI">retrieval-augmented generation</a> and other experiments. Eventually, every company might have its own model that is constantly refreshed, and that might change things again. 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. Just as BI was built on a bedrock of high-quality, relevant data, AI tools are only as trustworthy as the data that informs them. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a252657224.jpg https://www.techtarget.com/data-technologies/feature/Successful-AI-deployment-rests-on-strong-data-foundation Mon, 21 Sep 2026 10:28:00 GMT Successful AI deployment rests on strong data foundation Once primarily a data catalog provider for fueling analytics, Alation is turning its platform into a base for agentic AI. In July, Alation <a href="https://www.techtarget.com/data-technologies/news/366645666/Alation-launches-OS-for-building-governing-AI-agents">launched its AIOS</a> to provide Alation users with a dedicated environment for building and governing AI tools. It featured Agent Studio for development and AI Governance to keep agents' actions in <a href="https://www.techtarget.com/it-strategy/news/366646236/How-CIOs-can-navigate-federal-state-AI-regulation-uncertainty">compliance with AI regulations</a>. Two months later  during its annual revAlation user conference in Chicago, the vendor introduced <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">contextual data governance</a> features for its Alation Intelligence Operating System (AIOS). New capabilities, among others, include lineage tracing in AI Governance for visibility into the data that guides each agent's actions, native integrations with AI models and semantic layers, and Ontologies to better enable agents to understand an organization's unique characteristics. "The new features start to shift their governance from passive catalog documentation into an active, runtime enforcement system for enterprise data, context and AI agents," William McKnight, president of McKnight Consulting, told TechTarget. However, as the new capabilities become generally available -- most are in early access -- some will be limited in their scope, and humans will still need to be involved to oversee agent interactions with ontologies, McKnight continued. Alation's push toward agentic governance is in line with what <a href="https://www.techtarget.com/data-technologies/feature/15-top-data-catalog-software-tools-to-consider-using-in-2026">peers</a> such as Atlan, Collibra and <a href="https://www.techtarget.com/data-technologies/news/366643437/Informatica-update-aims-to-provide-trust-foundation-for-AI">Informatica</a> are doing rather than a competitive differentiator. "The entire data catalog market is pivoting from passive documentation for humans toward becoming the governed context layer for autonomous AI agents," McKnight said. "While Alation's execution, ecosystem neutrality, and analyst-friendly curation provide strong competitive leverage, several major vendors … are offering capabilities to solve the exact same AI trust gap." <section class="section main-article-chapter" data-menu-title="A governance suite for agents"> <h2 class="section-title">A governance suite for agents</h2> Lack of governance is one of <a target="_blank" href="https://www.growthaccelerationpartners.com/blog/why-agentic-ai-projects-never-make-it-to-production" rel="noopener">the main reasons</a> many AI projects still fail to make it into production. Alation built the AIOS to give customers a way to oversee AI tools so they can be trusted to perform as intended in production rather than produce <a target="_blank" href="https://www.evidentlyai.com/blog/ai-hallucinations-examples" rel="noopener">confident but incorrect answers</a> that can lead to financial and reputational harm, Alation co-founder and CEO Satyen Sangani told TechTarget in July. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The new features start to shift their governance from passive catalog documentation into an active, runtime enforcement system for enterprise data, context and AI agents. </figure> <figcaption> William McKnightPresident, McKnight Consulting </figcaption> </div> </blockquote> The new features are designed to further that aim of engendering trust in agents and other AI applications, according to GT Volpe, Alation's vice president of product management. "Before, Alation could tell you what your data meant and whether it was trustworthy," he told TechTarget. "Now, it can make that meaning executable by agents on any platform, test it against real agent consumption, and trace every agent's risk back to the live state of the data underneath it." Alation's recognition of <a target="_blank" href="https://www.gartner.com/en/articles/genai-project-failure" rel="noopener">what hinders AI initiatives</a> also played a part in developing the new capabilities, Volpe continued. "The direct signal came from the customers furthest along in deploying agents on AIOS, [but] the broader signal was watching where AI programs stall," he said. "It's almost never the model. It's that the business meaning and process required to effectively use the data was never made explicit. ... That told us the problem enterprises actually have is a governance and context problem." Specifically, the new AIOS features include the following: <ul type="disc" class="default-list"> <li>Expanded lineage in AI Governance so users can better understand every agent's regulatory risk, including the data it consumes.</li> <li>Semantic Model Mastering, a tool that ingests <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic models</a> from Databricks and Snowflake, governs them in Alation, and syncs the enriched semantic definitions between the platforms.</li> <li>Native connectors in AI Governance to bring models from AWS, Databricks, Microsoft and Snowflake into a cross-platform registry so customers can connect an agent's compliance standing to the policies and quality of its underlying data.</li> <li>Console, a natural language interface that automatically routs user's requests to AIOS capabilities without forcing them toggle between different environments.</li> <li>Governed Collections, a feature that automatically turns <a href="https://www.techtarget.com/data-technologies/feature/Unstructured-data-needed-but-often-untapped-for-agentic-AI">unstructured data</a> from applications such as SharePoint and Confluent, into governed catalog objects so they can inform agents.</li> <li>Intelligent Feeds to automatically deliver <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> to teams making operating decisions.</li> <li>Guides for agents called Ontologies that enable them to understand and execute business processes.</li> </ul> The new AI Governance and Semantic Model Mastering capabilities are now generally available, while the other capabilities are in early access. Semantic Model Mastering is perhaps the most valuable of the new capabilities because it helps enterprises prevent their <a href="https://www.techtarget.com/data-technologies/feature/Data-governance-metrics-Measure-success-identify-issues">metrics</a> and definitions from "turning into the next enterprise mess" as agentic AI spreads business logic across disparate systems, according to Michael Ni, an analyst at Constellation Research. Meanwhile, Alation's additions to the AIOS are significant for users given that they demonstrate the vendor's shift toward governing actions, he continued. "Alation has made a meaningful market move with their latest release," Ni told TechTarget. "They are messaging their shift from governance as documentation to governance that guides actions. If agents are going to act on enterprise data, they need current definitions, lineage, policy and business context at the moment of use." Collectively, the new features provide <a href="https://www.techtarget.com/data-technologies/tip/9-metadata-management-standards-that-guide-success">a metadata foundation</a> for AI, he added. However, Ni noted that it remains to be seen how completely they enable agents to understand what matters in the moment and what agents are allowed to do at a given instant. Meanwhile, from a competitive standpoint, Alation's additions advance its AIOS but don't represent differentiation, according to Ni. "Alation is not alone in moving toward governed context for agents," he said, naming Databricks, Snowflake and Microsoft as competitors also offering such capabilities. "Alation could differentiate as the neutral layer across them. Semantic Model Mastering is a good example where, instead of creating another semantic layer, Alation is trying to reconcile the ones enterprises already have." McKnight highlighted Ontologies as the most important of Alation's AIOS additions because it helps connect agents with the business logic they need to avoid confidently wrong responses. Meanwhile, though it includes key features, he added that capabilities such as automated LLM evaluation, <a href="https://www.techtarget.com/ai/tip/Managing-drift-in-AI-models-and-data">model drift monitoring</a> and real-time execution guardrails would make AIOS a more thorough foundation for agents. "Evaluating it as a complete agent foundation highlights key operational gaps," McKnight said. "What Alation is delivering here is an essential contextual and regulatory layer, but enterprises must pair it with runtime guardrails, evaluation suites, and orchestration tools to complete their agent stack." </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> As Alation plots its product development plans for the remainder of 2026, its priority is to get Ontologies, Governed Collections, Intelligent Feeds and Console ready for general availability, according to Volpe. Turning Console into <a href="https://www.computerweekly.com/blog/Data-Matters/Get-set-for-interfaces-that-know-what-youre-doing-before-you-do">the interface</a> for AIOS is a focal point. "Console is the biggest change to how people experience Alation since the [data] catalog itself," he said, noting that the intent is to turn Console into a single entry point that interprets a user's intent and takes them to the proper workspace. "The focus is broadening what Console can route to, so the operating system behaves like a single integrated system the same way the intelligent applications built on it do." McKnight suggested that Alation can continue its platform's transition from a passive data catalog to an active data intelligence layer by automating <a href="https://www.techtarget.com/data-technologies/opinion/Data-observability-for-AI-helps-curb-poor-model-performance">data and AI observability</a>, and deepening metadata governance across streaming and unstructured data. In addition, he noted that Alation has room to improve the AI capabilities within its platform as well as augment its querying capabilities with <a href="https://www.techtarget.com/data-technologies/feature/Improve-data-visualizations-become-a-better-storyteller">visualizations</a> that enable users to better understand their data. "It can elevate its AI capabilities by embedding native reasoning models that explain logic alongside agents that automatically resolve pipeline issues, [while] enriching its core querying features with dynamic, accompanying visualizations will give users deeper, immediate insights into their underlying data assets," he said. Ni, meanwhile, advised Alation to use the knowledge it has regarding which agents get used and what <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">sources they call upon</a> to provide more comprehensive direction for agents. "Alation can build from knowing which data gets used, which sources tend to be used together, and who are the experts," he said. "What I expect next from Alation is using those signals to provide dynamic guidance to agents beyond just the right data, but also what matters now and which policies apply given a specific intent and situation." 