IPO inevitable for Databricks after adding $5B in funding

Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Sat, 15 Aug 2026 02:54:12 GMT https://www.techtarget.com/searchdatamanagement editor@techtarget.com Databricks keeps raising funding. After revealing in July that it was in the process of securing new financing, Databricks on Thursday closed on $5 billion in venture capital funding at a rate that values the company at $190 billion and brings its total financing over $32 billion. But after raising $5 billion or more in four separate funding rounds -- <a href="https://www.techtarget.com/data-technologies/news/366617434/Databricks-record-10-billion-funding-round-to-fuel-growth">including $10 billion in December 2024</a> -- the question isn't what the data management and AI vendor should do with the money or whether it can raise more. Instead, it is what Databricks should do next. While many data management and analytics vendors are struggling to attract investors, there seems to be an endless supply of private capital willing to invest in Databricks. Beyond rounds exceeding $5 billion, the vendor raised $2 billion in debt financing in February and added $1 billion or more three additional times. However, an eventual initial public stock offering would be more advantageous for Databricks than continuing to raise private funding, according to Michael Ni, an analyst at Constellation Research. "Staying private has allowed Databricks flexibility to ignore public scrutiny on its profitability and predictability, but a successful public IPO could provide a growth flywheel," he told TechTarget. Specifically, if the public markets respond positively to Databricks' <a href="https://www.techtarget.com/data-technologies/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">continued additions</a> of  data and AI stack components, a healthy stock price would give the vendor greater flexibility to expand than raising private funding, he continued. In addition, going public -- and the need to respond to public-market sentiments -- would make it easier for the CIOs of potential customers to make Databricks part of their AI and <a href="https://www.techtarget.com/data-technologies/definition/What-is-data-architecture-A-data-management-blueprint">data architectures</a>. "The value in an IPO would go beyond the cash raised, enabling Databricks to move from a successful private platform to a consolidator and long-term standard for the enterprise AI stack," Ni said. However, when an IPO happens depends, to some degree, on external factors such as the <a target="_blank" href="https://www.ey.com/en_us/insights/ipo/ipo-market-trends" rel="noopener">market conditions for major IPOs</a>. Also in question is how significantly an IPO would benefit Databricks when the company is seemingly able to raise funding whenever it desires to fuel technological innovation, acquisitions and other initiatives. <section class="section main-article-chapter" data-menu-title="Databricks is different"> <h2 class="section-title">Databricks is different</h2> Databricks is far from the only data management vendor that has expanded over the past few years to provide a data foundation for AI development. Rival <a href="https://www.techtarget.com/data-technologies/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake</a> and hyperscale cloud providers <a href="https://www.techtarget.com/data-technologies/news/366644853/AWS-latest-to-introduce-context-layer-for-agentic-AI">AWS</a>, <a href="https://www.techtarget.com/data-technologies/news/366641929/Google-unveils-data-cloud-purpose-built-for-agentic-AI">Google Cloud</a> and <a href="https://www.techtarget.com/data-technologies/news/366643955/Microsoft-boosts-Fabric-to-make-it-a-foundation-for-AI">Microsoft</a> have all similarly added capabilities that enable customers to connect AI tools with proprietary data. In addition, database vendors such as MongoDB and Couchbase, niche specialists including Alation and Informatica, and analytics providers such as Qlik and Tableau have all added tools to enable AI development <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">informed by relevant, high-quality data</a>. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The value in an IPO would go beyond the cash raised, enabling Databricks to move from a successful private platform to a consolidator and long-term standard for the enterprise AI stack. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> Databricks' valuation of $190 billion, however, dwarfs Snowflake's market capitalization of $114 billion, despite Snowflake's stock price more than doubling since April. And while Databricks continues to raise billions of dollars with each funding round, vendors such as Confluent, Domo, Dremio, DBT Labs and Fivetran have either sought buyers or merged with peers with funding difficult for data and analytics vendors to raise since <a href="https://www.techtarget.com/data-technologies/news/252520740/Tech-stock-sell-off-signals-tough-times-for-data-vendors">tech stocks plummeted in 2022</a>. Venture capitalists simply view Databricks differently than they view most other data and AI providers, according to Sanjeev Mohan, founder and principal of analyst firm SanjMo. "Investors see Databricks becoming the single consolidated platform for a wide span of data and AI use cases," he told TechTarget. "Databricks is different in that it actually keeps absorbing adjacent categories onto one architecture rather than bolting on a feature and calling it a platform." In addition, venture capitalists have faith in Databricks CEO Ali Ghodsi, Mohan continued. "Investors find their CEO to be a force of nature who has tremendous ambition and grit to build out the platform," he said. One of those investors is Benjamin Black, co-founder and managing director of Akkadian Ventures, which holds exposure to Databricks through Powerlaw Corp. Numerous factors have led investors to flock to Databricks while only minimally funding some of its smaller competitors, according to Black. In particular, he noted that its execution at scale -- surpassing $7 billion in annual recurring revenue and year-over-year revenue growth of approximately 80% -- are significant factors in the venture capital community's <a href="https://www.techtarget.com/data-technologies/news/366617350/Record-funding-round-reflects-Databricks-differentiation">continued interest in Databricks</a>. "Databricks is popular because it has demonstrated unusually strong execution at enormous scale just as enterprises are trying to turn AI from a promising experiment into a core business capability," Black said. Strong growth at scale and technology execution -- a platform designed to enable users to successfully develop AI tools -- makes Databricks not merely a company investors want as part of their portfolio, but one they want to get in on early, he continued. "It has the revenue, growth, and institutional maturity to list when it chooses," Black said. "That combination is precisely why investors want exposure now rather than waiting for a ticker symbol. Beyond how investors view Databricks, which is as much a bet on growth potential and financial performance than a recognition of technological prowess, Databricks has distinguished itself from a technological perspective as well, according to David Menninger, an analyst at ISG Software Research. Significant features Databricks has developed over the past few years include Unity Catalog for governing data and AI, Mosaic AI for developing generative and agentic AI, <a href="https://www.techtarget.com/data-technologies/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers">Lakebase</a> to provide a PostgreSQL database foundation for AI initiatives, <a href="https://www.techtarget.com/data-technologies/news/366625695/Latest-Databricks-tools-use-AI-to-simplify-AI-development">Agent Bricks</a> to automate aspects of AI development, and its Genie natural language interface. "Databricks has executed well," Menninger said. "Investors are primarily interested in high-growth companies. Databricks has been growing rapidly for many years." ISG groups Databricks with 12 other data and AI platform providers, he continued. Peers include fellow data platform providers Cloudera, Snowflake and Teradata, <a href="https://www.techtarget.com/it-infrastructure/definition/What-is-hyperscale-cloud-Computing-and-data-center-uses-explained">hyperscale cloud</a> vendors AWS, Google Cloud and Microsoft, and other broad-based technology providers such as IBM, Oracle and SAP. "We rate Databricks as an overall leader among this group, which means we place them in the top three," Menninger said. </section> <section class="section main-article-chapter" data-menu-title="Private vs. public"> <h2 class="section-title">Private vs. public</h2> In theory, Databricks could stay private for the foreseeable future. "Databricks has had virtually no financial reason to face the public markets because the private markets are treating them so favorably," William McKnight, president of McKnight Consulting, told TechTarget. Similarly, Mohan noted that private investors have been so willing to keep Databricks flush with cash that the vendor has the luxury of waiting until the ideal time to <a href="https://www.techtarget.com/data-technologies/news/366636532/Databricks-adds-4B-funding-round-IPO-could-be-next">explore an IPO</a>. "The private market has given Databricks everything an IPO would, without the constraints," he told TechTarget. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Private secondary sales only stretch so far. A public market lets employees and early backers actually cash out, unlike private market. That is good for employee retention and morale. </figure> <figcaption> Sanjeev MohanFounder and principal analyst, SanjMo </figcaption> </div> </blockquote> Eventually, however, the benefits of going public outweigh those of remaining private, Mohan continued. For example, each private funding round dilutes the value that existing investors hold in Databricks. In addition, public companies have the transparency and credibility that can attract large enterprise and government customers. "Private secondary sales only stretch so far," Mohan said. "A public market lets employees and early backers actually cash out, unlike the private market. That is good for employee retention and morale." McKnight likewise noted that the <a href="https://www.investopedia.com/ask/answers/021015/what-difference-between-ipo-and-private-placement.asp">benefits of going public</a> ultimately outweigh those of continuing to raise private capital. Specifically, he noted that an IPO would enable Databricks to have access to liquid, market-validated funds for future acquisitions, stock to attract and retain elite talent, access to cheap debt and secondary capital markets, and SEC-audited transparency. "Even with unlimited private capital, going public provides advantages that private funding cannot replicate," McKnight told TechTarget. That said, raising more private funding remains a possibility for Databricks. Black noted that a company such as Databricks can theoretically remain private indefinitely, as long as it can continue to raise capital, its employees and early investors can get liquidity without the company going public, and it can demonstrate its financial health without public filings. "Private capital has gotten deep enough, with the largest funds able to write enormous checks, that a company can keep raising privately for a long time without ever needing the public markets," Black said. In fact, it's possible that Databricks and other companies,such as <a href="https://www.tradingview.com/symbols/NASDAQ-STRIPE/">Stripe,</a> that are able to raise massive amounts of funding, could create a new class of "forever private" companies, he continued. "Databricks and Stripe get all primary and secondary liquidity they need in the private markets," he said, noting that they can innovate without having to meet the expectations of public markets and each has already made the type of acquisitions that public stock is often used to fund. "If the private markets stay so accommodating for the best mega-privates, why go public at all?" Menninger likewise noted that Databricks -- in theory -- could keep raising funding and remain private. He pointed out that IPOs typically fund expansion, and Databricks has been able to expand by <a href="https://www.techtarget.com/data-technologies/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition">making numerous acquisitions</a> and investing in product development. "If you have access to the capital you need, it’s much better not to have to face the quarterly scrutiny of the public markets. It can really distract from the day-to-day operations of the business," Menninger said. However, the greater likelihood is that Databricks will eventually go public, he continued, noting that benefits such as the public disclosure of financials attracting new customers, equity given to employees having more value when a company is public, and more easily funded acquisitions could outweigh the benefits of remaining private. "Investors need to be able liquidate their investments at some point," Menninger said. "As long as there are other investors willing to step in, they can potentially go on like this forever, but that's highly unlikely. Even if they reach the point where they don't need additional capital, the existing investors will want to be able to close out their positions." </section> <section class="section main-article-chapter" data-menu-title="If not now, when?"> <h2 class="section-title">If not now, when?</h2> Databricks will eventually go public, according to Ghodsi. "We will go public, I promise," he said during an interview with CNBC on Thursday following the closing of Databricks' $5 billion funding round. But the timing for an IPO has to be right, Ghodsi emphasized. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/ghodsi_ali.jpg" alt="Databricks CEO Ali Ghodsi">Ali Ghodsi </div> The stock market, though there have been significant IPOs in 2026, has been volatile, <a target="_blank" href="https://www.google.com/search?q=dow+jones&oq=dow+jones&gs_lcrp=EgRlZGdlKgYIABBFGDkyBggAEEUYOTIICAEQ6QcY_FXSAQgxODE2ajBqNKgCAbACAQ&sourceid=chrome&ie=UTF-8" rel="noopener">trending generally up</a> but lurching more in response to economic indicators, major companies' earnings filings and other reports. "The markets have been really wobbly," Ghodsi told CNBC. "For three months, everything is down and it's going to be terrible. And then the other day, it's amazing. There was one great [announcement] by one software company and the whole market is up 10%, and then the next day everything is down 10%. It's just very turbulent times." Beyond market unpredictability, the final months of 2026 may not be a good time for Databricks to go public because other major tech companies are also planning to go public and a Databricks IPO could get overshadowed. Already in 2026, <a href="https://www.techtarget.com/ai/news/366644478/SpaceX-IPO-aims-for-AI-and-orbital-data-centers">SpaceX</a> pulled off the largest IPO in history when it raised $75 billion. Just as Databricks was announcing its latest funding round, reports surfaced that <a href="https://www.techtarget.com/ai/news/366624572/Anthropic-intros-next-generation-of-Claude-AI-models">Anthropic</a> may be planning an IPO in October that could surpass SpaceX's. In addition, <a href="https://www.techtarget.com/ai/news/366628814/OpenAIs-GPT-5-is-out-Where-it-shines-and-where-it-doesnt">OpenAI</a> has already filed initial paperwork for an IPO. Those IPOs could take both potential investors and the infrastructure required to go public -- the investment firms that manage such undertakings -- away from Databricks.  "Ghodsi has been open that he views this as a poor year to go public with SpaceX, OpenAI, and Anthropic all crowding the IPO conversation, and he's pointed to a possible window in 2027," Mohan said. One thing Databricks does not have to worry about is missing its opportunity to go public, which happened to numerous data management and analytics providers in 2022. Vendors including Qlik, <a href="https://www.techtarget.com/data-technologies/news/252477178/ThoughtSpot-IPO-could-be-coming-after-vendor-adds-first-CFO">ThoughtSpot</a> and <a href="https://www.techtarget.com/data-technologies/news/252516835/Pyramid-Analytics-raises-120M-IPO-could-be-next">Pyramid Analytics</a> spoke openly about IPOs, with Qlik even <a href="https://www.techtarget.com/data-technologies/news/252511695/Qlik-planning-an-IPO-files-application-with-the-SEC">filing its initial paperwork</a>. But after tech stocks plummeted in the spring of 2022, the technology landscape changed toward the end of the year, with AI becoming the dominant focus. Since then, niche data management and analytics vendors have had to reposition themselves and have largely lost the favor of the investment community. Qlik, ThoughtSpot and Pyramid remain private.  "Databricks is delaying because public markets are already giving it public company resources without public company constraints," Ni said. "Databricks is using the financial flexibility to drive growth rather than prove quarterly predictability. An IPO will become more necessary when the need for employee liquidity, acquisition currency and public market permanence outweighs the benefits of flexibility." </section> <section class="section main-article-chapter" data-menu-title="The outlook"> <h2 class="section-title">The outlook</h2> While an IPO is all but inevitable, and before going public Databricks remains able to attract large amounts of funding to fuel seemingly any growth initiative, technology needs to remain the vendor's main priority, according to McKnight. "My focus, and my clients' focus, is less on the financial headlines and much more on the core architectural and product execution they deliver to the enterprise," he said. For example, the <a href="https://www.techtarget.com/enterprise-software/feature/AI-feature-spend-is-the-new-software-cost-control-problem">high cost of AI development</a> is an ongoing concern for many enterprises, in some cases preventing companies from building agents and other applications. In addition, navigating Databricks to develop data and AI products can be complicated. Meanwhile, after acquiring a spate of companies -- including its purchase of PostgreSQL database innovator <a href="https://www.techtarget.com/data-technologies/news/366649200/Databricks-Electric-acquisition-adds-embeddable-PostgreSQL">ElectricSQL</a> this week -- there are still tools that need to be integrated with Databricks' existing platform before customers can take full advantage. "They could neutralize cost opacity, deliver a true zero-administration experience, provide prebuilt AI templates within Agent Bricks and see about integrating [acquisitions such as] PostgreSQL Lakebase (Neon) and Tabular under Unity Catalog," McKnight said, noting that some competitors deliver faster execution and lower costs across customers data estates. "Additionally, Databricks suffers from severe competitive multi-year ingestion pipeline degradation and critical autoscaling endpoint failures under real-time concurrent inference," he added. Mohan similarly suggested that while a well-timed IPO rather than another funding round is essential, so is ensuring that the many new features Databricks has <a href="https://www.techtarget.com/data-technologies/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">developed</a> and <a href="https://www.techtarget.com/data-technologies/news/366588032/Databricks-1B-plus-Tabular-acquisition-adds-Iceberg-support">acquired</a> work as intended, and do so in an integrated manner that enables customers to build the data and AI tools they desire. "Proof that their expansion is actually working," he said when asked what he'd like to see from Databricks over then next year. "There is no guarantee that their forays into marketing and security -- or even Lakebase -- will appeal to buyers who may be reluctant to put all their eggs in one basket. I'd like to see them prove these expansions merge into one coherent product rather than a widening surface." 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> Experts note that the benefits of going public ultimately outweigh those of staying private, even with the vendor seemingly able to raise unlimited financing. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1222040206.jpg https://www.techtarget.com/data-technologies/news/366649434/IPO-inevitable-for-Databricks-after-adding-5B-in-funding Fri, 14 Aug 2026 14:43:00 GMT IPO inevitable for Databricks after adding $5B in funding Databricks is going Electric. Though not as historic as Bob Dylan's move away from the acoustic guitar at the 1965 Newport Folk Festival, Databricks on Aug. 11 revealed the acquisition of startup ElectricSQL to add new <a href="https://www.theserverside.com/tip/MySQL-vs-PostgreSQL-Compare-popular-open-source-databases">PostgreSQL database capabilities</a>. Financial terms of the deal were not disclosed. Electric raised $600,000 in pre-seed funding in 2022. Databricks first added PostgreSQL database capabilities with its May 2025 <a href="https://www.techtarget.com/data-technologies/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition">acquisition of Neon</a>, which Databricks has subsequently transformed into the core of <a href="https://www.techtarget.com/data-technologies/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers">its Lakebase platform</a>. The acquisition of Electric allows Databricks to extend PostgreSQL database capabilities beyond data lakehouses to edge devices, including agents, additional AI applications and mobile devices. Perhaps the biggest benefit of the acquisition is that Databricks will enable agents to more quickly and easily fix themselves when they take missteps, given that the data they need to do so will be more readily available than before, according to Mike Leone, an analyst at Moor Insights & Strategy. "[The acquisition] definitely matters, but it's more of a specific fix than a big swing," he told TechTarget, adding that agents need to keep notes as they operate to see when they go wrong and correct any errors. "Electric shrank Postgres down small enough to run beside the agent instead of across a network. That makes it fast, and it lets the agent undo a step cleanly instead of leaving a half-finished mess." Stephen Catanzano, an analyst at Omdia, a division of TechTarget, called Databricks' acquisition of Electric significant. Like Leone, he noted that it addresses a specific need in agentic AI development and deployment. "The acquisition addresses a critical infrastructure gap in the emerging world of agentic applications, where AI agents need both ultra-low latency access to local data in sandboxed environments and real-time synchronization with centralized systems," he told TechTarget. "Electric … extends Databricks' existing Postgres capabilities from the centralized lakehouse to the edge where agents actually execute." San Francisco-based Databricks' purchase of Electric is the latest in a series of acquisitions Databricks has made over the past few years to accelerate its expansion beyond data management <a href="https://www.techtarget.com/data-technologies/news/366633904/New-Databricks-tools-target-successful-agentic-AI-development">into AI development</a>. <section class="section main-article-chapter" data-menu-title="Creating a competitive edge"> <h2 class="section-title">Creating a competitive edge</h2> PostgreSQL, an open source format for storing disparate data types, has emerged as a popular means of managing data for agents and other AI applications. By 2024, PostgreSQL had become <a target="_blank" href="https://survey.stackoverflow.co/2024/technology" rel="noopener">the most popular database</a>, ahead of MySQL, Microsoft SQL Server, MongoDB and Redis. Versatility -- storing geospatial, time series, JSON and vector database workloads -- is one reason for PostgreSQL's popularity. A large, vibrant community that contributes to its evolution is another. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The acquisition addresses a critical infrastructure gap in the emerging world of agentic applications, where AI agents need both ultra-low latency access to local data in sandboxed environments and real-time synchronization with centralized systems. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> Given PostgreSQL's growing role in the AI pipeline, Databricks, <a href="https://www.techtarget.com/data-technologies/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake</a> and <a href="https://www.techtarget.com/data-technologies/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite">Redpanda</a> all acquired PostgreSQL database capabilities in 2025. Now, by adding Electric's embeddable PostgreSQL databases, Databricks is extending such capabilities beyond traditional data and AI pipelines. In addition, Electric founders James Arthur and Kyle Matthews are joining Databricks as part of the acquisition. Based in Berkeley, Calif., Electric is the developer of <a href="https://pglite.dev/docs/about">PGlite</a>, a lightweight WebAssembly (WASM) version of a PostgreSQL database small enough to be embedded into agents and other applications. With PGlite embedded, applications can <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">access appropriate context</a> where they run rather than having to call back to traditional databases and other data repositories. In addition, Electric provides a real-time sync engine that connects data between agents and a centralized PostgreSQL architecture so that agents can access an organization's data of record in the cloud, as well as the local context that enables them to deliver outputs based on relevant data. Once Electric's capabilities are integrated with Lakebase, Databricks users will be able to deploy swarms of agents that stay current while working alongside one another. "Lakebase already made Postgres fast for agents," Devin Pratt, an analyst at IDC, told TechTarget. "Electric moves it the last few inches, right into the sandbox itself." Meanwhile, though Databricks competitors such as AWS, Snowflake and Microsoft provide PostgreSQL databases, the acquisition of Electric adds embeddable PostgreSQL capabilities that <a href="https://www.techtarget.com/data-technologies/news/366617350/Record-funding-round-reflects-Databricks-differentiation">distinguish Databricks</a> from its peers, Pratt continued. "Snowflake bought its Neon, [but] nobody's bought their Electric yet," he said. "That's the gap Databricks just closed on itself." Catanzano similarly noted that adding Electric's capabilities will help Databricks stand apart from competitors. While other providers offer managed PostgreSQL and data synchronization services, they are designed for traditional database workloads rather than the unique requirements of agents that access the data they need at runtime, operate in distributed sandboxed environments, and <a href="https://www.techtarget.com/data-technologies/news/366646162/Potential-consequences-are-severe-when-AI-agents-lack-context">require both individualized context</a> and a shared state. "Electric's combination of embeddable WASM Postgres with real-time sync architecture specifically designed for agent collaboration represents a more specialized solution for this emerging use case, positioning Databricks ahead of the curve … rather than simply keeping pace," Catanzano said. Leone, however, noted that while Electric's capabilities provide some differentiation, they aren't completely unique. For example, startup Turso similarly provides each agent with its own small database. "What's less common is Databricks insisting the small copy next to the agent be the exact same database as the big one in the middle, not a lookalike," he said. </section> <section class="section main-article-chapter" data-menu-title="Additive acquisitions"> <h2 class="section-title">Additive acquisitions</h2> While acquiring Electric adds capabilities that help distinguish Databricks among the vendors racing to add tools that help customers build AI tools, it's just the latest in a long line of acquisitions Databricks has made over the past few years to quickly add capabilities that fuel AI development. In addition to Neon and Electric, Databricks acquisitions include <a href="https://www.techtarget.com/data-technologies/news/366542389/Databricks-acquiring-MosaicML-to-add-more-generative-AI">MosaicML</a>, which forms the foundation of the Mosaic AI development platform, along with Arcion, BladeBridge, Einblick, Lilac AI, Mooncake and Tabular to add capabilities that complement Mosaic AI and Lakebase. "They've been incredibly smart, and unusually consistent about it," Leone said. "Databricks buys things it would otherwise spend years building itself, and it buys the people along with the technology, which is why the same names keep showing up. Electric fits that perfectly." In Electric's case, engineering a full database to run in a small workspace such as an agent's <a href="https://www.techtarget.com/cybersecurity/definition/sandbox">sandbox</a> is different than engineering a database to run in the cloud, he continued. "Electric has already solved it," Leone said. "The Electric team is joining the same group that came over with Neon, so that's two database companies now folded into one team on purpose." Pratt likewise noted that Databricks' acquisition strategy has been effective to date. In particular, MosaicML and Neon have become key parts of the vendor's platform, though Tabular's capabilities, which added support for Apache Iceberg storage, have been slower to integrate, he continued. "Databricks isn't buying new products, it's buying pieces that already have a home waiting for them," Pratt said. </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> With Databricks <a href="https://www.techtarget.com/data-technologies/news/366636532/Databricks-adds-4B-funding-round-IPO-could-be-next">continuing to raise capital</a> -- the vendor revealed on Thursday that it added $5 billion in venture capital funding that would bring its total funding to over $30 billion -- more acquisitions are possible. Leone, however, advised Databricks to concentrate at least some resources on delivering new capabilities it <a href="https://www.techtarget.com/data-technologies/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">publicly revealed in recent months,</a> but hasn't yet made generally available. In addition, making its growing platform easier to navigate would be wise, Leone continued. "A lot of what Databricks announced this year is still labeled beta or preview, meaning it isn't fully released," he said. "Getting that work done is worth more to current customers than another round of new features. I'd also want them to say plainly which tool to use for which job, because the platform has gotten big enough that its size is now the problem." Pratt, meanwhile, suggested that Databricks continue to ensure that the capabilities it is acquiring get integrated in a manner that makes them both additive as well as easy to use with the rest of <a href="https://www.techtarget.com/data-technologies/news/366639354/New-Databricks-tool-targets-streaming-data-cost-complexity">the vendor's platform</a>. "Neon to Lakebase took about a year -- that's the pace the rest of the portfolio should be held to," 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> Flush with funding, the data management and AI vendor continues to make purchases that add capabilities aimed at aiding users building and deploying agents. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1250581414.jpg https://www.techtarget.com/data-technologies/news/366649200/Databricks-Electric-acquisition-adds-embeddable-PostgreSQL Thu, 13 Aug 2026 17:15:00 GMT Databricks' Electric acquisition adds embeddable PostgreSQL MongoDB on Thursday unveiled new features designed to connect operational data with AI development tools so users can easily and quickly feed agents and other applications the context they need to deliver accurate outputs. Revealed during MongoDB.local Build Fest, a user event in San Francisco, the new capabilities, among others, include connectors and integrations with agentic coding platforms such as Grok Build and Vercel, a managed <a href="https://www.techtarget.com/data-technologies/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol (MCP) server</a> for MongoDB's Atlas database service, and automated Voyage AI <a href="https://www.techtarget.com/data-technologies/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector embeddings</a> in MongoDB Atlas. Collectively, MongoDB's latest set of tools are significant additions for existing users given that they unify capabilities in one platform that otherwise must be pieced together with tools from different vendors, according to Stephen Catanzano, an analyst at Omdia, a division of TechTarget. "This is a strong set of capabilities that collectively solve … the operational complexity and data staleness that comes from stitching together separate systems for databases, vector stores and embeddings," he told TechTarget. "They unify live operational data with AI retrieval and agent workflows, eliminating synchronization overhead while letting developers work inside tools they already use." Unification, in fact, is how MongoDB is distinguishing itself from competing vendors that are also introducing features designed to aid AI development, Catanzano continued. "Competitors typically require teams to bolt together separate operational databases, vector stores and embedding services that need constant synchronization," he said. "The emphasis on real-time data access combined with Voyage AI models and automated embedding management that eliminates custom pipelines, sets it apart from the fragmented architectures most vendors require." Based in New York City, MongoDB is a longtime database specialist that, like competing database vendors such as <a href="https://www.techtarget.com/data-technologies/news/366645317/Couchbase-evolution-continues-with-new-data-layer-for-AI">Couchbase,</a> along with broader data management vendors such as <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>, has expanded into AI development over the past few years. <section class="section main-article-chapter" data-menu-title="Aiding AI development"> <h2 class="section-title">Aiding AI development</h2> Despite the high priority data management and AI providers have placed on simplifying AI development, building agents and other AI tools that perform as intended in production remains challenging. Heading into 2026, <a target="_blank" href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjws_DTBhB_EiwAXZknGeJC8uqNU9YNaTWHUXJEhY_qQNxuAtQrd6VPywO0mCMZiX_C4vmHshoCupUQAvD_BwE" rel="noopener">Deloitte reported</a> that only one-quarter of all enterprises expected even 40% of their AI projects to ever make it past the pilot stage. In response, vendors ranging from tech giants such as AWS and Microsoft to specialists including Alteryx and Tableau have all introduced features designed to enable customers to more successfully build AI tools. In particular, developing tools that enable enterprises to build a data layer that <a href="https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">feeds AI tools relevant context</a> has been a focal point for many vendors. That includes MongoDB. In January, the vendor <a href="https://www.techtarget.com/data-technologies/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">launched new Voyage AI models</a> to improve data retrieval for agents, in May MongoDB <a href="https://www.techtarget.com/data-technologies/news/366642768/MongoDB-adds-new-vector-performance-capabilities-to-aid-AI">released new vector indexing capabilities</a> and in June <a href="https://www.techtarget.com/data-technologies/news/366645316/Latest-MongoDB-tools-tackle-top-AI-development-hurdles">introduced tools</a> that further address feeding agents with relevant context. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This is a strong set of capabilities that collectively solve … the operational complexity and data staleness that comes from stitching together separate systems for databases, vector stores and embeddings. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> Now, MongoDB is continuing its efforts to aid AI development more capabilities  The addition of the specific capabilities unveiled on Wednesday was motivated by a mix of customer feedback and MongoDB's own observations of how the AI development landscape is evolving, according to Ben Cefalo, the vendor's chief product officer. "The tools people build will change quickly, and if MongoDB isn't already present wherever that work is happening, [we would be] asking builders to step outside their workflow to use us," he said. "That's the wrong direction. … For us, it's the same principle MongoDB was founded on, [which is to] meet developers on their own terms, wherever they are." The new features with the most potential value for users are the managed MCP server for Atlas and Automated Voyage AI vector embeddings in MongoDB Atlas, according to Catanzano. The first provides developers with a managed version of <a href="https://www.techtarget.com/data-technologies/news/366631455/MongoDB-adds-MCP-server-expands-AI-development-capabilities">the vendor's MCP server</a>, which was first launched in September 2025 and enables users to standardize connections between agents and MongoDB. The second automates  the generation of vectors that make data discoverable to agents. "The managed MCP server provides a zero-maintenance, standards-based way for any agent to access MongoDB data with proper governance controls, while automated embeddings solve the painful synchronization problem by generating and updating embeddings inside the database as documents change," Catanzano said. In addition to the managed MCP server for Atlas and Automated Voyage AI vector embeddings in MongoDB Atlas, MongoDB's new capabilities include the following: <ul type="disc" class="default-list"> <li>Integrations with agent-powered coding platforms Cursor, Devin, Grok Build and Vercel, including a native integration between MongoDB Atlas and <a href="https://www.nocode.mba/articles/v0-review-ai-apps">v0 by Vercel</a>.</li> <li>Native connectors between real-time operational data and applications built with Claude and ChatGPT.</li> <li>Atlas App Connections, an identity and access layer that enables users to track and audit an agent's actions.</li> <li>An API that makes MongoDB's embedding and reranking models available through Atlas and accessible to any application whether inside MongoDB's environment or not.</li> <li>A new AI model that enables coding agents to retrieve information from <a href="https://www.techtarget.com/whatis/definition/codebase-code-base">codebases</a> with higher accuracy and lower cost than when doing so from general-purpose embedding models.</li> <li>Vector search in Atlas Stream Processing.</li> </ul> Most of the new capabilities are not yet generally available, although Automated Voyage AI vector embeddings in MongoDB Atlas, the Atlas Embedding and Reranking API, and vector search in Atlas Stream Processing are GA. "This is a strong new set of capabilities that strengthens MongoDB's position as a leading multi-modal database that supports agentic AI," Kevin Petrie, an analyst at BARC U.S., told TechTarget. "MongoDB correctly recognizes that AI projects require diverse structured and unstructured data inputs, along with diverse search and retrieval methods. Its combination of [capabilities] address these needs." Like Catanzano, Petrie noted the value of the managed MCP server for Atlas. In addition, he highlighted the significance of the connectors for users. "The native LLM connectors and MCP server help standardize integration with the rich ecosystem of AI elements," he said. </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> As MongoDB plots product development over the final months of 2026, the vendor plans to continue focusing on better enabling the developers and startup companies creating <a href="https://www.techtarget.com/ai/feature/11-real-world-agentic-AI-examples-and-use-cases">innovative AI applications</a> to build new tools, according to Cefalo. "That's a big part of why you're seeing us show up so heavily in San Francisco," he said. In addition, MongoDB aims to increase awareness of its Voyage AI models given how they can enable organizations to run elements of the <a href="https://www.techtarget.com/ai/tip/Agentic-AI-workflows-Trends-examples-and-best-practices">agentic AI workflow</a>, such as vector indexing and data retrieval, without having to piece together tools from various vendors, Cefalo continued. "The direction is consistent: fewer systems, better accuracy and less babysitting infrastructure," he said. Catanzano, meanwhile, suggested that MongoDB add more agent governance capabilities as more organizations begin to move agents into production environments. In addition, he noted that prebuilt agent templates for common uses such as customer support and <a href="https://www.techtarget.com/ai/opinion/How-to-manage-the-gap-between-enterprise-AI-use-and-AI-regulation">compliance monitoring</a> could aid customers that lack the expertise to build such tools from scratch. "MongoDB could deepen agent governance with comprehensive observability and policy enforcement for production deployments, build cost optimization features that intelligently route between models based on accuracy and budget tradeoffs, and offer prebuilt agent templates for common enterprise use cases," 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> Unifying frequently disparate capabilities such as vector embedding, data retrieval and agent workflows helps distinguish the vendor amid a crowded field of data and AI providers. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20learning_g1186820873.jpg https://www.techtarget.com/data-technologies/news/366649199/MongoDB-intros-latest-features-to-fuel-AI-development Thu, 13 Aug 2026 17:03:00 GMT MongoDB intros latest features to fuel AI development As of Aug. 2, the EU AI Act assesses penalties for the first time against AI oversights stemming from poor data management. The Act places the onus of compliance squarely on data teams and applies to any organization that interacts with EU residents. A clear problem is that most organizations didn't institute their data infrastructure with such documentation in mind because it wasn't previously required. Now that the Act's obligations for transparency, general purpose AI (GPAI) models and AI literacy are in force, there's a divide between what it penalizes and where most data governance programs actually stand. By examining which specific data management mechanisms are required and where most data governance programs fall short, data teams can identify concrete actions to rectify these shortfalls. <section class="section main-article-chapter" data-menu-title="Data management requisites"> <h2 class="section-title">Data management requisites</h2> Originally, the AI Act mandated high-risk Annex III systems to be fully compliant by August 2, 2026. The <a href="https://www.techtarget.com/searchenterpriseai/news/366646620/EU-AI-Act-compliance-deadline-is-here-What-to-watch">May 2026 Digital Omnibus</a> agreement tabled many of those requirements until December 2027. The components that took effect Aug. 2 still demand rigorous data management. Under Article 50's transparency requirements, data teams must mark AI-manipulated depictions of real people and deepfakes as AI-generated. Chatbots and similar conversational applications must inform people they're interacting with AI, unless it's obvious, and users must be told when biometric categorization or emotion recognition is in use. GPAI model providers, including teams who substantively tailor foundation models or build applications heavily reliant on them, must keep technical documentation under Annex XI, <a href="https://www.techtarget.com/ai/tip/Explore-the-role-of-training-data-in-AI-and-machine-learning">furnish training data summaries</a>, and confirm the data adheres to EU copyright law under a documented policy. Data literacy is also required of all users and operators of AI systems. </section> <section class="section main-article-chapter" data-menu-title="Governance shortcomings"> <h2 class="section-title">Governance shortcomings</h2> For data teams, the biggest obstacle to EU AI Act compliance is AI governance itself. <a href="https://www.gartner.com/en/documents/7854881">A 2025 Gartner survey</a> on how organizations handle cybersecurity risk found that nearly 90% lacked AI governance programs. Although that figure represents only one slice of the enterprise, it still indicates how immature AI governance is today. Many organizations run AI governance tied to specific employees or projects. Meeting the Act's transparency and GPAI mandates becomes far easier when the roles, rules and responsibilities for governing AI systems are <a href="https://www.techtarget.com/data-technologies/opinion/Data-governance-fails-without-CFO-ownership">formalized, documented and spread</a> throughout the enterprise. Many users and operators of AI systems know their own tooling but <a href="https://www.techtarget.com/ai/tip/How-to-keep-data-silos-from-damaging-your-AI-projects">not what runs in other departments</a> or serves other customers. Compliance with the Act's GPAI and transparency requirements all but requires data teams to maintain a comprehensive inventory of which AI systems the organization operates, along with their data dependencies and formal documentation of both -- a demand that falls hardest on GPAI model providers. Noncompliance penalties for GPAI models are €15M or 3% of global turnover, whichever is higher. Systemic risk GPAI models are trained with 10²⁵ FLOPs of compute and require adversarial testing such as red teaming, cybersecurity protections and energy consumption data disclosures. Data teams without complete inventories of AI assets can't meet this requirement.     Siloed, employee-tied governance undermines transparency even more directly when it comes to labeling model outputs as AI-generated. When only individual employees hold that knowledge, it rarely reaches end users, even if they pass it to a manager or colleague. The Act requires that knowledge be readily available to the general public when people interact with these systems, as well as to internal users. Noncompliance penalties are €7.5M or 1.5% of global turnover, whichever is higher. </section> <section class="section main-article-chapter" data-menu-title="Remediation"> <h2 class="section-title">Remediation</h2> Applying the fundamentals of data governance to their AI instances lets data teams close these gaps. The first step is to classify AI assets fully: the models themselves, their use and users, training data, data models, taxonomies, and data sources. That inventory is what the Act's GPAI documentation and transparency obligations assume already exists. Without it, a data team cannot show which systems are in scope, let alone document them. Typically, only a few key employees hold this institutional knowledge. However, data teams can formalize it through enterprise architecture to map these systems, and through regex, machine learning, and other statistical methods to discover, classify and tag them.  Next, data teams should categorize the AI assets by <a href="https://axis-intelligence.com/eu-ai-act-enforcement-guide/">the EU AI Act risk tier</a>: minimal risk, limited risk with transparency requirements, high risk, and prohibited. The tier a system falls into determines which obligations attach to it. A limited-risk chatbot triggers Article 50's disclosure rules, while a systemic GPAI model pulls in adversarial testing and energy reporting. These tiers inform next steps, particularly the immediate documentation of GPAI models and public disclosures when people are interacting with AI systems. The tiers require data teams to update existing governance policies or write new ones. Splitting those policies into two distinct sets helps: one set for the public's interactions with AI systems, another for internal work, such as <a href="https://www.techtarget.com/whatis/definition/red-teaming">red teaming</a> or cybersecurity for systemic GPAI models. Legal counsel can tell an organization whether their use of GPAI models classifies it as a model provider, even if it didn't build the model. In many cases, deployments of dynamic agents can automate parts of the transparency requirements, including <a href="https://www.techtarget.com/searchenterpriseai/feature/How-watermarking-AI-content-benefits-businesses">watermarking content as AI-generated</a> or triggering workflows to inform end users they're interacting with AI. Jelani Harper is a data industry analyst and journalist covering data management, AI and enterprise IT for more than a decade. He is a research lead at Blue Badge Insights, writing analyst reports for GigaOm and articles for VentureBeat and The New Stack.    </section> Most enterprises govern AI in siloed, employee-tied pockets, but the EU AI Act assumes formalized enterprise-wide governance. That gap drives the risk data teams now face. https://cdn.ttgtmedia.com/rms/onlineimages/legal_g1065824400.jpg https://www.techtarget.com/data-technologies/feature/Where-data-teams-fall-short-on-governance-for-the-EU-AI-Act Wed, 12 Aug 2026 14:46:00 GMT Where data teams fall short on governance for the EU AI Act Who is responsible when an AI agent acts on a supplier proposal that is three years out of date? The developer will likely claim that the agent performed well, but with hopelessly inadequate information. The business will discover that the problem isn't the agent or the model; it's the context, and that redefines the role of the Chief Data Officer. The rise of agentic AI has shifted the chief data officer (CDO) from a traditional data steward to a context curator – the executive accountable for governing what AI systems retrieve and under what definitions. This reorientation of the CDO's role should be exciting, but it faces some genuine challenges. <a href="https://newsroom.ibm.com/2025-11-13-ibm-study-chief-data-officers-redefine-strategies-as-ai-ambitions-outpace-readiness">IBM research</a> shows that 81% of CDOs are focusing on AI investments, but only 26% feel confident that their data can support new revenue streams with AI. A comparable share say their organizations are prepared to use unstructured data to deliver business value. <section class="section main-article-chapter" data-menu-title="The CDO and agentic AI"> <h2 class="section-title">The CDO and agentic AI</h2> When AI models were primarily driving chatbots or text generation, <a href="https://www.techtarget.com/searchenterpriseai/tip/Why-does-AI-hallucinate-and-can-we-prevent-it">errors and hallucinations</a> were largely mitigated by human users who interacted with the system. But today, the pressure on CDOs originates in <a href="https://www.techtarget.com/searchenterpriseai/feature/Real-world-agentic-AI-examples-and-use-cases">what agentic systems can do</a>. For teams used to delivering enterprise data to human decision-makers through reports and dashboards, the autonomy of AI agents is a different problem entirely. An AI agent given a business goal -- rebalancing the quarterly marketing budget, drafting a compliance report, routing a vendor contract -- breaks that goal into subtasks, retrieves information from across enterprise systems and executes actions without waiting for a human to evaluate each step. As a result, errors in an agentic workflow compound across steps and may not surface until the action is already complete. The CDO's traditional toolkit was not built for this. Conventional data governance answered specific questions: what data exists, where does it live, is it accurate, and who is authorized to see it? Data catalogs and <a href="https://www.techtarget.com/searchdatamanagement/definition/master-data-management">master data management</a>  <a href="https://www.techtarget.com/searchdatamanagement/definition/master-data-management"></a>programs answered those questions well for structured data in reporting environments, where a human analyst received the output and applied judgment. Context architecture for agentic AI must answer a different question: which data, in what combination, with what business definitions, should an AI system retrieve at the time of query to produce a correct result? Context is the defining challenge here. </section> <section class="section main-article-chapter" data-menu-title="Context and the CDO"> <h2 class="section-title">Context and the CDO</h2> A data catalog shows users that policy documents exist in a given folder. It does not tell an AI agent which of several versions of that document is authoritative, or that the finance team's definition of revenue excludes deferred recognition while the sales team's does not. Increasingly, CDOs must deliver and maintain a <a href="https://www.techtarget.com/searchenterpriseai/tip/Advantages-of-a-semantic-layer-for-enterprise-AI">semantic layer to capture those distinctions</a>. For example, an agent reviewing a supplier evaluation may read from an enterprise knowledge base that has versions from both 2023 and 2026. How does it know that the 2026 version is authoritative, especially if it has not been given a rule to prefer the most recent version? The agent may use both versions to draft a report that's clear, well-written and professionally formatted. The output contains no hallucinations: the agent executed perfectly, but on <a href="https://www.techtarget.com/searchdatamanagement/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist">unvalidated context</a>. The distinction is important because these two kinds of issues point to different parts of the organization. Accountability for AI outputs depends on someone owning the context layer. In too many enterprises, no single executive owns that authority. If marketing spins up customer sentiment agents while finance builds forecasting agents, then each may apply different definitions of the same business terms, different standards for what counts as an authoritative source and different thresholds for data freshness. The CDO's concern is governing what agents retrieve, a business-critical mandate defined by the following four capabilities: <ul type="disc" class="default-list"> <li>Inventory. An enterprise ontology or <a href="https://www.techtarget.com/searchenterpriseai/definition/knowledge-graph-in-ML">knowledge graph</a> maps every data asset while recording relationships between documents, databases, agents, and the sources that feed them. This may include vector databases or unstructured data that traditional data catalogs did not reach. Agents should not retrieve what they cannot understand.