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. </section> Lineage tracing in AI Governance and governed access to unstructured data advance the platform and keep the vendor in step with what competitors are offering. https://cdn.ttgtmedia.com/rms/onlineimages/folder-files13.jpg https://www.techtarget.com/data-technologies/news/366650554/New-features-position-Alations-AIOS-as-AI-management-layer Thu, 17 Sep 2026 09:30:00 GMT New features position Alation's AIOS as AI management layer Graph database specialist Neo4j is upping its fight against financial crime. Just over one month after completing the acquisition of GraphAware, which was first <a href="https://www.techtarget.com/data-technologies/news/366643806/Neo4js-GraphAware-acquisition-targets-new-customer-segment">agreed upon in June</a>, Neo4j launched a new suite for detecting and stopping fraud that includes capabilities it inherited through the purchase. Neo4j GraphAware Financial Crime Intelligence is a <a href="https://www.techtarget.com/data-technologies/news/366593101/Graph-technology-helps-battle-election-misinformation">graph-native</a> tool featuring a graph-based knowledge layer purpose-built for banks and insurance companies to use AI to detect and prevent financial crime. Neo4j has provided fraud detection capabilities since launching its first graph database in 2010. However, after detecting potential incidents, customers had to carry out their investigations and analyses using other platforms. GraphAware Financial Crime Intelligence, which combines Neo4j's existing <a href="https://www.techtarget.com/data-technologies/news/252507769/Gartner-predicts-exponential-growth-of-graph-technology">graph-based</a> fraud detection capabilities with complementary capabilities from GraphAware, encompasses the entire financial crime investigation cycle from detection through alerting and investigation to decision. "Detection was only ever half the job," Devin Pratt, an analyst at IDC, told TechTarget. "Putting the full investigation on one graph-native stack removes the handoffs -- and the risk -- that live in the gaps between systems." Stephen Catanzano, an analyst at Omdia, a division of TechTarget, likewise noted that the significance of Financial Crime Intelligence is that it expands on Neo4j's <a href="https://www.techtarget.com/cybersecurity/feature/Why-companies-should-use-AI-for-fraud-management-detection">fraud detection capabilities</a>.  "What makes it particularly valuable is the addition of a comprehensive workflow … underpinned by a reusable knowledge layer for trustworthy AI that enables explainable decision-making throughout the entire detection, investigation and prevention process," he told TechTarget. <section class="section main-article-chapter" data-menu-title="A complete crime-fighting lifecycle"> <h2 class="section-title">A complete crime-fighting lifecycle</h2> Neo4j is not the only graph database specialist to offer fraud detection capabilities. TigerGraph, PuppyGraph and Memgraph do as well. In addition, broader-based data management vendors such as AWS and Oracle provide fraud detection tools as part of their database offerings. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Detection was only ever half the job. Putting the full investigation on one graph-native stack removes the handoffs -- and the risk -- that live in the gaps between systems. </figure> <figcaption> Devin PrattAnalyst, IDC </figcaption> </div> </blockquote> However, Catanzano noted that Neo4j is going beyond what most <a href="https://www.computerweekly.com/news/252524802/How-graph-technology-is-making-a-dent-in-the-database-market">competing vendors</a> offer by combining fraud detection with investigative and decision-making capabilities in a single feature. "While other vendors may offer fraud detection tools, Neo4j's approach of combining graph-native storage with a reusable knowledge layer that continuously enriches context and enables multi-hop reasoning across the entire investigation workflow positions it distinctively in the market," he said. Pratt similarly stated that Neo4j's differentiation lies in the depth and completeness of Financial Crime Intelligence. In particular, he highlighted the inclusion of an explainable <a href="https://www.techtarget.com/data-technologies/tip/5-knowledge-graph-use-cases-in-data-fabric-architecture">knowledge layer</a> that provides a map of connections between data points and shows how and why the tool's AI capabilities deemed something suspicious and made the recommendations it did. "Plenty of vendors do graph, or do fraud," Pratt said. "Neo4j's bet is owning the whole workflow, with explainability built in." While acquiring GraphAware enabled Neo4j to build a complete financial crimes detection suite, the vendor's impetus for developing Financial Crime Intelligence was also a response to the growing need for customers to show they are <a target="_blank" href="https://www.aciworldwide.com/blog/2026-fraud-trends-banks-must-prepare-for" rel="noopener">taking appropriate steps to fight fraud</a>, according to Michael Down, Neo4j's global head of financial solutions. "With the acquisition, we have the investigation and knowledge layer capabilities to package a … decision workflow, rather than requiring institutions to build it themselves," he told TechTarget. "The timing reflects both the acquisition coming together and a market under real pressure, with regulators pushing harder on institutions to prove proactive prevention, not just after-the-fact detection." </section> <section class="section main-article-chapter" data-menu-title="Yearend plans"> <h2 class="section-title">Yearend plans</h2> Neo4j's product development plans over the final months of 2026 include expanding and improving its knowledge layer for AI, adding agentic AI capabilities to its platform to aid users, and enabling customers to remain compliant with <a href="https://www.techtarget.com/data-technologies/feature/Data-sovereignty-expands-beyond-compliance-boundaries">data sovereignty</a> regulations by enabling them to control where and how their data models run, according to Down. "Financial Crime Intelligence is an early proof point of this approach [to data sovereignty], applying what we built with GraphAware to a specific vertical," he said. "We expect the same pattern to extend into other industries over time." Turning its knowledge layer into a foundation for other industry-specific capabilities is a wise strategy, according to Pratt, who noted that financial services is not the only vertical that benefits from <a href="https://www.techtarget.com/ai/feature/How-to-ensure-AI-transparency-explainability-and-trust">explainable decisions</a> based on connected data. "Build the knowledge layer once for fraud, and the payback comes from every regulated decision it can serve next," he said. Catanzano, meanwhile, suggested that Neo4j could improve its fraud detection and financial crime fighting prowess by adding AI capabilities such as predictive risk scoring and investigation recommendations to help analysts battle growing <a target="_blank" href="https://kpmg.com/ca/en/insights/2026/03/fraud-in-the-age-of-ai.html" rel="noopener">AI-powered fraud tactics</a>. In addition, like Pratt, he advised Neo4j to expand crime detection capabilities beyond financial services. "Neo4j might extend the platform to regulated industries facing similar networked crime challenges, such as healthcare fraud or supply chain integrity, thereby appealing to new market segments while deepening its position as the go-to graph intelligence platform for complex, relationship-driven risk detection across enterprises," he said. 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. </section> The vendor is adding tooling from its GraphAware acquisition to go beyond fraud detection and differentiate itself from those providing only part of the crime-stopping ecosystem. https://cdn.ttgtmedia.com/rms/onlineimages/security_a283199860.jpg https://www.techtarget.com/data-technologies/news/366649996/Neo4j-intros-graph-based-suite-for-fighting-financial-crime Wed, 16 Sep 2026 03:00:00 GMT Neo4j intros graph-based suite for fighting financial crime The latest features from Alteryx advance its transformation from a data preparation specialist toward becoming a connective layer between enterprise data and AI tools. Unveiled on Sept. 9, the new capabilities include <a href="https://www.techtarget.com/data-technologies/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">a Model Context Protocol (MCP) server</a> and studio for transforming trusted datasets into agents. Collectively, they are designed to link AI applications with the information, including enterprise rules and previous analyses, that gives AI tools <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">the context</a> to perform properly. As a result, as enterprises <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">increasingly try to move past experimenting</a> with agents and put AI tools into production, Alteryx's new capabilities are valuable for the vendor's users, according to Stephen Catanzano, an analyst at Omdia, a division of TechTarget. "These capabilities represent strong additions because they shift Alteryx … to becoming connective infrastructure between AI agents and enterprise business logic," he told TechTarget. Of particular importance is that the new features don't require Alteryx customers to create a new context layer each time they build an AI application, <a href="https://www.techtarget.com/enterprise-software/feature/AI-feature-spend-is-the-new-software-cost-control-problem">saving them both cost and effort</a>, Catanzano continued. "They [address] the practical challenge of organizations recreating the same business rules across multiple AI implementations while maintaining security, auditability and cost efficiency," he said. <section class="section main-article-chapter" data-menu-title="Changing with the times"> <h2 class="section-title">Changing with the times</h2> Before the AI era, enterprises often relied on specialists to build their data workflows, including vendors such as Alteryx for <a href="https://www.techtarget.com/data-technologies/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a>, Fivetran and Informatica for <a href="https://www.techtarget.com/data-technologies/feature/8-data-integration-challenges-and-how-to-overcome-them">data integration</a>, and Qlik and Tableau for data analysis. Now, AI tools can perform many data and analytics tasks on their own, reducing the need for specialized platforms. To remain relevant to existing customers and attempt to appeal to potential new ones, many data and analytics providers are evolving. For example, longtime self-service analytics specialists <a href="https://www.techtarget.com/data-technologies/news/366640059/GoodDatas-Context-Management-aims-to-make-AI-trustworthy">GoodData</a> and <a href="https://www.techtarget.com/data-technologies/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">Tableau</a> are re-positioning themselves as underlying layers for AI. Additionally, whether database providers such as <a href="https://www.techtarget.com/data-technologies/news/366649199/MongoDB-intros-latest-features-to-fuel-AI-development">MongoDB</a>, data platform vendors like <a href="https://www.techtarget.com/data-technologies/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">Databricks</a> and <a href="https://www.techtarget.com/data-technologies/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake</a>, or governance layers including <a href="https://www.techtarget.com/data-technologies/news/366645666/Alation-launches-OS-for-building-governing-AI-agents">Alation</a> and <a href="https://www.techtarget.com/data-technologies/news/366580432/Collibra-launches-AI-Governance-unveils-GenAI-capabilities">Collibra</a>, AI has forced data technology vendors to adjust. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> These capabilities represent strong additions because they shift Alteryx … to becoming connective infrastructure between AI agents and enterprise business logic. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> Alteryx likewise doing so with its latest capabilities. David Menninger, an analyst at ISG, noted that enterprises and software vendors are attempting to figure out the best mix of deterministic processing that relies on fixed rules and probabilistic processing that uses statistical patterns when building AI tools. For agents that execute tasks such as data engineering and analysis, deterministic processing, which results in a consistent output when using the same input, is generally preferred. "This is where Alteryx has earned its bread and butter," Menninger told TechTarget. "The new Alteryx features provide the glue between the data and analytics processes it manages and the AI agents enterprises are deploying to help with automation and enhanced decision making." Specifically, Alteryx's latest new capabilities include the following: <ul type="disc" class="default-list"> <li>Agent Studio, an environment where business users can transform existing, governed datasets into agents they can query using natural language.</li> <li>Alteryx MCP Server, a governed connection based on <a target="_blank" href="https://modelcontextprotocol.io/docs/2026-07-28/getting-started/intro" rel="noopener">the MCP open standard</a> that enables AI tools to interact with Alteryx to discover and operationalize relevant data.</li> <li>An updated version of Ask Alteryx that turns the conversational tool from an embedded assistant into the main interface for <a href="https://www.techtarget.com/data-technologies/news/366623973/Alteryx-One-launch-aims-to-unify-simplify-vendors-platform">the vendor's Alteryx One platform</a>.</li> <li>Alteryx Insights for OpenAI, an integration that enables users to derive insights in ChatGPT based on proprietary data and business logic previously approved by their organization's analysts. Similar integrations with Claude, Gemini, Slack and Microsoft Teams are being developed, according to Alteryx.</li> <li>Alteryx Skills, a <a href="https://www.techtarget.com/it-infrastructure/definition/GitHub">GitHub</a> integration that teaches agentic interfaces such as OpenAI Codex and Claude Code to develop Alteryx assets similar to the way Ask Alteryx builds them.</li> </ul> All but Alteryx Skills, which is in local preview on GitHub, are now generally available. User feedback led Alteryx to develop the new features, according to Ben Canning, the vendor's chief product officer. Through conversations with customers, Alteryx gleaned that enterprises were having trouble getting consistent answers to the same queries from agents built with different development tools such as Microsoft <a href="https://www.techtarget.com/enterprise-software/news/366633987/Microsoft-opens-Copilot-agent-building-to-office-rank-and-file">Copilot</a>, Salesforce <a href="https://www.techtarget.com/enterprise-software/news/366640897/Salesforce-Agentforce-head-discusses-future-of-AI-agents">Agentforce</a> and Amazon <a href="https://www.techtarget.com/ai/definition/What-is-Amazon-Bedrock-AWS-Bedrock">Bedrock</a>. "You end up with [different] answers to the same question and no way to tell which is right, and every agent is paying tokens to re-derive logic the business settled years ago," Canning told TechTarget. "That is the problem we built for." With the business logic that agents need to deliver consistent outputs living in existing Alteryx workflows, the vendor developed tools that connect agents with Alteryx's environment, he continued.  "The gap was that no agent could call them, so we built the layer that lets it," Canning said. "The business builds the logic once, governs it once, and exposes it to any AI system that asks." Menninger noted that Agent Studio is perhaps the most valuable of the new capabilities because it enables users to extend the value of their existing Alteryx workflows to agents. In addition, Alteryx's longstanding position as a connector between an organization's data and its analytics team is helping it stand apart from competing vendors as all data and analytics vendors <a href="https://www.techtarget.com/data-technologies/feature/Evolving-Alteryx-focusing-on-AI-ready-data-logic-for-agents">adjust to AI</a>, Menninger continued. "That unique position gives Alteryx building blocks to bring together the world of AI with data and analytics in ways that others can't necessarily do as easily," he said. Catanzano highlighted the MCP server because it enables agents to interact with Alteryx workflows in a governed and secure manner. Meanwhile, like Menninger, he noted that by positioning its capabilities between agents and enterprise data, Alteryx is carving out a somewhat unique position in <a href="https://www.computerweekly.com/blog/CW-Developer-Network/Series-brief-AI-workflow-platforms-tools">the AI workflow</a>. "These features position it as middleware or a business logic layer between AI agents and enterprise data rather than competing directly as another AI agent platform, which is a distinct architectural approach," he said. Other providers such Databricks, Microsoft and Snowflake provide AI-powered analytics, but they keep connections between data and AI functionality within their own environments, Catanzano continued. "Alteryx is explicitly designing for cross-platform governance and interoperability, which could provide competitive advantage for enterprises already committed to multiple AI tools," he said. </section> <section class="section main-article-chapter" data-menu-title="Alteryx's next steps in its ongoing evolution"> <h2 class="section-title">Alteryx's next steps in its ongoing evolution</h2> Over the final months of 2026, Alteryx is focused on increasing the <a href="https://www.techtarget.com/whatis/definition/transparency">transparency</a> of agent workflows to engender more trust in AI outputs, according to Canning. That includes explaining to an end user why an output is reliable and enabling agents to better understand the information they're acting upon. In addition, Alteryx plans to add capabilities that enable agents to know how well their results hold up over time. "Connecting an agent to governed logic is the part we have solved," Canning said. "Knowing what happened after it ran is the part nobody has." Menninger suggested that Alteryx could benefit from better messaging. He noted that the new capabilities serve existing customers, but the vendor needs to show potential new ones how it fits in <a target="_blank" href="https://mitsloan.mit.edu/ideas-made-to-matter/openness-control-and-competition-generative-ai-marketplace" rel="noopener">the evolving paradigm created by AI</a>. "The key thing Alteryx needs to do to attract new customers is help the market understand better where they fit and how they can complement enterprise AI deployments," he said. "Its existing customers already know this." Catanzano, meanwhile, advised Alteryx to build <a href="https://www.techtarget.com/it-infrastructure/feature/How-providers-industry-specific-cloud-offerings-impact-IT">industry-specific capabilities</a> that enable governed workflows to easily work with the operational systems where organizations execute their business processes. "Additionally, developing more sophisticated workflow templates or a marketplace where organizations can share anonymized, governed workflow patterns for common business processes like supply chain reconciliation or compliance reporting could help attract new users," he said. 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. </section> New features such as an MCP server and a studio for transforming datasets into agents aid the vendor's transformation and help distinguish itself from competing providers. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a199952058.jpg https://www.techtarget.com/data-technologies/news/366650345/Alteryx-evolving-to-become-connective-tissue-for-AI Fri, 11 Sep 2026 06:50:00 GMT Alteryx evolving to become connective tissue for AI Concerns about losing control of data fed into AI models are preventing some enterprises from putting AI tools into production. Cloudera's new partnership with Mistral AI, unveiled on Thursday, will provide customers with tools to exert better command over the data that feeds agents and other AI applications. The partnership allows Cloudera users to integrate <a href="https://www.techtarget.com/ai/news/366625822/What-differentiates-Mistral-AI-reasoning-model-Magistral">Mistral's models</a> directly into their governed data in Cloudera's secure environment, effectively enabling developers to bring AI to their data. Historically, enterprises have had to move data out of their secure data management systems and combine it with models to feed AI <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">the context it needs</a> to deliver accurate outputs. Now, however, as <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">more enterprises</a> put AI tools in production environments, some AI initiatives are stalling as users get more apprehensive about exposing sensitive information to external services, violating local regulations and boosting deployment costs beyond expectations. Data management providers such as Databricks, IBM and Snowflake have responded by integrating with model providers to reduce certain risks by letting customers bring AI to their data, including data kept in private clouds and on-premises environments. Cloudera's partnership with Mistral does the same. "This integration is significant because it underscores the popularity of data and AI sovereignty," Kevin Petrie, an analyst at BARC U.S., told TechTarget. "Volatile geopolitics, regulatory uncertainty and security threats are prompting AI adopters across the globe to reconsider their commitment to the cloud." Cloudera's partnership with Mistral, which has its headquarters in Paris, particularly benefits enterprises with operations in Europe that want to reduce their presence in the United States where AI regulations are <a target="_blank" href="https://www.twobirds.com/en/insights/2026/comparing-us-and-eu-ai-legislation-divergent-regulatory-approaches-and-practical-governance-implicat" rel="noopener">less stringent</a> than in Europe, he continued.  "As a French company, Mistral appeals in particular to European organizations and business units that want to limit their exposure to U.S. technology," Petrie said. <section class="section main-article-chapter" data-menu-title="Minimizing movement"> <h2 class="section-title">Minimizing movement</h2> Moving data costs money. Constantly doing so as agents call on data to carry out their tasks can lead to <a href="https://www.techtarget.com/enterprise-software/feature/AI-feature-spend-is-the-new-software-cost-control-problem">unexpected spending</a>. Meanwhile, moving data across environments can mistakenly violate <a href="https://www.techtarget.com/whatis/definition/data-sovereignty">data sovereignty</a> regulations, and there is the always risk of accidental exposure when shifting data from one system to another. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This integration is significant because it underscores the popularity of data and AI sovereignty. Volatile geopolitics, regulatory uncertainty and security threats are prompting AI adopters across the globe to reconsider their commitment to the cloud. </figure> <figcaption> Kevin PetrieAnalyst, BARC U.S. </figcaption> </div> </blockquote> To bring AI capabilities into data management environments rather than force users to move data to build AI, Databricks now has <a href="https://www.techtarget.com/data-technologies/news/366631961/Databricks-partners-with-OpenAI-to-boost-AI-development">a native integration with OpenAI</a> that makes models available to users in the Databricks Intelligence Platform. In addition, among other vendors, IBM enables customers to integrate data and AI in <a href="https://www.techtarget.com/ai/news/366537216/IBM-launches-Watsonx-a-new-generative-AI-platform">watsonx</a>, and Snowflake makes models such as Google's Gemini 3 <a href="https://www.techtarget.com/data-technologies/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">natively available</a> in Cortex AI. "When you move data into an AI environment, you make a second copy that has to be secured, governed and kept current all over again," Mike Leone, an analyst at Moor Insights & Strategy, told TechTarget. "Bringing AI to the data avoids most of that overhead. ... And for regulated data that legally can't move, running the model in place is the only option." Now, by partnering with Mistral, Cloudera -- which in August introduced Cloudera Anywhere to enable users to build and run AI across multi-cloud and on-premises systems -- is similarly enabling customers to bring AI to their data. "It matters because Mistral's models can be downloaded and run inside a customer's own walls," Leone said. "Most frontier model providers only rent access through their own service. For Cloudera customers in banking or government working disconnected from the internet, a model they can run themselves is the only kind they can use." Cloudera, like many data management vendors that now provide AI development environments, makes a variety of AI models available to users, including <a href="https://www.techtarget.com/cybersecurity/video/Cutting-through-the-Claude-Mythos-hype-What-it-means-for-CISOs">Claude from Anthropic</a> and those that are part of <a href="https://www.techtarget.com/ai/news/366608795/Nvidia-launches-NIM-Agent-Blueprints-to-speed-AI-use">Nvidia's NIM microservices suite</a>. However, the vendor's partnership with Mistral marks the first time it is natively integrating AI models with its platform to eliminate the need to move data and enable users to build AI in private environments. "Model access alone doesn’t solve the challenge of putting AI into production," Abhas Ricky, Cloudera's chief business officer and GM of applied AI, told TechTarget. "Enterprises increasingly need a more complete approach that connects the model to their governed data. ... It's about moving beyond access to a model and giving customers what they need to turn that model into a production AI capability." While Cloudera's move to partner with Mistral enables customers -- especially those in Europe -- to more safely and <a href="https://www.computerweekly.com/news/366649643/Rising-token-bills-force-rethink-of-AI-cost-management">cost effectively</a> build and deploy AI tools, Petrie noted that there is a compromise to using Mistral's models. Because Mistral's default is to route and store data within the European Union, it simplifies staying compliant with Europe's data and AI regulations. However, not all AI models are created equally, and Mistral's, though more cost effective, do not perform as well as those from Anthropic, Google and OpenAI in <a target="_blank" href="https://artificialanalysis.ai/leaderboards/models" rel="noopener">benchmark testing</a> for reasoning. "There are tradeoffs," Petrie said. "While Mistral helps non-U.S. companies increase their sovereignty, its models generally do not perform as well as [leading] LLMs." Leone, meanwhile, noted that bringing AI to data -- though not unique -- remains rare enough that Cloudera's partnership with Mistral somewhat distinguishes Cloudera from other <a href="https://www.techtarget.com/data-technologies/opinion/The-race-to-build-the-ultimate-data-platform">data platform providers</a>. "Model partnerships are close to table stakes across data platforms now, and some already stretch to disconnected environments," he said. "The training piece is the sharper part here, since a Cloudera customer could build a custom model on their own data without moving that data. </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> As Cloudera looks ahead to the final months of 2026, enabling customers to run their data and AI wherever they need is a focal point, according to Ricky. "The Mistral partnership is an extension of that strategy because it brings advanced AI capabilities into the private, sovereign and air-gapped environments where many of our customers keep their most valuable data," he said. "We want customers to have consistent control over their data and AI regardless of where either is deployed." Another wise area to concentrate product development efforts would be <a href="https://www.techtarget.com/data-technologies/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">AI governance</a>, according to Leone. He noted that Cloudera's security and data governance capabilities are highly regarded. Extending those <a href="https://www.techtarget.com/ai/feature/Agentic-governance-must-go-beyond-traditional-IT-practice">to agents</a> on an individualized basis would benefit Cloudera's existing customers and appeal to potential new ones. "Cloudera already has the tooling for building and running agents, so the next step is every agent carrying its own identity and permissions like an employee does," Leone said. "Governance is Cloudera's strongest ground, and agents are being viewed as the hardest thing to govern." 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. </section> The integration enables Cloudera customers to keep data secure when building and deploying agents and helps differentiate the vendor from some of its competitors. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1019737194.jpg https://www.techtarget.com/data-technologies/news/366648767/Cloudera-partners-with-Mistral-to-bring-AI-to-users-data Thu, 10 Sep 2026 07:00:00 GMT Cloudera partners with Mistral to bring AI to users' data Databricks is once again aiming to improve the accuracy of data retrieval systems for AI. In January, Databricks <a href="https://www.techtarget.com/data-technologies/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">launched Instructed Retriever</a>, an alternative to the traditional <a href="https://www.techtarget.com/ai/definition/What-is-retrieval-augmented-generation-RAG-in-AI">retrieval-augmented generation</a> (RAG) pipelines that many enterprises deploy to try to find and feed data to AI agents. With <a target="_blank" href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf" rel="noopener">AI projects failing</a> far more than they succeeded heading into 2026, RAG, which searches for data based on users' queries, showed it was not good enough on its own at providing agents with relevant data. Databricks designed Instructed Retriever, powered by the Instructed-Retriever-1 model, to discover more contextually appropriate data by augmenting user queries with additional parameters to make data retrieval more precise. To further improve data retrieval for AI, Databricks is introducing Adaptive Instructed-Retriever, a new retrieval model specifically designed for complex searches. Instructed Retriever, which deploys parallel single-step retrieval capabilities to simultaneously search multiple parameters, works well for one-step queries but does not perform as fast or accurately when responses require multi-step searches. Adaptive Instructed-Retriever adds sequential search to parallel single-step retrieval to enable multi-step queries without adding <a href="https://www.techtarget.com/whatis/definition/latency">latency</a>. "Adaptive Instructed-Retriever is a significant upgrade because it enables agents to efficiently locate relevant … enterprise assets across large workspaces," William McKnight, president of McKnight Consulting, told TechTarget. "Its key advantage is it can use fast, single-step retrieval for straightforward requests while escalating to multi-step sequential search for more complex ones." Maintained speed, more relevant search results and <a href="https://www.techtarget.com/ai/tip/AI-inference-cost-optimization-An-enterprise-guide">lower costs</a> could all result from using the new model, he continued. "Unlike queries that are focused due to a single-source -- like a data lake query -- agent data retrieval is harder because it can be for anything … and the ways of ensuring which is the 'right data' for a particular agent task are nascent," McKnight said. "Adaptive retrieval addresses this by matching retrieval depth to query complexity to improve the accuracy, speed and cost of agent-driven data discovery." <section class="section main-article-chapter" data-menu-title="Improving accuracy"> <h2 class="section-title">Improving accuracy</h2> When Databricks launched Instructed Retriever, it benchmarked the tool against <a href="https://www.techtarget.com/ai/tip/How-to-prepare-data-for-your-RAG-pipeline">traditional RAG pipelines for AI</a> to demonstrate its proficiency. Adaptive Instructed-Retriever was similarly measured against competing capabilities to show its effectiveness. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Adaptive Instructed-Retriever is a significant upgrade because it enables agents to efficiently locate relevant … enterprise assets across large workspaces. </figure> <figcaption> William McKnightPresident, McKnight Consulting </figcaption> </div> </blockquote> In tests conducted by Databricks -- third-party evidence was not provided by the vendor -- the new model's recall capabilities were in line with those of models from <a href="https://www.computerweekly.com/blog/Cliff-Sarans-Enterprise-blog/Anthropic-Claude-incidents-show-the-limits-of-AI-guard-rails">Anthropic</a>, <a href="https://www.techtarget.com/cybersecurity/tip/What-CISOs-should-know-about-DeepSeek-cybersecurity-risks">DeepSeek</a> and <a href="https://www.techtarget.com/searchsecurity/news/366646755/What-CISOs-can-learn-from-the-Hugging-Face-OpenAI-incident">OpenAI</a>. However, Adaptive Instructed-Retriever executed its searches significantly faster. "This release is incremental for users but significant for the product," Donald Farmer, founder and principal of TreeHive Strategy, told TechTarget. "Adaptive Instructed-Retriever adds multi-step search for questions that the previous Instructed Retriever would get wrong, so a user gets better answers with a fixed latency budget at low cost." Farmer noted that enterprises continue to struggle to discover and operationalize the right data for agents because finding information <a href="https://www.techtarget.com/data-technologies/feature/Controlling-data-sprawl-requires-governance-discipline">across disparate systems</a> is difficult, especially when query instructions can differ from user to user. As a result, agile searches rather than rigid ones are beneficial. "In the enterprise, retrieval is not one problem," Farmer said. "Data is spread over tables, notebooks, dashboards and documents, and getting the right answer often depends on instructions that require interpretation. Plain similarity search can't help." </section> <section class="section main-article-chapter" data-menu-title="Differentiating with governance"> <h2 class="section-title">Differentiating with governance</h2> Although beneficial for users and an important advancement of Databricks' data retrieval capabilities for AI, Adaptive Instructed-Retriever itself is not a competitive differentiator for the vendor, according to McKnight. For example, Snowflake similarly offers a model that delivers text-to-SQL and <a href="https://www.techtarget.com/ai/tip/5-advantages-of-a-semantic-layer-for-enterprise-AI">semantic-based retrieval</a> across widespread enterprise data estates. However, what will appeal to Databricks' users is Adaptive Instructed-Retriever synchronization with Unity Catalog, the vendor's centralized governance layer. "Databricks differentiates [by anchoring] dynamic search, predicate extraction and governance directly into Unity Catalog, which serves as the optimized backbone for its conversational data tools, including Databricks Genie, Databricks Assistant and the Mosaic AI Agent Framework," McKnight said. Farmer likewise noted that Databricks is not the first vendor to provide a small model specialized for data retrieval. Google Cloud and Microsoft -- among others -- offer tools that plan and run parallel subqueries to discover and <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">operationalize relevant data</a>. Databricks, however, goes further with Adaptive Instructed-Retriever by training the model to know when to stop searching, according to Farmer. "When an AI agent searches for data, it can take one look and answer, or it can keep looking," he said. "More looks usually give a better answer, but each one takes time and costs money. The hard part is knowing when to stop. Databricks trained a small model to make the stopping decision itself." In addition, Farmer posited that the savings which the new model could lead to might force <a href="https://www.techtarget.com/data-technologies/opinion/The-race-to-build-the-ultimate-data-platform">competing vendors</a> to respond. "Customers do not choose a data platform for a retrieval model, and the competitors' own retrieval layers are probably good enough, but the argument for cost-control is compelling," he said. "So, every vendor will need a cheap, fast retrieval layer for agents. But they'll do it their own way, I expect." </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title">Next steps</h2> Databricks developed Adaptive Instructed-Retriever after observing customers that have begun to put agentic AI tools into production ask increasingly intricate questions of those agents, according to Michael Bendersky, the vendor's director of research. "Agentic search requires multiple rounds of retrieval using tools, reviewing the information and then searching again," he told TechTarget. "In these environments, the agents search, retrieve, and then examine again until they find the right answer. At the same time, you don't want the user to wait longer than necessary for the system to finish the search." Over the next few months, Databricks will add serverless <a href="https://www.techtarget.com/enterprise-software/definition/What-is-a-graphics-processing-unit-GPU">GPU</a> compute capabilities -- now in public preview -- to Adaptive Instructed-Retriever to optimize the model's usage by automatically scaling up when needed and scaling down when workload demands decrease, Bendersky continued. In addition, he said that Databricks plans to continue to reduce barriers enterprises face when moving AI into production by addressing <a href="https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context">context for agents</a>, AI governance, cost control and the openness of its platform. Continuing to address the quality and performance of existing capabilities to enable customers to build and deploy reliable AI capabilities is a wise strategy for Databricks, according to McKnight. Specifically, improving the accuracy rate of its model serving capabilities and simplifying the use of <a href="https://www.techtarget.com/data-technologies/feature/Databricks-lakehouse-a-secret-weapon-for-WNBAs-Fever">its platform</a> would be beneficial. "To support enterprise-grade AI agents, Databricks must resolve its real-time concurrency bottlenecks and high model serving failure rates to ensure stable, low-latency agent execution loops," McKnight said. "Additionally, they need to simplify the overall user experience by shifting toward a zero-administration serverless platform and offering prebuilt operational templates … to accelerate business ROI." Farmer, meanwhile, suggested that Databricks further improve its data retrieval capabilities by better enabling them to choose which tables, metrics and semantic definitions to use when searching for <a href="https://www.techtarget.com/ai/post/AI-agents-are-only-as-smart-as-the-data-that-feeds-them">which assets to feed AI tools</a>. "The hard problem for data agents is not finding documents, but choosing among thousands of tables, metrics and semantic definitions," he said. "I think that's a future demand [from customers]." 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. </section> Adding sequential search to improve the speed and accuracy of responses to complex queries evolves existing capabilities and keeps Databricks in step with competitors. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20learning_g1186820873.jpg https://www.techtarget.com/data-technologies/news/366650164/Databricks-launches-model-to-boost-data-retrieval-for-AI Thu, 10 Sep 2026 06:56:00 GMT Databricks launches model to boost data retrieval for AI AI is on track to be the key technology that enables business transformation, giving companies a competitive edge. The implementation of AI throughout the enterprise is projected to grow significantly in the years ahead. According to Gartner, overall worldwide <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener">spending on AI</a> is forecasted to total $2.52 trillion by the end of 2026, an increase of 44% year-on-year. Worldwide end-user spending on AI models and platforms is projected to reach $64 billion by the end of 2026, a rise of just over 63% from 2025. Spending on generative AI (GenAI) models is forecasted to grow by 117% and spending on AI platforms will rise by about 37%. AI can help businesses be more productive by automating their processes, including using robots and autonomous vehicles, and by supporting their existing workforces with AI technologies such as assisted and <a href="https://www.techtarget.com/ai/definition/What-is-augmented-intelligence">augmented intelligence</a>. Most organizations are working to implement AI in their business processes and products. Companies are using <a href="https://www.techtarget.com/ai/tip/15-top-applications-of-artificial-intelligence-in-business">AI in numerous business applications</a>, including finance, healthcare, smart home devices, retail, fraud detection and security surveillance. Numerous AI certifications and courses cover the basics and applications of AI systems, so we've narrowed the field to 10 of the more diverse and comprehensive programs. <section class="section main-article-chapter" data-menu-title="Why AI certifications are important"> <h2 class="section-title">Why AI certifications are important</h2> AI certifications are important for the following reasons: <ul class="default-list"> <li>Learning about and understanding artificial intelligence can set individuals on the path to <a href="https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer">promising careers in AI</a>.</li> <li>A prestigious AI certification can set you apart from the competition and <a href="https://www.techtarget.com/ai/feature/How-to-start-a-career-in-AI-Advice-from-experts">show employers you have the skills</a> they want and need.</li> <li>The <a href="https://www.techtarget.com/ai/feature/AIs-business-future-Whats-to-come-in-the-next-5-years">AI field is constantly changing</a>, and it can be a challenge to keep up with that pace of change. A certification tells an employer you are familiar with the latest developments in the field.</li> <li>AI demands advanced education. A National University examination of 15,000 job postings on Indeed.com found that nearly 80% of <a href="https://www.techtarget.com/whatis/feature/Top-AI-jobs">AI job openings</a> require candidates to have a master's degree, 60% demand at least a bachelor's degree. Another 18% require a PhD, while only 8% would consider a high school diploma.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="10 notable AI certifications and courses"> <h2 class="section-title">10 notable AI certifications and courses</h2> <h3>1. Artificial Intelligence Graduate Certificate by Stanford University School of Engineering</h3> Key elements. This graduate certificate program covers the principles and technologies that form the foundation of AI, including logic, probabilistic models, machine learning (ML), robotics, <a href="https://www.techtarget.com/ai/definition/What-is-natural-language-processing-NLP">natural language processing</a> (NLP) and knowledge representation. Learn how machines can engage in problem-solving, reasoning, learning and interaction, as well as how to design, test and implement algorithms. To complete the Artificial Intelligence Graduate Certificate, you must complete one or two required courses and two or three elective courses. You must receive a 3.0 grade or higher in each course to continue taking courses through the non-degree option program. Prerequisites. Applicants must have a bachelor's degree with a minimum 3.0 grade point average, as well as college-level calculus and linear algebra credit, including a good understanding of multivariate derivatives and matrix/vector notation and operations. Familiarity with probability theory and basic probability distributions is necessary. Programming experience, including familiarity with Linux command-line workflows, Java/JavaScript, C/C++, Python or similar languages, is also required. Each course might have individual prerequisites. <a target="_blank" href="https://online.stanford.edu/programs/artificial-intelligence-graduate-certificate" rel="noopener">Registration details</a> <h3>2. MIT's Professional Certificate Program in Machine Learning and Artificial Intelligence</h3> Key elements. This certification program operates like a traditional college course, running for 16 days and taught during the summer either online or at MIT's campus. Courses are taught by MIT's AI professors. The program provides a well-rounded foundation of knowledge that can be put to immediate use to help people and organizations advance cognitive technology. MIT recommends taking two core courses first. These are Machine Learning for Big Data and Text Processing: Foundations and Machine Learning for Big Data and Text Processing: Advanced. The Information Classification: General Foundations course costs $2,500; the Advanced course costs $3,500. The remaining 11 days consist of elective classes, which last between two and five days each and cost between $2,500 and $4,700. Prerequisites. The program is designed for technical professionals with at least three years of experience in computer science, statistics, physics or electrical engineering. MIT highly recommends this program for anyone in data analysis or for managers who need to learn more about predictive modeling. <a target="_blank" href="https://professional.mit.edu/course-catalog/professional-certificate-program-machine-learning-artificial-intelligence-0" rel="noopener">Registration details</a> <h3>3. Artificial Intelligence: Business Strategies and Applications by University of California, Berkeley Executive Education and Emeritus</h3> Key elements. Instead of teaching the how-tos of AI development, this certificate program targets senior leaders looking to integrate AI into their organizations and managers leading AI teams. It introduces the basic applications of AI to those in business; covers AI's current capabilities, applications, potential and pitfalls; and explores the effects of automation, ML, deep learning, <a href="https://www.techtarget.com/ai/definition/What-is-a-neural-network">neural networks</a>, computer vision and robotics. In this course, you'll learn how to build an AI team, organize and manage successful AI application projects, and study the technology aspects of AI to communicate effectively with technical teams and colleagues. Prerequisites. This program is mainly targeted toward C-suite executives, senior managers and heads of business functions, data scientists and analysts, and mid-career AI professionals. <a target="_blank" href="https://em-executive.berkeley.edu/artificial-intelligence-business-strategies" rel="noopener">Registration details</a> <h3>4. IBM AI Professional Certificate by Coursera</h3> Key elements. This beginner-level AI certification course aims to help students gain job-ready skills in AI technologies, GenAI models and programming skills to build AI chatbots and apps. The six-month program consists of a 10-course series: <ul class="default-list"> <li>Introduction to Software Engineering.</li> <li>Introduction to Artificial Intelligence.</li> <li>Generative AI: Introduction and Applications.</li> <li>Generative AI: Prompt Engineering Basics.</li> <li>Introduction to HTML, CSS, and JavaScript.</li> <li>Python for Data Science, AI, and Development.</li> <li>Developing AI Applications with Python and Flask.</li> <li>Building Generative AI-Powered Applications with Python.</li> <li>Generative AI: Elevate Your Software Development Career.</li> <li>Software Development Career Guide and Interview Preparation.</li> </ul> The program runs for six months with four hours per week, flexible self-paced learning. Students who successfully complete the program earn a professional certificate. Prerequisites. While the series is open to everyone with both technical and nontechnical backgrounds, the final two courses require some knowledge of Python to build and deploy AI applications. For learners without any programming background, an introductory Python course is included. <a target="_blank" href="https://www.coursera.org/professional-certificates/applied-artifical-intelligence-ibm-watson-ai" rel="noopener">Registration details</a> <h3>5. Deep Learning Specialization by Andrew Ng via Coursera</h3> Key elements. This comprehensive series of five intermediate- to advanced-level courses covers neural networks and deep learning, as well as their applications. Build and train deep neural networks, identify key <a href="https://www.techtarget.com/ai/tip/AI-parameters-Explaining-their-role-in-AI-model-performance">architecture parameters</a>, and implement vectorized neural networks and deep learning to applications. In this course, you will build a <a href="https://www.techtarget.com/ai/definition/What-is-a-convolutional-neural-network-CNN">convolutional neural network</a> and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data. You will also build and train recurrent neural networks, work with NLP and word embeddings, and use HuggingFace tokenizers and transformer models to perform <a href="https://www.techtarget.com/whatis/definition/named-entity-recognition-NER">named entity recognition</a> and question answering. Prerequisites. Intermediate Python skills; basic programming; understanding of for loops, if/else statements, and data structures; and a basic grasp of linear algebra and ML. <a target="_blank" href="https://www.coursera.org/specializations/deep-learning" rel="noopener">Registration details</a> <h3>6. Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning via Coursera</h3> Key elements. This four-course deeplearning.ai certificate program runs 18 hours and covers best practices for using TensorFlow, an open-source ML framework. Students will also learn how to create a basic neural network in TensorFlow, train neural networks for <a href="https://www.techtarget.com/ai/definition/What-is-machine-vision">computer vision applications</a> and learn to use convolutions to improve their neural networks. This is one of four courses that are a part of the <a target="_blank" href="https://www.coursera.org/professional-certificates/tensorflow-in-practice" rel="noopener">DeepLearning.AI TensorFlow Developer Professional Certificate</a>. Prerequisites. This series is constructed for software developers who want to build scalable AI-powered algorithms. High school-level math and experience with Python coding are required. Prior ML or deep learning knowledge is helpful but not required. <a target="_blank" href="https://www.coursera.org/learn/introduction-tensorflow" rel="noopener">Registration details</a> <h3>7. Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL</h3> Key elements. This is a comprehensive online course designed to teach the tools and technologies of agentic AI, GenAI and reinforcement learning in order to create real-world AI applications. The course is structured into 22 sections, comprising 128 lectures and 15.5 hours of video content. Key topics include Q-Learning; Deep Convolutional Q-Learning; Proximal Policy Optimization (PPO); LLMs; Low-Rank Adaptation (LoRA) and Quantization (QLoRa); Asynchronous Advantage Actor-Critic (A3C); Soft Actor-Critic (SAC); and Transformers. Students can practice these tools by building eight different AIs: <ul class="default-list"> <li>Build an AI Agent with a Foundation Model (LLM) for business assistance, all powered by the Cloud.</li> <li>Build an AI with a Q-Learning model and train it to optimize warehouse flows in a Process Optimization case study.</li> <li>Build an AI with a Deep Q-Learning model and train it to land on the moon.</li> <li>Build an AI with a Deep Convolutional Q-Learning model and train it to play the game of Pac-Man.</li> <li>Build an AI with an A3C (Asynchronous Advantage Actor-Critic) model and train it to fight Kung Fu.</li> <li>Build an AI with a PPO (Proximal Policy Optimization) model and train it for a self-driving car.</li> <li>Build an AI with a SAC (Soft Actor-Critic) model and train it for a self-driving car.</li> <li>Build an AI by fine-tuning a pre-trained LLM (Llama 2 by Meta) with Hugging Face and re-train it to chat with you about medical terms.</li> </ul> Upon completion, students receive access to three extra AI models: DDPG, or Deep Deterministic Policy Gradient; Full World Model; and Evolution Strategies & Genetic Algorithms. Each of these extra AIs comes with a video lecture explaining the implementation, a mini-PDF and the Python code. The course also includes a free three-hour extra course on GenAI and LLMs with cloud computing. Prerequisites. A basic understanding of high school math and some knowledge of Python programming are all that is required. <a target="_blank" href="https://www.udemy.com/course/artificial-intelligence-az/?ranMID=39197&couponCode=OF83024F" rel="noopener">Registration details</a> <h3>8. Google Cloud's Introduction to Generative AI Learning Path</h3> Key elements. Google Cloud's Introduction to Generative AI Learning Path covers what GenAI and <a href="https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models">large language models</a> are for beginners. Since it's from Google, it is oriented around specific Google applications, which is only good if you are a Google shop. Tools used include Google Tools and Vertex AI. It includes a section on responsible AI, encouraging the learner to keep ethical practices around GenAI in mind. Prerequisites. None. <a target="_blank" href="https://www.skills.google/paths/118" rel="noopener">Registration details</a> <h3>9. Artificial Intelligence Engineer (AIE) Certification by the Artificial Intelligence</h3> Key elements. The ARTiBA certification exams consist of a three-track AI learning deck that contains specialized resources for skill development and job-ready capabilities to help credentialed professionals move into senior positions as individual contributors or team managers. The AIE curriculum covers every concept of ML, regression, supervised learning, unsupervised learning, reinforced learning, neural networks, NLP, cognitive computing and deep learning. Prerequisites. The ARTiBA certification has three tracks aimed at different educational and experience levels: AiE Track 1 – Bachelor's Degree Pathway Requires a Bachelor's degree in computer science, data science, AI or related discipline -- such as mathematics, statistics or computational sciences -- from an accredited institution. Professional experience requirements include three years in hands-on programming or software