</li> <li>Access control. Agents can accidentally combine information that humans would never be permitted to view in a single session. Application-layer security, such as the permissions Salesforce enforces on CRM records or SharePoint enforces on documents, governs what a human user can open when they log into a specific system. But an AI agent may simultaneously retrieve and assemble data from multiple systems. Context-layer access control prevents an agent from combining data that no individual human would have been permitted to combine.</li> <li>Governed delivery. Context must reach agents without centralizing every system. Model Context Protocol (MCP) -- an open standard with rapidly growing adoption across AI vendors -- enables an agent to query enterprise data sources and receive governed, logged and auditable responses in real time, without the organization first moving all that data into a centralized store.</li> <li>Observability. Continuous monitoring of how agents behave, what they retrieve and why they make specific choices. Of the four capabilities, this one most deserves a close look.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Observability"> <h2 class="section-title">Observability</h2> For both auditability and continuous development, organizations must understand why an agent made particular choices. Traditional data observability, tracking data quality as it moves through pipelines, and standard LLM observability, tracking prompts and token usage, are no longer sufficient on their own. The table below shows the differences between these forms of observability,  illustrating the changing demands on the CDO. <iframe title="" aria-label="Table" id="datawrapper-chart-Eajgi" src="https://datawrapper.dwcdn.net/Eajgi/1/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="336" data-external="1"></iframe> <script type="text/javascript">(function(){function e(){window.addEventListener(`message`,function(e){if(e.data[`datawrapper-height`]!==void 0){var t=document.querySelectorAll(`iframe`);for(var n in e.data[`datawrapper-height`])for(var r=0,i;i=t[r];r++)if(i.contentWindow===e.source){var a=e.data[`datawrapper-height`][n]+`px`;i.style.height=a}}})}e()})();</script> </section> <section class="section main-article-chapter" data-menu-title="The semantic layer"> <h2 class="section-title">The semantic layer</h2> The semantic layer translates governed data into AI-readable context and is critical to all four capabilities. It tells the agent retrieving revenue data whether that term means gross revenue, net revenue or revenue recognized under the organization's specific accounting policy. The semantic layer explicitly records these distinctions, makes them consistently available across data systems and delivers data to the agent with that precision intact. Without semantics, <a href="https://www.techtarget.com/searchdatamanagement/feature/Why-enterprise-AI-depends-on-the-semantic-layer">agents guess the meaning of business terms</a> based on their model's general training, rather than on specific business vocabulary. As Fern Halper, senior research director of TDWI, put it, effective organizations "formalize an enterprise business vocabulary and explicitly encode business rules and logic so that models operate within defined guardrails rather than inferred assumptions." Human validation of those definitions remains part of that process, because the semantic layer is only as reliable as the business logic people have approved and recorded in it. If an agent measures its performance against KPIs that influence operational or strategic decisions, someone needs to verify that those KPIs are properly defined and managed. </section> <section class="section main-article-chapter" data-menu-title="The new CDO"> <h2 class="section-title">The new CDO</h2> CDOs who can build and manage this infrastructure of context transform their businesses. An enterprise with a mature context layer dramatically shortens the time required to deploy new agents safely. The CDO who can own this infrastructure becomes the executive who manages what AI knows about the business. That sets the boundary of what the enterprise is capable of. Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle. </section> Agentic AI demands that CDOs move beyond cataloging to curating the context AI systems retrieve, creating a new accountability layer for data governance. https://cdn.ttgtmedia.com/rms/onlineimages/folder-files13.jpg https://www.techtarget.com/data-technologies/feature/The-CDOs-new-role-is-curating-context-for-data-governance Wed, 29 Jul 2026 11:00:00 GMT The CDO's new role is curating context for data governance Nimble on Wednesday launched Web Search Agents, new agentic AI applications that autonomously learn customer use cases to scour the internet for relevant information that can be converted into curated data tables. While analytics and AI tools are largely informed by an enterprise's operational data, information from the web is also valuable when building applications that inform decisions and business processes. Operational data reveals what is happening within an organization, while web data shows what is taking place externally. Together, the two provide <a href="https://www.techtarget.com/searchdatamanagement/opinion/Context-is-the-make-or-break-layer-for-AI-in-production">richer context for agents</a> and other decision-informing tools than one type of data alone. "Web data is the world's largest real-time signal network," Michael Ni, an analyst at Constellation Research, told TechTarget. "Nimble allows enterprises to combine internal and external data, enabling AI to move from internally informed analysis to context-aware decisions." Use cases include real time competitive intelligence, such as dynamic pricing and product availability, examining market and sentiment signals, insight into competitors, and <a href="https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker">ensuring regulatory compliance</a>. Nimble's Web Search Agents are designed to automate much of the research work that previously had to be performed by humans, building and maintaining complex <a href="https://www.techtarget.com/whatis/feature/How-to-scrape-data-from-a-website">web-scraping tools</a>. In addition, Nimble's agents are designed to self-learn a user's domain to perform web data research with the accuracy of an expert, but with greater efficiency.   As a result, Web Search Agents are a potentially significant addition for Nimble customers, according to Donald Farmer, founder and principal of TreeHive Strategy. "There has always been a bottleneck accessing web data because it meant building and maintaining brittle scraping scripts and managing proxies," he told TechTarget. "Agents should autonomously … remove that bottleneck." Based in New York City, Nimble is a web data search specialist that <a href="https://www.techtarget.com/searchdatamanagement/news/366639416/Web-data-curator-Nimble-secures-47M-to-fuel-growth">raised $47 million</a> in venture capital funding in February to fuel its growth. In addition to Nimble, vendors that specialize in enabling users to pull data from the web include Apify, Bright Data, Grepsr, MixRank and <a href="https://www.computerweekly.com/blog/Data-Matters/Giving-ourselves-the-chance-to-lead-the-AI-race-and-stay-ethical-too">Oxylabs</a>. <section class="section main-article-chapter" data-menu-title="Aiding AI"> <h2 class="section-title">Aiding AI</h2> Agents and other AI tools require reams of contextually relevant information to deliver accurate outputs at a high enough rate that they can be <a href="https://www.techtarget.com/searchcio/feature/Startup-founder-says-trust-is-biggest-barrier-to-AI-agents">trusted in production</a>. Despite continually <a href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html">rising investments</a> in AI development, <a href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE">more projects fail</a> than succeed. Frequently, difficulties discovering and connecting agents with the context that enables them to perform properly  prevent AI initiatives from moving beyond the pilot stage. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> There has always been a bottleneck accessing web data because it meant building and maintaining brittle scraping scripts and managing proxies. Agents should autonomously … remove that bottleneck. </figure> <figcaption> Donald FarmerFounder and principal, TreeHive Strategy </figcaption> </div> </blockquote> With context critical to developing trustworthy AI tools, many vendors have introduced products in 2026 designed to help customers discover and operationalize relevant data. In June alone, <a href="https://www.techtarget.com/searchdatamanagement/news/366644853/AWS-latest-to-introduce-context-layer-for-agentic-AI">AWS</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">Databricks</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366643955/Microsoft-boosts-Fabric-to-make-it-a-foundation-for-AI">Microsoft</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake</a> unveiled new features aimed at connecting agents with context. With Web Data Agents, Nimble is similarly providing capabilities aimed at helping customers feed agents with contextually relevant data, in a move motivated by user feedback, according to Uri Knorovich, the vendor's co-founder and CEO. "The feedback we kept hearing was simple -- [customers'] agents could search the web, but they weren't getting the specific data they needed. … Agents need web search that adapts to their specific task, context and objectives to optimize for accuracy and cost, not one-size-fits-all results," he said. Web Search Agents are built on customized search algorithms that enable them to learn autonomously, Knorovich continued. "These advances allow search models to continuously learn a domain, optimize retrieval for a specific use case and deliver … more accurate web context than generic search engines," he said. Beyond accurately finding and retrieving relevant web data, Nimble's Web Search Agents are designed to <a href="https://www.techtarget.com/searchenterpriseai/tip/Reduce-project-expenses-with-AI-cost-optimization-strategies">reduce the cost</a> of searching the internet for task-specific context. Using generic search engines that are task-agnostic can become expensive, according to Farmer, who noted that such tools often return massive, unstructured amounts of text that, when processed by an agent, consumes a high number of <a href="https://www.techtarget.com/whatis/definition/context-window">context window</a> tokens. In benchmark testing conducted by Nimble but not independently verified, Web Search Agents increased answer quality by over 20% and approximately halved the number of tokens spent on each query. "A smart agent retrieving data should therefore be a great advantage," Farmer said. However, while valuable for Nimble customers, Web Search Agents are not the only such web data discovery and retrieval tools on the market, he continued. For example, Grepsr and MixRank both offer AI-powered web scraping capabilities that can discover and deliver task-specific data. However, Farmer pointed out that there is slight differentiation, since Nimble's Web Search Agents deploy <a href="https://www.techtarget.com/whatis/definition/headless-browser">headless browsers</a> that pull the freshest version of a web page instead of a data <a href="https://www.techtarget.com/searchstorage/definition/cache">cache</a>. "If you need that, it is a compelling feature," Farmer said. "I don't think there's a lot here that is innovative compared to the rest of the market, but the work appears to be solid." Ni similarly noted that Nimble's web data collection capabilities are not unique. Instead, the vendor has the potential to distinguish itself by combining live access to web data, agent-powered navigation and repeatable output into a managed external sensing layer, he continued. "The proof point will be whether that delivers greater completeness and lower maintenance costs than traditional scraping platforms, vertical data platforms, or AI-native search APIs," Ni said. </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title">Next steps</h2> While operational data remains the focal point for many enterprises, Nimble's leadership believes that web data is becoming the primary source of <a href="https://www.techtarget.com/searchenterpriseai/tip/Exploring-the-context-layer-for-AI-systems">context for agents</a>, according to Knorovich. With that in mind, Nimble plans to make it easier to connect an enterprise's agents with web search by adding and improving self-learning and agent memory capabilities so Web Search Agents better understandtasks and get smarter with each use. "The bigger vision is a web that agents can navigate as fluently as people do, and we intend to keep building toward that," Knorovich said. Ni noted that Nimble is positioning itself as an enterprise-grade layer for providing external context to agents. Going forward, its opportunity to continue serving existing users and attract new ones is by turning that layer into <a target="_blank" href="https://www.linkedin.com/pulse/rise-ai-control-planes-governing-models-agents-scale-mahmoud-abufadda-d1f5f/" rel="noopener">a control plane</a> for external context. "Parallel to the growth of internal context layers, enterprise leaders will want to know where any specific fact came from, how fresh and reliable it is, whether sources conflict, and whether an agent is permitted to act on it given a specific context," Ni said. "Productizing context controls would expand Nimble’s relevance from developers to CDAOs and CAIOs." 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 Web Search Agents autonomously learn domain-specific use cases to help enterprises collect contextually relevant information for AI and analytics. https://cdn.ttgtmedia.com/rms/onlineimages/telecommunications_g1189468316.jpg https://www.techtarget.com/data-technologies/news/366646240/Nimble-launches-agents-to-cull-relevant-web-data-for-AI Wed, 29 Jul 2026 09:00:00 GMT Nimble launches agents to cull relevant web data for AI Lack of context is the enemy of accuracy. After <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-quality-fast-failures-and-quick-wins-key-to-AI-success">experimenting with agentic AI</a>, many enterprises are attempting to move projects past the pilot stage and into production to finally realize the productivity gains and <a href="https://www.techtarget.com/searcherp/feature/AI-feature-spend-is-the-new-software-cost-control-problem">cost savings</a> of deploying agentic AI networks. However, to reap the potential rewards of agentic AI and avoid serious consequences, organizations need to make sure they're developing agents that can be trusted to perform in production. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/farmer_donald.jpg" alt="Donald Farmer, founder and principal of TreeHive Strategy">Donald Farmer </div> Enterprises need to provide AI tools with the context -- the data and <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- that enables them to accurately carry out their intended work. If they don't, agents could wind up costing businesses time,  money and, in some cases, reputational harm or legal peril. "With good context, an agent can map vague natural language requests to exact corporate data," Donald Farmer, founder and principal of TreeHive Strategy, told TechTarget. "Without it, the agent can only act generically and may hallucinate what it does not know." <section class="section main-article-chapter" data-menu-title="Investments in AI continue to rise"> <h2 class="section-title">Investments in AI continue to rise</h2> In January, research and advisory firm <a 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" target="_blank" rel="noopener">Gartner predicted</a> that worldwide spending on AI will total $2.5 trillion in 2026, up 44% from $1.8 trillion in 2025, and increase to $3.3 trillion in 2027. Simultaneously, some enterprises are putting at least some agents into production as they try to benefit from AI's potential. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> With good context, an agent can map vague natural language requests to exact corporate data. Without it, the agent can only act generically and may hallucinate what it does not know. </figure> <figcaption> Donald FarmerFounder and principal, TreeHive Strategy </figcaption> </div> </blockquote> ISG Software Research's year-end 2024 survey found that at the time, only 17% of AI initiatives reached production, according to ISG analyst David Meninger. One year later, the firm's year-end 2025 survey showed about one-third of AI projects making it into production. Similarly, <a href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE" target="_blank" rel="noopener">Deloitte's State of AI in the Enterprise report</a>, published in January, found that one-quarter of the 3,200 business and IT leaders surveyed reported having put 40% of their AI projects into production. In addition, Deloitte predicted that about half of the organizations it surveyed would reach that 40% threshold by midyear. "We are making significant progress with agents -- we have more enterprises putting agents into production -- but that still leaves a lot that aren't in production," Menninger said. "If we are doubling the number of agents going into production, we're paying more attention to the issues surrounding production quality agents." <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/menninger_david.jpg" alt="ISG Software Research analyst David Menninger">David Menninger </div> In other words, many organizations are paying more attention to context to realize agentic AI's potential. However, it's imperative that they get the context aspect of development correct, Menninger continued. If agents are asked to execute straightforward tasks such as drafting a document, accuracy isn't imperative. Mistakes can be caught, and the consequences aren't catastrophic. But if <a href="https://www.techtarget.com/searchapparchitecture/tip/Multi-agent-architectures-How-coordinated-AI-systems-scale">multi-agent systems</a> are managing entire supply chains or making <a href="https://www.techtarget.com/healthtechanalytics/feature/LLMs-struggle-with-clinical-reasoning-study-finds">recommendations to medical workers</a>, government employees or other people doing critical work, the consequences of agents acting without proper context can be severe. "Now that we're starting to do more agentic activities … we need to get it right, and the risks are higher," Menninger said. </section> <section class="section main-article-chapter" data-menu-title="Real-world consequences"> <h2 class="section-title">Real-world consequences</h2> Enterprises are spending vast amounts of money and devoting significant time and effort to building agents because the potential benefits of deploying networks of AI agents are massive. For example, <a href="https://arxiv.org/pdf/2503.18238" target="_blank" rel="noopener">authors from Johns Hopkins and MIT</a> reported that teams comprised of humans and agents were 60% more productive than teams made up of humans alone. Meanwhile, <a href="https://www.randgroup.com/insights/services/ai-machine-learning/how-much-does-ai-save-a-company/">a separate study</a> conducted by Rand Group found that agents can help enterprises reduce spending by 40%. But there are repercussions when AI tools lack appropriate context. "Context is given to the agent to ensure accuracy," Cindi Howson, chief data and AI strategist at ThoughtSpot, told TechTarget. "In the absence of context, you get hallucinations. The agent takes its best guess, but it is just a guess." Real-world <a href="https://www.evidentlyai.com/blog/ai-hallucinations-examples" target="_blank" rel="noopener">examples of AI hallucinations</a> causing harm include the following: <ul type="disc" class="default-list"> <li>Air Canada suffered reputational harm and was forced to refund money when an AI chatbot didn't know the company's policy related to bereavement travel and misled a passenger.</li> <li>Google's parent company, Alphabet, lost $100 billion in market value after incorrect AI-generated content was included in a promotional video.</li> <li>OpenAI's Whisper, used by tens of thousands of medical professionals to transcribe patient visits, made up entire sentences, invented medication names and attributed racially charged statements to patients with aphasia, a disorder that impairs the ability to speak.</li> <li>A California court found that agents from Perplexity <a href="https://www.reuters.com/legal/litigation/amazon-wins-order-blocking-access-perplexitys-ai-shopping-agent-2026-03-10/" target="_blank" rel="noopener">may be violating state and federal laws</a> by accessing Amazon accounts without prior authorization.</li> </ul> Beyond public consequences, there are private ones when agents don't have the context they need to deliver accurate outputs, Howson noted, pointing out that organizations are charged for LLM use by the number of <a href="https://www.theserverside.com/tutorial/An-introduction-to-LLM-tokenization">tokens they consume</a>. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/howson_cindi.jpeg" alt="Cindi Howson, chief data and AI strategist at ThoughtSpot">Cindi Howson </div> "Happening in parallel are the token costs," she said. "The more context you give an LLM, you're going to get a better answer on the first attempt, whereas if the LLM gets it wrong, you're going to run up your token costs and it's not going to be efficient." When an agent cannot be trusted in production, the time and money spent building it will be wasted. Not including all the tools it takes to create an AI infrastructure, the cost to develop a single agent can range from around $1,000 for simple applications to over $100,000, according to IT consulting firm Triple Minds. Differences come down to the scope, autonomy level and <a href="https://www.techtarget.com/searcherp/podcast/Plan-a-multi-agent-orchestration-framework-for-scalable-AI">depth of integration</a> with enterprise systems, including other agents. A basic agent that responds to frequently asked questions might cost around $15,000 to build, while a custom-trained, fully autonomous agent capable of working with other agents could cost over $100,000. "Enterprises want agents that are reliable, with responses that are grounded, properly attributed and consistent in how long they take to run," Michael Bendersky, director of research at Databricks, told TechTarget. "Speed and cost matter as much as accuracy. An agent that [takes] unnecessary steps before reaching the right answer is expensive and unpredictable at scale." </section> <section class="section main-article-chapter" data-menu-title="Success stories"> <h2 class="section-title">Success stories</h2> While failure to connect agents with appropriate context can have consequences, the potential benefits of agentic AI outweigh the risks. Improved efficiency and cost savings are real when agents can instantaneously access the relevant data and business logic they need to be accurate. Over the past few months, as data management and analytics providers have hosted their annual user conferences, they've put on a parade of companies that are among those having early success building, deploying and benefiting from agents. In late March, during one of the spring's first conferences, <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domo-drives-customer-excitement-with-latest-AI-capabilities">analytics vendor Domo</a> introduced Nichole Gunn, CEO of channel marketing specialist Extu. Aided by a specialist from Domo, Gunn used capabilities launched by the vendor during its conference to build an agent in less than 30 minutes that takes on time-consuming operational tasks related to Extu's customers. Gunn, who wanted an agent that eliminated multi-step onboarding tasks that must be repeated for each Extu customer, estimated that the agent <a href="https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/agents-for-growth-turning-ai-promise-into-impact" target="_blank" rel="noopener">would save</a> Extu hundreds of thousands of dollars in operational costs in its first few months in production and millions within six months. The agent not only handles onboarding but provides updates on customers and suggests campaigns to keep customers and partners engaged, according to Gunn. "It went from this one thing that I wanted to a multi-faceted way of saving our company a lot of money," she told TechTarget in March. "We will be using the AI agents immediately when I get home." Throughout April and May, vendors including Alteryx, Informatica, Qlik and Tableau held events to showcase success stories. And just last month, AWS, Databricks, Microsoft and Snowflake closed out the spring conference season with events where they highlighted customers successfully building contextually aware agents and realizing some of agentic AI's potential. One of the customers Snowflake highlighted was <a href="https://www.computerweekly.com/news/366642986/Accenture-joins-IBM-in-battle-for-323m-Post-Office-Horizon-deal">Accenture</a>, a global IT services and consulting firm that is using <a href="https://www.techtarget.com/searchdatamanagement/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake's platform</a> to help its own customers manage their data and build insight-generating analytics and AI tools. For example, a European utility company is using combined capabilities from Snowflake and Accenture to reduce queries from weeks to seconds, according to Accenture chief strategy and services officer Manish Sharma, who spoke with <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571855/Snowflake-CEO-Slootman-steps-down-Ramaswamy-takes-over">Snowflake CEO Sridhar Ramaswamy</a> during the Snowflake Summit keynote address on June 1. Similarly, a U.S. manufacturer is using tools from Snowflake and Accenture to create a unified data layer for analytics and AI after its data was previously fragmented across myriad systems. "There's a simplicity and beauty to all of this," Ramaswamy said. "We succeed when we help our customers make more money or spend less money." 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> When connected with relevant context, agents can increase organizational efficiency and reduce spending. When left to guess, they can cause significant harm. https://cdn.ttgtmedia.com/rms/onlineimages/machine_learning_g1209661950.jpg https://www.techtarget.com/data-technologies/news/366646162/Potential-consequences-are-severe-when-AI-agents-lack-context Wed, 29 Jul 2026 08:00:00 GMT Potential consequences are severe when AI agents lack context The announcements have cascaded like an avalanche. One after another throughout the first six months of 2026, data management vendors unveiled and launched new capabilities designed to connect AI agents with the context needed to perform in production. Just days into January, Databricks and MongoDB each introduced new capabilities aimed at discovering and delivering agents the relevant data and business logic they require. Since then, most product development releases have similarly focused on enabling AI tools to call on contextually appropriate information the instant they need it. The reason is simple: without appropriate context, AI agents will fail. "Without it, they're guessing," Michael Bendersky, director of research at Databricks, told TechTarget. "Guesswork doesn't work for enterprise businesses." <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/bendersky_michael.jpg" alt="Michael Bendersky, director of research at Databricks">Michael Bendersky </div> Agents always attempt to answer questions and act when asked, whether they have the information to do so accurately or not. They make <a href="https://www.techtarget.com/whatis/definition/What-is-AI-inference">inferences</a> based on the context they can access, and if that context isn't relevant, their inference won't be relevant either. Connecting agents with relevant context, however, is complex. Data and <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> need to be prepared for AI, which can be a lengthy, labor-intensive process for massive organizations with data spread across disparate systems. And relevant context needs to be instantly discoverable and retrievable. Otherwise, <a target="_blank" href="https://business.uq.edu.au/momentum/why-80-per-cent-ai-projects-fail" rel="noopener">agent initiatives will break down</a>, either never making it past the pilot stage or being untrustworthy -- and unusable -- in production. As a result, with <a target="_blank" href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE" rel="noopener">most AI projects failing</a> heading into 2026, all other data management trends have receded. <section class="section main-article-chapter" data-menu-title="An array of announcements"> <h2 class="section-title">An array of announcements</h2> Models, as it turned out, weren't the only problem. For AI tools to be trusted, reasoning capabilities, whether provided by large language models (LLMs) such as ChatGPT and Claude or smaller, purpose-built models, need to be strong for an AI tool to respond accurately to queries. However, even as <a target="_blank" href="https://www.vellum.ai/llm-leaderboard" rel="noopener">LLM reasoning capabilities</a> close in on 100% accuracy for basic queries, most AI initiatives still stall. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Without it, they're guessing. Guesswork doesn't work for enterprise businesses. </figure> <figcaption> Michael BenderskyDirector of research, Databricks </figcaption> </div> </blockquote> Context remains the missing piece. "Without context, an agent is at risk of taking the wrong action, not necessarily because it malfunctioned but because it didn't have the information it needed to make the right decision," David Menninger, an analyst at ISG Software Research, told TechTarget. To improve <a href="https://www.techtarget.com/searchdatamanagement/post/Successful-data-analytics-starts-with-the-discovery-process">data discovery</a> and retrieval, data management vendors have centered product development efforts on enabling customers to find and feed agents the context they need. A week after Databricks' early January launch of Instructed Retriever, which improved on traditional <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> (RAG) by adding parameters such as user instructions to searches to enhance retrieval accuracy, MongoDB unveiled new vector embedding and reranking models to improve the relevance of data discovered using <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector search</a>. Among many others, vendors such as Alteryx, AWS, Google Cloud, GoodData, Informatica, Snowflake, Tableau, Teradata and ThoughtSpot have followed suit. Not all have taken the same approach. Databricks aims to simplify and improve data retrieval systems. Like MongoDB, Teradata is among the vendors attempting to improve vector search to discover and operationalize context so Instructed Retriever, RAG and other retrieval pipelines can pull it in. And many are adding or augmenting <a href="https://www.techtarget.com/searchenterpriseai/tip/Advantages-of-a-semantic-layer-for-enterprise-AI">semantic modeling capabilities</a> to enable users to build context layers for AI. In June alone, Databricks, AWS, Microsoft and Snowflake revealed new tools that help customers create context layers for agents. The organizations that proactively went through the painstaking process of putting their data estates in order and building infrastructures that can handle the speed and scale of AI workloads are <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Domo-drives-customer-excitement-with-latest-AI-capabilities">putting at least some agents into production</a>. For example, Deloitte, which in January reported that one-quarter of organizations were able to move 40% of their AI projects into production, predicted that around half would reach that 40% mark by midyear. Similarly, Databricks' 2026 State of AI Agents report, published in January, showed a 327% increase in the number of organizations transitioning from single chatbots to multi-agent systems over the previous four months. "When agents are given clear, sufficient context, they can complete tasks in fewer steps and reach the right data faster, rather than spending cycles exploring dead ends," Bendersky said. Successes, however, still represent a minority. </section> <section class="section main-article-chapter" data-menu-title="Semantic strength"> <h2 class="section-title">Semantic strength</h2> With many enterprises aiming to build broad, <a href="https://www.techtarget.com/searchapparchitecture/tip/Multi-agent-architectures-How-coordinated-AI-systems-scale">multi-agent systems</a>, improving vector search and RAG are key to connecting agents with context. However, within this year's dominant data management trend, semantic modeling has emerged as the most common approach to improving context retrieval. Without semantic layers, LLM accuracy drops sharply when asked to derive outcomes from data distributed across multiple systems. But when <a href="https://www.gartner.com/en/newsroom/press-releases/2026-05-11-gartner-says-lack-of-semantics-causes-inaccurate-artificial-intelligence-agents-and-wasted-spending">semantic layers augment LLM queries</a>, they recover their accuracy. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/howson_cindi.jpeg" alt="Cindi Howson, chief data and AI strategist at ThoughtSpot">Cindi Howson </div> "Semantic layers are the magic," Cindi Howson, chief data and AI strategist at ThoughtSpot, told TechTarget. "[Accuracy] depends on how much context you give agents and how robust the semantic layer is, but semantic layers are the difference. The variability will be the complexity of the data model, as well as how robust that semantic and context layer is." Like vector embeddings, semantic modeling makes data discoverable by assigning definitions and characteristics that ensure consistency across an organization. But similar to vector search and storage, semantic modeling was, until recently, a niche capability used to augment analytics systems rather than a key component. Now, semantic layers are becoming so critical to agent performance that a group of data management and analytics vendors has banded together to simplify semantic modeling by <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">creating an open industry standard</a>. Without one, vendors provide proprietary semantic layers that can't interact with one another, adding complexity rather than reducing it as developers and engineers piece together AI pipelines that connect data stored in myriad systems. "There's the whole notion of what the data means, and we've never been very good at that," Menninger said, noting that there is no single, universal semantic language. "We had the same issue in the BI world as we do in the AI world. … We need a way to describe what the data means." One enterprise deploying a semantic layer to help organize its data and make it available for analytics and AI applications is supply chain management specialist Blue Yonder, which uses semantic modeling provider <a href="https://www.techtarget.com/searchdatamanagement/news/366582098/AtScale-adds-semantic-layer-support-for-AI-GenAI-models">AtScale's platform</a>. With At Scale's capabilities, Blue Yonder in July 2024 rearchitected its previously disparate data using semantic modeling, then connected its unified semantic layer to AI tools with AtScale's Model Context Protocol server to enable agents to call on contextually relevant data. "It allows us to get that clear alignment between the data and the business, and really define everything in one single place," Jeremy Arendt, senior director of analytics engineering at Blue Yonder, said at a session during AtScale's virtual Semantic Layer Summit in May. "The semantic layer became an integral part of making sure we have trust and faith in that data." Now, queries that previously took days to answer happen in <a href="https://www.computerweekly.com/news/366640193/Why-real-time-data-is-key-for-enterprise-AI">near real time</a>, he continued. "Our semantic layer is for everyone in the company who needs data," Arendt said. "We're focusing on enabling self-service [insight generation] at every layer of the business." </section> <section class="section main-article-chapter" data-menu-title="Other tools of the trade"> <h2 class="section-title">Other tools of the trade</h2> Though semantic layers have emerged as a key aspect of context retrieval for AI, they are just one part of a larger whole. Semantic layers map enterprise <a href="https://www.techtarget.com/searchdatamanagement/definition/schema">schemas</a> to make relevant data ready for agents. Also critical are instruction-aware retrieval layers and <a href="https://www.techtarget.com/searchsecurity/tip/How-to-implement-zero-trust-for-AI">zero-trust governance and security capabilities</a> that treat every user, device or application attempting to access an organization's data and business logic, according to Farmer. Data governance establishes policies and standards that determine how an enterprise's data is managed and accessed by users, including agents. Security capabilities, meanwhile, protect organizations from potential misuse by users, including agents, as well as attacks from outside threat actors. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/farmer_donald.jpg" alt="Donald Farmer, founder and principal of TreeHive Strategy ">Donald Farmer </div> Recently, with agents <a href="https://www.techtarget.com/searchdatamanagement/news/366644845/Agents-are-altering-data-security-needs-Oracle-responds">introducing new data security concerns</a> by increasing the scale and speed with which data is accessed and operationalized as well as enabling attackers to increase the speed and scale of threats, Oracle emphasized the need for enhanced security. However, instead of applying security measures to the operating systems historically protected by such tools, Oracle determined that security now needs to be applied at the data layer to better protect against increased threats.  "Advanced retrievers treat business constraints … as strict rules rather than just suggestions for the LLM, [and] zero-trust agent security frameworks ensure that agents inherently use and cannot exceed the security clearance of human users who initialized them," Farmer said. Meanwhile, all the components that comprise a context retrieval system for AI -- those that foster discovery, retrieval and action -- need to be in a single environment, according to Bendersky. If they aren't delivered as a unified capability, connecting agents with context becomes a complex engineering exercise that has to be manually recreated for <a href="https://www.techtarget.com/searchenterpriseai/feature/Real-world-agentic-AI-examples-and-use-cases">each use case</a>. "Context delivery needs to be a platform capability, not an engineering project," Bendersky said. "This means access controls, lineage and auditability need to be built in… because how your agent got its context matters as much as what it 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> With agents requiring situational awareness to be accurate, connecting AI with relevant data and business logic has been an almost singular focus throughout 2026. https://cdn.ttgtmedia.com/rms/onlineimages/machine_learning_g1205538888.jpg https://www.techtarget.com/data-technologies/news/366646161/Data-management-vendors-race-to-connect-AI-with-context Tue, 28 Jul 2026 08:00:00 GMT Data management vendors race to connect AI with context Context makes or breaks an AI agent. It's what gives AI agents the situational awareness to deliver trustworthy, accurate outputs. Without it, all other parts might be in place, but the relevant meaning that powers an agent to perform properly will be absent, dooming it to fail. When provided with proper awareness, agents can <a href="https://www.techtarget.com/healthtechanalytics/feature/LLMs-struggle-with-clinical-reasoning-study-finds">help make medical diagnoses</a>, personalize retail experiences, discover new clients, generate sales campaigns, manage and optimize entire supply chains and monitor vast swaths of data that are impossible for humans to oversee to detect fraud. Sometimes, they're even building other agents. But to deliver those outputs, agents need proper context. They need relevant, high-quality data and task-appropriate <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a>, and the ability to discover and ingest them independently. "It's no different than providing context to an employee doing a job," David Menninger, an analyst at ISG Software Research, told TechTarget. "You wouldn't give a junior analyst a task without giving them some context." <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/menninger_david.jpg" alt="ISG Software Research analyst David Menninger">David Menninger </div> Now, after the laborious task of getting data ready for AI and investing heavily in experimental pilots -- while model reasoning capabilities improved -- some enterprises are ready to put agents into production at scale and reap the benefits. The most advanced organizations already have. But widespread deployment of <a href="https://www.techtarget.com/searchapparchitecture/tip/Multi-agent-architectures-How-coordinated-AI-systems-scale">multi-agent networks</a> requires difficult work that could take years to complete, according to Menninger. "We'll … gradually eat away at the parts that are difficult, and we'll hopefully, in a several year time period, have tackled the biggest challenges," he said. <section class="section main-article-chapter" data-menu-title="AI success amid failures"> <h2 class="section-title">AI success amid failures</h2> Despite the time and money that enterprises have invested in agentic AI development, most AI projects don't make it into production. Studies show that the failure rate is going down, which is evidence that enterprises that have addressed the underlying data issues that often stall AI projects are achieving at least some success connecting AI applications with the context they need to deliver trustworthy outputs. But they also reveal that more AI projects still fail than succeed. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It's no different than providing context to an employee doing a job. You wouldn't give a junior analyst a task without giving them some context. </figure> <figcaption> David MenningerAnalyst, ISG Software Research </figcaption> </div> </blockquote> For example, Deloitte's 2026 <a target="_blank" href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE" rel="noopener">State of AI in the Enterprise report</a>, released in January, showed only one-quarter of the organizations surveyed have been able to move 40% of their AI experiments -- whether single agents, multi-agent systems, chatbots, traditional machine learning projects or other pilots -- into production. Similarly, Menninger noted that ISG's year-end 2025 research found that about one-third of the 1,200 projects it examined are moving into production, an improvement from 17% a year earlier but still well under 50%. "[Some] enterprises are no longer experimenting with agents," Michael Bendersky, director of research at Databricks, told TechTarget. "They're deploying them for real, complex work." Databricks' own <a target="_blank" href="https://www.databricks.com/resources/ebook/state-of-ai-agents" rel="noopener">2026 State of Agents report</a>, published in January, found a 327% increase in usage of multi-agent systems built on domain intelligence over the previous four months, demonstrating that some enterprises -- after there were almost none until late 2025 -- have built at least remedial agentic systems. Nevertheless, long-term challenges to building broad networks of context-aware agents remain. </section> <section class="section main-article-chapter" data-menu-title="Progress despite problems"> <h2 class="section-title">Progress despite problems</h2> One challenge to putting multi-agent systems into production is the completeness and quality of the AI workflow, according to Bendersky. Humans used to perform all aspects of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a>, retrieval and operationalization for analytics and AI, and they had time to ensure the quality of all parts of the pipelines that connect data with applications. Once they're in production, agents are the ones calling on context, and for them to do so properly, everything they require for accuracy must be ready the instant they need it. Enterprises that haven't already done so need to modernize -- and in some cases, overhaul -- their data and AI infrastructures, which can be <a href="https://www.techtarget.com/searchenterpriseai/feature/The-dollars-and-sense-of-implementing-AI">time-consuming and costly</a> depending on how much needs to be revamped. To connect agents with context, infrastructures need tools that improve data discovery to ensure agents call on context-appropriate information. Among others, they include the following: <ul class="default-list"> <li>Vector embedding and reranking models.</li> <li>Semantic layers and <a href="https://www.techtarget.com/searchdatamanagement/feature/Hybrid-search-demands-reshape-retrieval-frameworks-for-AI">data retrieval</a> engines that are purpose-built for AI workloads rather than BI tasks.</li> <li>Security capabilities that address new risks posed by agents.</li> <li>Governance frameworks that oversee not only data but also agent behavior.</li> </ul> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/bendersky_michael.jpg" alt="Michael Bendersky, director of research at Databricks">Michael Bendersky </div> "What will still remain difficult is the environment," Bendersky said. "We are moving from an enterprise where most data access is done by humans to one where interactions with the data are done by agents. This will require continued investment in architectures where the right context is accessible to the agent at the right time. Governance, platforms, auditability, etc., will continue being challenging." In particular, <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems">semantic modeling capabilities</a> are proving to be a crucial enabler for enterprises successfully building agents and need to be part of an organization's AI workflow, according to Cindi Howson, chief data and AI strategist at ThoughtSpot. Model reasoning capabilities have improved to the point that when large language models (LLMs) are asked questions that require them to call on limited amounts of contained data, they are 95% accurate. However, <a target="_blank" href="https://axiussdc.substack.com/p/the-7-table-fallacy-why-text-to-sql?utm_campaign=post&utm_medium=web&triedRedirect=true" rel="noopener">a September 2025 study</a> demonstrated that when LLMs are asked to derive outcomes based on broad swaths of data spread across myriad systems, their accuracy falls to around 50% or lower. A separate <a target="_blank" href="https://docs.getdbt.com/blog/semantic-layer-vs-text-to-sql-2026?version=2.0&name=Fusion" rel="noopener">April 2026 study</a> by DBT Labs -- which provides semantic modeling capabilities -- showed that accuracy again approaches 100% when LLM queries, often using traditional <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> (RAG) pipelines to connect models with AI tools, are augmented by a semantic layer. "Some organizations thought they could just use a basic RAG approach, and it's not happening -- it's just not good enough," Howson told TechTarget. "[They need] semantic layers and context layers, and humans in the loop to coach agents." </section> <section class="section main-article-chapter" data-menu-title="The next challenge(s)"> <h2 class="section-title">The next challenge(s)</h2> While known problems slowly get solved, new ones constantly emerge. For example, connecting agents with context only became the dominant trend in data management and AI at the start of 2026 after unreliable model reasoning was viewed as the initial culprit holding back agentic AI development. Only once <a target="_blank" href="https://www.vellum.ai/llm-leaderboard" rel="noopener">model reasoning capabilities</a> reached a certain threshold and agents still weren't consistently delivering accurate outputs did context become the primary focus, with a litany of vendors introducing tools to connect agents and context. Now, data and AI infrastructures that don't effectively connect agents with context are a principal focus, but as Menninger noted, it will take time to solve on a widespread basis. In the interim, Incremental improvements will be made that enable more enterprises than those now at the forefront to move agents into production and perhaps even build multi-agent networks. "We'll be making progress, identifying the bigger challenges and devoting research to solving those," Menninger said. But what then? Assuming a combination of enterprise investment and technological advancement continues to result in a rising AI development success rate, <a href="https://www.techtarget.com/searchapparchitecture/tip/Multi-agent-architectures-How-coordinated-AI-systems-scale">managing systems</a> that include thousands of autonomous agents will be a significant challenge, according to Donald Farmer, founder and principal of TreeHive Strategy. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/farmer_donald.jpg" alt="Donald Farmer, founder and principal of TreeHive Strategy">Donald Farmer </div> "Multi-agent systems -- or swarms – [are one emerging issue]," he told TechTarget. Agents within connected systems are meant to work together, to collaborate on a scale beyond human capacity. But for them to do so without overstepping boundaries, without exposing sensitive information, without accessing data they're not authorized to operationalize, without violating regulatory statutes, without breaching <a href="https://www.techtarget.com/whatis/definition/data-sovereignty">data sovereignty</a> rules -- without behaving exactly as intended -- is challenging.  "Once individual agents have great context, [organizations have to figure out] how do you get an accounting agent, a legal agent and a procurement agent to collaborate, negotiate and hand off tasks to one another without human intervention or emergent anomalies," Farmer said. Another issue enterprises will have to address is how much responsibility to give agents and how much to keep under human supervision, according to Menninger. He noted that ISG research shows that of those agents currently in production, 50% assist humans, 40% work in conjunction with humans but will eventually be empowered to act autonomously, and 10% are fully autonomous. Figuring out <a href="https://www.techtarget.com/searchdatacenter/tip/AI-operating-models-Balancing-autonomy-and-human-oversight">the right balance</a> will be critical for enterprises to derive the benefits of agentic AI without suffering accidental consequences. "There's always going to be a next problem or opportunity," Menninger 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> Awareness enabled by appropriate data and business logic is one of the differences between agentic applications that properly perform and those that never reach production. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20learning_g1186820873.jpg https://www.techtarget.com/data-technologies/news/366646252/Context-is-king-as-agents-evolve-but-problems-persist Mon, 27 Jul 2026 08:00:00 GMT Context is king as agents evolve, but problems persist Oracle is taking aim at rising concerns over cost control and data residency with its latest AI database. Introduced on July 22, Oracle Base Database Cloud@Customer is a scaled down version of Oracle's Exadata Cloud@Customer database. The new offering is purpose-built for small and mid-sized data workloads, so customers don't have to use the compute power of databases <a href="https://www.techtarget.com/searchdatamanagement/news/366617783/Oracle-Exadata-update-boosts-performance-to-meet-AI-needs">optimized for large-scale workloads</a>. In addition, the new AI database is designed to enable Oracle customers to run their data workloads in their locations of choice. Driven by AI development, which requires far more data than analytics or traditional machine learning projects, <a href="https://www.techtarget.com/searchcloudcomputing/tip/3-factors-that-influence-multi-cloud-cost-optimization">the cost of data management workloads</a> is on the rise. Meanwhile, as enterprises build agents and other AI applications that autonomously access and replicate data across multiple regions, <a href="https://www.techtarget.com/searchcloudcomputing/definition/data-residency">data residency</a> concerns are growing. Given that Base Database Cloud@Customer addresses both, it is a valuable addition for existing Oracle AI Database users as well as potential new ones that don't need to run massive-scale workloads, according to Holger Mueller, an analyst at Constellation Research. "It is a key step to run a less complex version of the Oracle Database," he told TechTarget. "It creates value for those customers who want or need to run things on premises. Simpler is also a key value proposition for small and mid-sized businesses. So, all in all, a very good move." Stephen Catanzano, an analyst at Omdia, a division of TechTarget, similarly noted the value of the newest addition to Oracle's <a href="https://www.techtarget.com/searchdatamanagement/news/366640598/Oracle-AI-Database-update-aims-to-ease-developing-agents">lineup of AI Databases</a>. "Oracle Base Database Cloud@Customer is a solid addition because it extends Oracle's cloud operating model to mid-sized workloads that previously had no good hybrid option," he told TechTarget. Previously, potential customers with data residency requirements or latency-sensitive applications at remote sites were forced to use traditional on-premises deployments or databases such as Exadata Cloud@Customer that were more powerful -- and expensive -- than they needed, Catanzano continued. "This matters because it democratizes hybrid cloud access for smaller organizations, departments, and remote facilities that need to keep data local but want cloud benefits like automated management, pay-per-use pricing, and simplified operations," he said. <section class="section main-article-chapter" data-menu-title="Addressing user needs"> <h2 class="section-title">Addressing user needs</h2> Cost control is <a href="https://www.techtarget.com/searcherp/feature/AI-feature-spend-is-the-new-software-cost-control-problem">a continuing concern</a> for many enterprises as they increase their investments in AI development and move more of their operations to the cloud, with <a href="https://www.gartner.com/en/newsroom/press-releases/2024-11-05-gartner-says-cios-need-to-overcome-four-emerging-challenges-to-deliver-value-with-artificial-intelligence#:~:text=The%20Cost%20of%20AI%20Can,risk%20as%20security%20or%20hallucinations.">Gartner finding</a> that managing the expense of AI development limits what some organizations can build. In response, some data management vendors are introducing performance improvements, new pricing models and other features designed to help customers curb some of their spending. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It is a key step to run a less complex version of the Oracle Database. It creates value for those customers who want or need to run things on premises. Simpler is also a key value proposition for small and mid-sized businesses. So, all in all, a very good move. </figure> <figcaption> Holger MuellerAnalyst, Constellation Research </figcaption> </div> </blockquote> For example, <a href="https://www.techtarget.com/searchdatamanagement/news/366635663/Latest-AWS-data-management-features-target-cost-control">AWS</a> in December 2025 unveiled new pricing for its databases and capabilities that lowered the cost of storing and searching vectors. Similarly, <a href="https://www.techtarget.com/searchdatamanagement/news/366629235/Aerospike-update-aims-to-improve-database-performance">Aerospike</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366630145/Neo4js-latest-targets-graph-database-performance-at-scale">Neo4j</a>, Oracle and <a href="https://www.techtarget.com/searchdatamanagement/news/366626143/SingleStore-boosts-database-performance-to-meet-AI-needs">SingleStore</a> have all made performance improvements to their databases to improve their efficiency. Now, Oracle is further addressing cost control with Base Database Cloud@Customer in a move motivated by customer feedback, according to Ashish Ray, Oracle's senior vice president of database product management. "Enterprise customers of Exadata Cloud@Customer who rely on it for their mission-critical, large-scale datacenter workloads have been looking for a smaller form factor Cloud@Customer product for their satellite locations, warehouses and regional offices," he said. Growing <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-navigate-data-sovereignty-for-AI-compliance">concerns over data residency</a> were an additional factor for Oracle when developing latest AI database, Ray continued. "Organizations are facing data residency mandates and want the same cloud control plane … and highly efficient storage technologies to deploy their private AI workloads anywhere they require," he said. Base Database Cloud@Customer is an AI database and hybrid cloud tool that runs on Oracle Data Infrastructure Cloud@Customer X11, a managed hybrid cloud infrastructure accessed through a simple <a href="https://www.techtarget.com/searchcloudcomputing/definition/subscription-based-pricing-model">subscription-based pricing model</a>. The infrastructure includes two servers, so workloads can continue running despite unplanned power outages and planned maintenance. In addition, because Base Database Cloud@Customer runs on the same infrastructure as other Oracle AI capabilities, it can be used in conjunction with Oracle AI Database 26ai and Oracle Database 19c, as well as AI development tools such as the following: <ul type="disc" class="default-list"> <li>AI Vector Search so users can operationalize both structured and <a href="https://www.techtarget.com/searchdatamanagement/feature/Unstructured-data-needed-but-often-untapped-for-agentic-AI">unstructured data</a> for AI.</li> <li>AI Database Private Agent Factory to easily build and deploy agents.</li> <li>Private AI Services Container to secure data and enforce data residency requirements.</li> </ul> The result is that Base Database Cloud@Customer users can deploy complete agentic workloads in controlled environments. "What stands out is the unified infrastructure that lets customers run databases, applications and agents on the same hardware behind their firewall," Catanzano said. "The ability to deploy complete agentic AI workloads with private data entirely at customer locations addresses a critical gap in the market, where organizations want AI capabilities but cannot send sensitive data to external cloud providers." Matt Aslett, an analyst at ISG Software Research, similarly noted that the biggest benefit of adding Base Database Cloud@Customer isn't necessarily the issues it addresses -- cost and data residency -- but instead that it gives <a href="https://www.techtarget.com/searchcloudcomputing/news/366610596/Oracle-users-share-challenges-successes-with-cloud-AI">Oracle users</a> another option when selecting an appropriate database for their work. "The new product expands the breadth of scalability options available for customers deploying data and AI workloads in hybrid and on-premises environments, … without having to commit to the scalability provided by Oracle Autonomous Database on Exadata Cloud@Customer," he told TechTarget. </section> <section class="section main-article-chapter" data-menu-title="Setting the pace"> <h2 class="section-title">Setting the pace</h2> Beyond the benefit to users, Base Database Cloud@Customer is evidence of Oracle's innovation, according to Mueller. He noted that competitors, including <a href="https://www.techtarget.com/searchbusinessanalytics/news/366624233/New-Microsoft-database-analytics-tools-target-agentic-AI">Microsoft</a> and IBM -- among others -- offer on-premises database capabilities. But those on-premises databases are designed for large-scale workloads, like most cloud databases now optimizing for AI rather than scaled down versions of a more powerful offering. Meanwhile, other Oracle databases also help differentiate the tech giant from its peers, Mueller continued. "Oracle is leading," Mueller said. "Oracle Autonomous Database is eight years old, and the equivalent initial offerings of, for example, IBM and Microsoft are only coming out now." Catanzano likewise noted that Base Database Cloud@Customer is different than other <a href="https://www.techtarget.com/searchdatamanagement/opinion/The-database-is-the-new-battleground-for-enterprise-AI">database offerings</a>. He pointed out that other vendors offer public cloud database services or on-premises offerings, but Oracle is unique by providing integrated hybrid cloud database capabilities with a fully managed infrastructure. For example, Microsoft offers Azure SQL and <a href="https://www.techtarget.com/searchdatamanagement/news/366577632/Vector-search-and-storage-key-to-AWS-database-strategy">AWS</a> provides RDS, both of which are hybrid cloud databases that compete with Oracle's offerings, but neither integrates with AI capabilities running on the same infrastructure. "This matters because the market is fragmenting between cloud-first vendors and traditional database companies, and Oracle is carving out a middle path that acknowledges real-world constraints while delivering genuine cloud benefits," Catanzano said. While Oracle may have certain competitive advantages, most data and AI providers are now <a href="https://www.computerweekly.com/feature/Breaking-the-stranglehold-Responses-to-data-sovereignty-risk">addressing data residency requirements</a> by providing numerous deployment choices, according to Aslett. However, Oracle continues to distinguish itself with its level of integration, he continued. "Oracle has a distinct advantage over many of its software-focused competitors by virtue of its ability to address the full combination of AI and data requirements through integrated AI and data platforms, as well as its investment in Cloud@Customer infrastructure," Aslett said. </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> As Oracle plots its AI Database strategy for the second half of 2026, <a href="https://www.techtarget.com/searchsecurity/feature/Best-practices-to-secure-data-at-rest-in-use-and-in-motion">data security</a> features prominently, according to Ray. Agentic AI is leading to new threats by enabling attackers to discover vulnerabilities and <a href="https://www.computerweekly.com/news/366642503/AI-is-widening-the-asymmetry-between-attackers-and-defenders">automate attacks</a> at exponentially greater speed than in the past. In addition, because agents autonomously access data at greater speed and scale than humans, they have the potential to expose security gaps or create new ones. "As AI accelerates the need for stronger security, Oracle remains focused on helping organizations secure data at the source, secure data at speed and secure data through resilience," Ray said. "By making it easier for organizations to assess, patch, upgrade and protect their databases, Oracle is helping them build a secure foundation for the next generation of AI-driven applications." Mueller noted that Oracle would be wise to focus on <a href="https://www.techtarget.com/searchdatamanagement/news/366583139/Oracle-adds-vector-search-capabilities-to-database-platform">improving the vector embedding</a> capabilities in its AI Data Lakehouse that are often used in conjunction with transactional databases. "It's not really new, but [Oracle has] been relatively quiet on the topic," he said. Catanzano, meanwhile, suggested that Oracle expand its ecosystem of prebuilt AI agents and industry-specific agentic AI development templates in Private Agent Factory, so users can more easily derive value from their <a 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">investments in AI</a>. "They could also introduce more granular consumption tiers and multi-site orchestration capabilities that let organizations manage fleets of hybrid cloud deployments across dozens or hundreds of locations from a central control plane," he said. "The current launch addresses the technical foundation, but the next wave of adoption will come from reducing time-to-value and operational overhead at scale." 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 tech giant's latest offering is a scaled-down version of its Exadata platform and helps differentiate its lineup of databases from those of competing providers. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1078919244.jpg https://www.techtarget.com/data-technologies/news/366646237/New-Oracle-AI-database-targets-cost-data-residency-concerns Thu, 23 Jul 2026 14:33:00 GMT New Oracle AI database targets cost, data residency concerns Most data governance programs fail not because of bad tooling or weak culture, but because they lack financial ownership. A governance council might meet monthly, but the glossary remains unpublished, steward roles are assigned but never activated, and budgets stay constrained. Without financial authority behind the program, governance becomes a peripheral activity. Data governance is more than a technical concern. As finance takes the lead in AI, planning and risk, the distinction between financial data and operational data is disappearing, and governance gaps carry direct financial consequences. <section class="section main-article-chapter" data-menu-title="Why data governance programs fail without CFO ownership"> <h2 class="section-title">Why data governance programs fail without CFO ownership</h2> If governance is <a href="https://www.techtarget.com/searchdatamanagement/tip/5-data-governance-framework-examples">managed by IT or a central data office</a>, the program can set standards, publish policies and recommend controls. However, it cannot reliably force business units to absorb friction, change processes or invest in cleanups that do not bring immediate local payoff. At this point, most programs stop. Corporate governance and risk management are trending concerns among finance executives. A <a href="https://www.gartner.com/en/newsroom/press-releases/2026-02-10-gartner-research-reveals-cfos-budget-plans-prioritize-grotwth-functions-tech-and-ai-in-2026">Gartner Research press release</a> revealed that three-quarters of CFOs plan to increase technology investments, and nearly half will increase spending by 10% or more, while 88% said increasing productivity is a focus area, with automation and faster decision-making closely behind. In this context, data and information quality and reliability are dependent on the quality of financial governance. Team participation in the <a href="https://www.techtarget.com/searchdatabackup/tip/Enterprise-data-governance-Frameworks-and-best-practices">development of governance frameworks</a> means guidelines can vary across functions based on their activities, and controls may be flexible, with data quality determined by local priorities. These conditions create a gap between policy documents and operational behavior. Downstream activity widens the gap. For example, a minor error in source data flowing into reporting and analysis. Some inconsistencies are caught through manual reconciliation, but the fixes introduce variation in how data is interpreted and used. The <a href="https://www.cfodive.com/news/massive-trust-gap-hinders-cfo-ai-ambitions-study-finds/810786/">rapid adoption of AI</a> has increased the significance of this gap. Errors that once affected a single report or team now propagate through forecasting models, workflow automation and decision support systems. What once was an inefficiency is now a <a href="https://www.techtarget.com/searchdatamanagement/feature/How-agentic-AI-amplifies-data-management-challenges">scaled-up financial and operational risk</a>. </section> <section class="section main-article-chapter" data-menu-title="Responsibility for data governance is being moved to finance"> <h2 class="section-title">Responsibility for data governance is being moved to finance</h2> The increase in CFO oversight demonstrates how organizations discipline themselves in the areas that count. The finance functions already have control processes, audit trails, approval workflows and materiality thresholds in place. That infrastructure does not exist in IT or a central data office, and the data at stake -- customer, product, vendor and transactional -- is financial data, which directly affects revenue, cost visibility and risk exposure. Many <a href="https://www.techtarget.com/searchcio/feature/Why-the-CIO-CFO-relationship-is-key-to-digital-success">finance leaders are expanding their responsibilities</a>, going beyond reporting to include wider data governance and control. One of the main benefits of making governance an explicit, systematized part of the financial operating model is that stewardship work is funded and prioritized alongside other financial activities, and governance trade-offs are discussed in forums where they can be resolved. Success metrics also change, from activity measures such as the number of datasets cataloged or policies published, to outcomes such as reduced reconciliation time, shorter reporting cycles, improved audit readiness and more confident decisions. That reframing is what turns governance from a conceptual system into an operational discipline. </section> <section class="section main-article-chapter" data-menu-title="What does a CFO-led data governance program look like?"