engineering. AiE Track 2 – Master's Degree Pathway Requires a Master's degree in computer science, data science, AI or related discipline -- such as mathematics, statistics or computational sciences -- from an accredited institution. Professional experience requirements include two years in software engineering, data engineering, ML or related technical roles. AiE Track 3 – Professional Experience Pathway There are no fixed academic requirements. The track is aimed at professionals with experience as self-taught engineers, AI startup builders or community-recognized technical work; also, candidates without formal degrees who have published or deployed real-world AI or ML systems. <a target="_blank" href="https://www.artiba.org/ai-certifications/artificial-intelligence-engineer" rel="noopener">Registration details</a> <h3>10. University of Phoenix and OpenAI Partnership</h3> Key elements. The University of Phoenix is partnering with OpenAI to offer AI skills education for adult learners. The collaboration lets students explore AI applications across teaching and learning, student support, career services, institutional operations and collaborative research. The aim is to help working professionals develop practical AI skills that they can apply in their workspaces. The program also focuses on the ethical and proper use of AI tools. The University of Phoenix and OpenAI's collaborative approach is to teach tool-independent AI skills using a skills-mapped curriculum embedded in university programs. Students can map the learnings to badge and skills pathways, and they can share their verified AI badges and skills achievements on resumes or digitally using LinkedIn, Zip Recruiter or other sites. Prerequisites. None. The OpenAI collaboration is available to any University of Phoenix student, regardless of the student's program of study or degree program. <a target="_blank" href="https://www.phoenix.edu/" rel="noopener">Registration details</a> Editor's note: This article was originally written by Andy Patrizio and expanded upon by Jim O'Donnell. Andy Patrizio is a technology journalist with almost 30 years' experience covering Silicon Valley who has worked for a variety of publications on staff or as a freelancer, including Network World, InfoWorld, Business Insider, Ars Technica and InformationWeek. He is currently based in southern California. Jim O'Donnell is a news director for TechTarget, where he covers IT strategy and enterprise ESG. </section> As AI adoption accelerates, AI certifications and courses have proliferated. These offerings here go beyond the basics, deepening your knowledge of this rapidly evolving technology. https://cdn.ttgtmedia.com/rms/onlineimages/certification_g483411626.jpg https://www.techtarget.com/whatis/feature/10-top-artificial-intelligence-certifications-and-courses Wed, 09 Sep 2026 00:00:00 GMT Top 10 AI certifications and courses for 2026 Contact centers have been an effective way to take advantage of AI advancements. These technologies deliver businesses rapid ROI and actionable insights that can streamline processes and improve operational efficiency. AI now accurately and conveniently resolves customer issues across several communication channels using voice, text, messaging and other emerging channels. Additionally, businesses can take advantage of improved contact center visibility and predictive insight through AI-derived analytics, metrics and KPIs. <section class="section main-article-chapter" data-menu-title="3 main types of contact center AI"> <h2 class="section-title">3 main types of contact center AI</h2> The first category of AI is <a href="https://www.techtarget.com/searchenterpriseai/definition/conversational-AI">conversational</a> and <a href="https://www.techtarget.com/ai/feature/How-generative-AI-is-changing-creative-work">generative AI</a>, which uses large language models (<a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM">LLM</a>s) combined with Retrieval-Augmented Generation (RAG) and natural language understanding. These technologies enable natural conversations across voice, text, messaging and other channels through modern interactive voice response (<a href="https://www.techtarget.com/searchcustomerexperience/definition/Interactive-Voice-Response-IVR">IVR</a>) systems, chatbots and virtual assistants. A closely related and fast-growing category in the contact center space is agentic AI. These systems go beyond basic conversations and push to execute multi-step actions. This allows contact center systems to work autonomously, only escalating to human agents when absolutely needed. The third type of contact center AI uses <a href="https://www.techtarget.com/data-technologies/tip/Generative-AI-can-improve-not-replace-predictive-analytics">predictive generative analysis</a> to process interaction data, statistics and KPIs with the goal of making recommendations on how to improve operational performance or increase customer satisfaction. This type of AI helps contact center operators meet their performance goals without having to manually sift through and analyze data using manual or semi-automated processes. </section> <section class="section main-article-chapter" data-menu-title="5 popular contact center AI features"> <h2 class="section-title">5 popular contact center AI features</h2> IVR systems, voice AI, chatbots, agentic virtual agents, agent coaching and monitoring, predictive analytics and generative AI capabilities are some of the key features in a modern contact center. AI-generated channel summaries are among the more popular, most widely adopted and high-impact capabilities in modern contact center platforms. <h3>1. Advanced conversational AI</h3> Traditional, menu-based IVR has rapidly evolved due to AI. Modern conversational AI now powers natural voice interactions with support for multiple languages and use advanced biometrics capabilities for contact authentication. These conversational AI systems can complete routine tasks, retrieve information from an LLM in real time and transfer callers to the appropriate human agent when required. When properly applied, organizations can expect shorter wait times and a smoother customer experience. <h3>2. Agentic self-service chatbots and virtual agents</h3> Modern virtual agents now go beyond scripted chatbots. Powered by custom LLMs, RAG and integrated tools, self-service chatbots and virtual agents now understand and can assist with the following workflows: <ul class="default-list"> <li>Handle complex customer requests with accurate, context-aware responses.</li> <li>Retrieve and act on live data from CRM and other enterprise systems.</li> <li>Execute multi-step transactions across connected tools.</li> <li>Resolve issues end to end without human intervention when appropriate.</li> </ul> When escalation to a human agent is needed, AI chatbots and agents deliver a full conversation and task context history. This helps drive faster resolution rates, delivers a consistent experience and can operate 24/7 with minimal staffing. <h3>3. Real-time agent coaching and performance monitoring</h3> At this stage, most contact centers still use a combination of AI-powered IVR, chatbots, virtual assistants and human agents. For the human side of the operation, AI continues to improve the customer service experience. Nearly every aspect of a human agent's contact with customers can be analyzed in real-time using AI. <a href="https://www.techtarget.com/searchcustomerexperience/tip/Top-7-call-center-agent-performance-metrics-to-track">Examples of collected metrics</a> include call and chat logs, handle times, time-to-service resolution, queue times, hold times and customer survey results. All this information is collected and analyzed to determine how customer satisfaction can increase, while simultaneously decreasing time-to-service resolution. AI is used to track these statistics, formulate performance profiles and <a href="https://www.techtarget.com/searchcustomerexperience/answer/Nine-skills-every-call-center-agent-job-requires">make automated coaching suggestions to agents</a>. <h3>4. Automatic call insights with predictive analytics</h3> CRM software is commonly used with contact center platforms and stores a wealth of customer data, including contact info, purchase preferences and history and previous interaction touchpoints. AI now commonly combines CRM data with real-time interaction signals to intelligently deliver relevant context and predictive recommendations to both human and virtual agents. These capabilities also support proactive engagement, helping contact center managers anticipate needs, improve personalization and expand revenue upsell opportunities. <h3>5. AI-generated transcription, call and chat summaries</h3> Generative AI and advanced language models are now used to transcribe, organize and summarize post-call and post-chat summaries. These rich summaries can then be put into a CRM system and further analyzed to determine various aspects of a customer's interaction with the contact center, including their overall satisfaction, likelihood of purchasing products and services in the future, brand <a target="_blank" href="https://www.shopify.com/blog/loyalty-program" rel="noopener">loyalty</a> and which targeted marketing and sales methods are most likely to translate into future sales. </section> <section class="section main-article-chapter" data-menu-title="Choosing the right contact center AI platform"> <h2 class="section-title">Choosing the right contact center AI platform</h2> If you're evaluating contact center platforms and are interested in understanding what to look for when it comes to AI, focus on the following three criteria: <h3>1. Strong integration with existing apps and tools</h3> Contact center operations include a number of tools where AI platforms must integrate cleanly. This includes CRM platforms, knowledge bases, workforce management, business intelligence (BI) ticketing and underlying telephony infrastructure. Look for a contact center platform with API and <a href="https://www.techtarget.com/ai/tip/How-the-Model-Context-Protocol-simplifies-AI-development">Model Context Protocol (MCP) support</a>. <h3>2. Mature generative and agentic AI features</h3> Identify tools with proven support for LLM, RAG and the ability for AI to complete multi-step actions fully autonomously. Also be sure that platforms have clear guardrails and reliable escalation paths to human agents so AI can operate safely and reliably inside a contact center environment. <h3>3. Clear and measurable outcomes</h3> When evaluating contact center platforms, identify those that deliver clear, actionable metrics that managers and human agents can use to track and improve over time. The strongest platforms for your environment will highlight performance data on resolution rates, handle times, customer satisfaction and others. AI should be able to turn these insights into real-time coaching, automated quality feedback checks and other improvement opportunities for all contact center agents and managers. Andrew Froehlich is founder of InfraMomentum, an enterprise IT research and analyst firm, and president of West Gate Networks, an IT consulting company. He has been involved in enterprise IT for more than 20 years. </section> As multifunctional contact centers grow more complex and vital as revenue and relationship centers, generative and agentic AI have become a foundational component. https://cdn.ttgtmedia.com/rms/onlineimages/chatbot_g1250576673.jpg https://www.techtarget.com/enterprise-software/feature/5-key-contact-center-AI-features-and-their-benefits Thu, 03 Sep 2026 09:00:00 GMT 5 key contact center AI features and their benefits Boomi is betting big on a control layer for AI. The vendor, which began as <a href="https://www.techtarget.com/data-technologies/news/366554135/New-Boomi-AI-tool-enables-natural-language-data-integration">a data integration specialist</a> but now focuses on enabling customers to activate their data for AI, on Tuesday launched the Agent Control Plane. The new layer brings previously disparate Boomi capabilities together in a single environment and is designed to provide customers with capabilities that enable them to connect AI agents with business systems, govern agents in production and <a href="https://www.techtarget.com/enterprise-software/feature/AI-feature-spend-is-the-new-software-cost-control-problem">rein in spending on AI</a>. Keeping control over agents and the cost of doing so are rising concerns for many enterprises as they try to <a href="https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf">move past experiments with AI</a> and put agents into production. Boomi's Agent Control Plane is therefore a valuable addition for the vendor's users, according to Michael Ni, an analyst at Constellation Research. "Boomi is making a bid for one of the more valuable control points in enterprise AI, the layer between agents and the systems where business actually gets done," he told TechTarget. "As companies add more models and agents, they need a way to control what those agents can access, what they can do and what they cost." Boomi is not the only vendor adding tools aimed at giving customers better control over their agents, Ni continued, noting that AWS, Salesforce and ServiceNow are among the others that also provide features for orchestrating agents. However, Boomi's integration capabilities give it a foundation from which to build <a href="https://www.techtarget.com/it-strategy/feature/The-AI-agent-governance-gap-How-CIOs-can-gain-control">a control plane for agents</a> that could help it compete for market share. "The idea of an Agent Control Plane is quickly becoming a crowded but strategic land grab," Ni said. "Boomi's advantage is … to turn its footprint into a neutral data activation layer where enterprise data can be contextualized, governed and acted upon across models and platforms." <section class="section main-article-chapter" data-menu-title="Control vs. chaos"> <h2 class="section-title">Control vs. chaos</h2> Agents that are improperly overseen can cause serious problems. For example, an agent powered by Anthropic's Claude large language model <a target="_blank" href="https://www.theguardian.com/technology/2026/apr/29/claude-ai-deletes-firm-database" rel="noopener">deleted a company's entire production database</a> last April. Two months earlier, another agent allegedly <a target="_blank" href="https://sumsub.com/media/news/developer-warns-ai-agent-defamation-post-shows-risks-of-autonomous-ai/" rel="noopener">wrote a blog post</a> defaming a developer after the developer rejected the agent's code contribution. Beyond headline-grabbing mishaps, agents that aren't closely controlled can drive up an organization's spending by getting caught in recursive loops and consuming unlimited amounts of tokens. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Boomi is making a bid for one of the more valuable control points in enterprise AI, the layer between agents and the systems where business actually gets done. As companies add more models and agents, they need a way to control what those agents can access, what they can do and what they cost. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> Gartner <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure" rel="noopener">predicts</a> that by the start of 2027, 40% of organizations will demote or decommission agents due to a lack of proper governance. Boomi's Agent Control Plane is designed to provide the governance enterprises need to trust that their AI tools appropriately connect to enterprise systems such as Salesforce and Workday, act only as intended in production, and do so within an expected spending range. As a result, though other vendors are similarly addressing AI governance, Boomi's control layer for agents is significant for its customers, according to Stephen Catanzano, an analyst at Omdia, a division of TechTarget. "The Agent Control Plane represents a big addition because it addresses what Boomi identifies as the largest unmanaged risk in enterprise technology today, [which is] giving AI agents the deep access they need to transactional systems while maintaining governance, cost control, and security that most organizations currently lack," he told TechTarget. Meanwhile, there is some potential differentiation between Boomi's approach to governing AI and competing capabilities from Salesforce through <a href="https://www.computerweekly.com/blog/CW-Developer-Network/MuleSoft-Omni-Gateway-offers-route-to-agentic-visibility-consistent-governance">MuleSoft</a>, AWS and specialists such as Apigee, Catanzano continued. "Boomi seems to be betting that their unified approach sitting between any agent and any system, rather than being tied to a single cloud ecosystem, provides the differentiation," he said. Boomi's Agent Control Plane is designed to be flexible, running across public clouds, customers' virtual private clouds and on-premises environments. In addition, it supports data and <a href="https://www.techtarget.com/cybersecurity/feature/The-push-for-digital-sovereignty-What-CISOs-need-to-know">digital sovereignty</a> to ensure that customers remain compliant with the rules and regulations where their information is created. The Agent Control Plane combines pre-existing Boomi capabilities to provide the following: <ul class="default-list"> <li>A single, centralized location for overseeing agents and tools across platforms, including putting limits on <a href="https://www.techtarget.com/whatis/definition/token">token</a> consumption and stopping high-risk actions until they have human approval.</li> <li>Enforcement through the Boomi AI Gateway, which combines MCP Gateway and LLM Gateway in one control panel and is built on the technology the vendor acquired when <a href="https://www.constellationr.com/insights/news/boomi-buy-lunardev-eyes-intelligent-prompt-and-model-routing">it bought Lunar.dev</a>.</li> <li>Governed connectivity through Boomi Connect MCP servers so that agents can safely call on enterprise systems to inform their actions.</li> <li>Deployment flexibility via a hybrid runtime architecture to adhere to data sovereignty rules, secure sensitive information and control spending.</li> <li>Data semantics and lineage to ground agent reasoning with approved data.</li> <li>Natural language processing capabilities in Boomi Orchestrate so that non-technical teams can build agentic AI workflows.</li> </ul> "This came directly from what we were hearing in a critical mass of customer conversations, not from a single feature request," Ed Macosky, Boomi's chief product and technology officer, told TechTarget. User feedback consistently included concerns over <a href="https://www.computerweekly.com/news/366645085/Boomi-CEO-shares-vision-of-AI-cost-management">increasing spending on AI</a>, difficulty connecting agents with disparate transactional systems and fear of losing control over intellectual property once it's accessed by agents, he continued. "The Agent Control Plane exists because enterprises shouldn't have to choose between moving fast on AI and keeping control of their data and infrastructure," Macosky said.  Meanwhile, Boomi's control layer for AI appears logically constructed by combining centralized visibility with enforcement capabilities, deployment flexibility and <a href="https://www.techtarget.com/data-technologies/opinion/Tracing-data-lineage-in-AI-systems">data lineage</a>, according to Catanzano. "The architecture directly maps to the three problems Boomi highlights -- governance gaps that cause production incidents, runaway token costs that have made AI spend management the most in-demand FinOps skill, and trust deficits," he said. Ni likewise noted that the Agent Control Plane features the capabilities needed to provide Boomi's customers with a single environment for <a href="https://www.techtarget.com/data-technologies/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">governing AI</a>. In addition, it does so by combining tools that have already been proven to work. "Boomi has put the major pieces together and built on areas where it already has known strengths," Ni said. "Boomi's next step will be to go deeper into runtime context and managing agents across their full lifecycle. ... That becomes increasingly important as enterprises move from experimenting with agents to operating them at scale." </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> After launching the Agent Control Plane , Macosky said that Boomi's product development plans for the rest of 2026 focus on improving AI governance across providers and models, broadening its partnership network to add openness, and enhancing basic platform features such as <a href="https://www.techtarget.com/data-technologies/feature/Why-AI-forces-security-first-governance">security</a> and reliability. One specific way the vendor could better serve its customers would be to add tools that show whether agents in action delivered their desired outcome, according to Ni. "Boomi already connects the data, exposes the tools, governs the agent and orchestrates the action. Now it needs to show whether that action delivered the desired business outcome, then use that feedback to improve the next decision," he said. "That would make Boomi’s positioning around data activation more tangible to CIOs and business leaders." Catanzano similarly suggested that Boomi expand beyond governance and connectivity to measure and improve the <a href="https://www.techtarget.com/enterprise-software/feature/Software-value-needs-outcome-measures">business outcomes</a> generated by agents. "Boomi could continue serving users and attract new ones by … helping organizations not just control what their agents do, but systematically improve how well they do it through analytics that connect agent actions to actual business metrics," he said. 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. </section> The Agent Control Plane combines previously disparate capabilities in one environment to help users better observe and command their fleets of agents. https://cdn.ttgtmedia.com/rms/onlineimages/machine_learning_g1303163039.jpg https://www.techtarget.com/data-technologies/news/366649690/Boomi-intros-layer-for-controlling-AI-costs-and-connections Wed, 02 Sep 2026 09:49:00 GMT Boomi intros layer for controlling AI costs and connections Search Enterprise AI Resources and Information from TechTarget 60 webmaster@techtarget.com