> <h2 class="section-title">What does a CFO-led data governance program look like?</h2> In a CFO-led model, the finance department sets how the most important controls are structured, funded and enforced. In practice, the CFO sponsors the governance function, sets priorities, provides guidance on what is financially material, funds stewardship and remediation, and establishes escalation paths to be used when business units don't follow standardization efforts. IT owns infrastructure and architecture. Data teams design governance models. Internal audits check whether these controls are operating as designed. But when disputes arise, the CFO-led governance model ensures that there is an authority that can assess costs, risks and impacts to make final decisions across the organization. Governance challenges are <a href="https://www.techtarget.com/searchdatamanagement/tip/Data-governance-challenges-that-can-sink-data-operations">rarely just technical</a>. They quickly devolve into questions of definition, ownership and trade-offs. One business unit may need flexibility to meet local requirements, while another may need consistency for reporting. Without financial authority, the tension between these two can never be resolved. CFO ownership also changes how resistance is handled. Once <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-governance-metrics-Data-quality-data-literacy-and-more">governance is framed</a> in terms of cost, reporting implications and operational risk, the accountability some teams prefer to avoid becomes visible and measurable. Starting with the domains that most affect the bottom line, external reporting and forecasting, builds credibility and extends governance to adjacent areas naturally. Where the finance function leads indirectly through transformation programs, executives can embed data quality and controls in reporting, planning and risk management processes, connecting how data is managed to how performance is measured. Zac Amos is a freelance tech writer specializing in AI, cybersecurity and business tech. He is also the Features Editor at ReHack Magazine, and he has bylines on publications like VentureBeat, TechRepublic and Forbes. </section> Without CFO ownership, data governance stays underfunded and unenforceable. Finance leadership ties governance to audit controls and budget accountability. https://cdn.ttgtmedia.com/rms/onlineimages/keys_a290218126.jpg https://www.techtarget.com/data-technologies/opinion/Data-governance-fails-without-CFO-ownership Thu, 23 Jul 2026 11:45:00 GMT Data governance fails without CFO ownership With vector indexing a crucial aspect of AI development, the latest release of Zilliz's open source Milvus vector database is designed to improve the vector search results that feed AI pipelines. Milvus 3.0, which was made generally available on July 16, features an architectural update that simplifies development by adding lake-native data access. Native access to data lakes such as <a href="https://www.techtarget.com/searchstorage/news/366616623/Amazon-S3-storage-adds-new-capabilities-for-data-lakes">Amazon S3</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/252515666/Google-grows-cloud-capabilities-with-BigLake-data-lakehouse">Google BigLake</a> and Microsoft <a href="https://www.techtarget.com/searchstorage/tutorial/How-to-create-an-Azure-Data-Lake-Storage-Gen2-account">Azure Data Lake Storage</a> enables Milvus to read, write and query data where it's stored using open file formats including <a target="_blank" href="https://iceberg.apache.org/" rel="noopener">Apache Iceberg</a>. Previously, users had to extract and load data from data lakes into Milvus as part of their development pipelines, adding cost, complexity and data movement that could accidentally expose proprietary data. In addition, Milvus now features an updated data retrieval engine to better discover and operationalize contextually appropriate vectors for agents and other AI applications that require large amounts of <a href="https://www.techtarget.com/searchdatamanagement/opinion/Context-is-the-make-or-break-layer-for-AI-in-production">relevant data to deliver accurate results</a>. Given that using Milvus now eliminates certain data migration projects, the latest update is significant for not only existing users but also potential new ones, according to Mike Leone, an analyst at Moor Insights & Strategy. "[It's valuable] as long as your data already sits in one of the open table formats Milvus can read," he told TechTarget. "If it does, adopting Milvus isn't a migration project, and that opens it up to teams that could never justify the migration in the first place." Based in Redwood City, Calif., Zilliz is a vector database vendor that competes with fellow specialists such as Pinecone, Qdrant and Redis, as well as broader-based data management providers that offer vector database capabilities, including AWS, Google Cloud and Oracle. In addition to Milvus, <a href="https://www.techtarget.com/searchdatamanagement/news/252524175/Zilliz-raises-60M-for-open-source-Milvus-vector-database">Zilliz</a> offers Zilliz Cloud, a managed service that includes Milvus' vector database capabilities. <section class="section main-article-chapter" data-menu-title="Aiding AI development"> <h2 class="section-title">Aiding AI development</h2> As interest in building agents and other AI applications <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">has surged</a>, so has the importance of vector search. Vectors are numerical representations of data that make information, including unstructured data such as text and audio, searchable so it can found and used to feed inform applications. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> [It's valuable] as long as your data already sits in one of the open table formats Milvus can read. If it does, adopting Milvus isn't a migration project, and that opens it up to teams that could never justify the migration in the first place. </figure> <figcaption> Mike LeoneAnalyst, Moor Insights & Strategy </figcaption> </div> </blockquote> As vector databases have <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">gained widespread popularity</a> over the past few years, providers have updated and improved their capabilities to deliver more accurate search results so that the data fed into AI pipelines enables organizations to build trustworthy AI tools. For example, <a href="https://www.techtarget.com/searchdatamanagement/news/366642768/MongoDB-adds-new-vector-performance-capabilities-to-aid-AI">MongoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366639802/Teradata-updates-vector-indexing-suite-to-aid-AI-development">Teradata</a> have each added new vector embedding and search capabilities this year. Now, Zilliz is following suit with its latest Milvus database update, adding capabilities tailored to meet the needs of users doing more complex work than when they were building analytics and traditional AI tools such as predictive models, according to James Luan, co-founder and CTO of Zilliz. "[The update] was very much driven by what we were hearing from production AI users, including large language model labs and self-driving companies," he told TechTarget. "As AI workloads matured, teams weren't just doing simple search anymore." One of the most pressing problems organizations faced was wasted time, effort and money spent copying and <a target="_blank" href="https://getdeploying.com/reference/data-egress" rel="noopener">moving data</a> from data lakes into Milvus, Luan continued. "It's the kind of invisible infrastructure tax that slows AI teams down considerably," he said. "Milvus 3.0 tackles this by bringing production-grade vector indexes directly to lake-resident data. … Teams can search, analyze, and process from one copy, which is simpler to manage and much less expensive to operate." New Milvus database capabilities aimed at bringing vector search closer to where data is stored and improving the relevancy of vector search include the following: <ul type="disc" class="default-list"> <li>Loon, a new storage engine that optimizes <a href="https://www.techtarget.com/searchstorage/definition/object-storage">object storage</a> reads to reduce latency and improve the accuracy of searches.</li> <li>StructList, a multi-vector retrieval tool that enables users to discover documents, images and other data represented by more than one vector.</li> <li>An index that strengthens sparse and hybrid vector retrieval searches while reducing the compute power required to run such searches.</li> <li>External Collections, a feature that defines collections in Milvus over data stored in Apache Iceberg, Apache Parquet, Lance or Vortex to build searchable indexes.</li> <li>Snapshots, a tool that enables users to create point-in-time, read-only views of collections so they can perform offline jobs such as evaluations and validations while production work continues in online environments.</li> <li>A Spark connector that exposes Milvus as a data source for <a href="https://www.techtarget.com/searchaws/definition/Amazon-Elastic-MapReduce-Amazon-EMR">Amazon EMR</a>, Databricks and <a href="https://www.techtarget.com/searchdatamanagement/definition/Apache-Spark">Apache Spark</a> pipelines as part of standard batch data workflows.</li> </ul> Kevin Petrie, an analyst at BARC U.S., noted that Zilliz's blending of database and data lake capabilities in Milvus 3.0, which helps users more easily combine previously disparate analytical and transactional data workloads, plays to a growing trend. Numerous vendors, including <a href="https://www.techtarget.com/searchdatamanagement/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers">Databricks</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake</a>, are now providing database capabilities that enable customers to unify data to build a foundation for AI. "Zilliz's support of lakebase environments plays well to the convergence trend, which leading platforms such as Databricks are pushing aggressively," Petrie told TechTarget. "Zilliz enables enterprises to deploy rich vector capabilities to support those converged workloads in lakebase environments." In addition, the Milvus update is significant for users given that it reduces the need to <a href="https://www.techtarget.com/searchstorage/definition/data-migration">migrate data</a> and improves the database's search and indexing capabilities, he continued. "Zilliz addresses several market requirements with this release," Petrie said. "First, data remains highly distributed, [so] Zilliz is wise to help customers index vectors in place without moving them. … Second, enterprises need multimodal AI that goes beyond basic vectorization to handle capabilities such as the full-text search and indexing that Zilliz provides." </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title">Competitive standing</h2> Although Zilliz is addressing user needs with its latest Milvus database update, the vendor faces a significant challenge as it competes for market share, according to Petrie. With hyperscale cloud and data platform providers adding vector search and storage capabilities over the past few years, many enterprises that already use those providers for data management no longer need to seek out <a href="https://www.techtarget.com/searchdatamanagement/tip/Top-vector-database-options-for-similarity-searches">a specialist</a> for their vector database needs. Specialists sometimes still provide more robust vector embedding and search capabilities than broader-based vendors, which appeals to certain potential customers. But for many, simplification is a selling point. "Zilliz faces a similar challenge as all vector database companies, [which] is that vectors are just one piece of the multimodal puzzle," Petrie said. "Larger vendors such as Google manage and retrieve vector, graph and tabular data from the same multimodal database, which simplifies enterprise AI architectures compared with point vector solutions." Leone noted that Snapshots is perhaps the most valuable of the new features because it allows users to evaluate their <a href="https://www.techtarget.com/searchenterpriseai/tip/Tools-and-techniques-for-optimizing-AI-data-pipelines">retrieval systems</a> even if their data is continually changing. From a competitive standpoint, he added that, while certain new Milvus capabilities are found in other vector databases as well, indexing data formats that aren't converted to vectors -- Apache Iceberg, Apache Parquet, Lance and Vortex -- is a potential differentiator for Zilliz. "The area to pay attention to is indexing formats you never had to convert first," Leone said. "That's harder than it sounds, because serving live queries straight off object storage is slow. It's why Zilliz built Loon, the new storage engine underneath Milvus." </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title">Looking ahead</h2> With the latest version of Milvus now GA, over the second half of 2026 Zilliz plans to add deeper integrations between the database and data lakes that better enable users to <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-audit-AI-systems-for-transparency-and-compliance">audit and debug</a> AI behavior, according to Luan. In addition, smarter storage and elasticity at scale to optimize usage are part of the product development roadmap for Milvus, he continued. Beyond Milvus, Zilliz plans to bring all the new capabilities planned for its open source database to its Zilliz Cloud offering. "Bringing the full Milvus 3.0 feature set into this managed platform is a key part of our [second half] roadmap," Luan said. Leone, meanwhile, suggested that Zilliz add <a href="https://www.techtarget.com/searchsecurity/tip/Evaluate-cloud-database-security-controls-best-practices">security capabilities</a> beyond access controls to reduce reliance on system administrators. "If Milvus is reading straight from a customer's tables, someone in security is going to ask who can see what," he said. "Milvus already has its own access controls, so next I'd want the permissions on those tables to carry through automatically. Otherwise, an admin is keeping two separate systems in sync by hand." 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> Native access to data lakes and a new data retrieval engine highlight the latest release as the vendor competes for market share amid a crowded field. https://cdn.ttgtmedia.com/rms/onlineimages/machine%20learning_g1186820873.jpg https://www.techtarget.com/data-technologies/news/366646015/Zilliz-ups-vector-search-results-with-Milvus-database-update Mon, 20 Jul 2026 14:57:00 GMT Zilliz ups vector search results with Milvus database update Startup database vendor Regatta Data on Wednesday made its entry into a market populated by long-established vendors, launching RegattaDB to provide users with a unified data foundation for developing agents and other AI applications. Traditionally, databases have separated transactional processing, analytical processing and <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector search</a> to avoid potential performance bottlenecks that can occur when systems get overloaded. However, to be accurate, agentic and generative AI (GenAI) tools require far more data  than business intelligence or traditional AI applications such as predictive models. Fragmented, isolated data doesn't provide agents and GenAI chatbots with enough context. Instead, they need unified data layers to properly interpret <a href="https://www.techtarget.com/searchdatamanagement/opinion/Context-is-the-make-or-break-layer-for-AI-in-production">business context</a>, deliver accurate outputs and avoid <a href="https://www.evidentlyai.com/blog/ai-hallucinations-examples">misleading hallucinations</a>. In response, long-established database providers such as Couchbase, Microsoft, MongoDB and SingleStore now offer unified platforms. In addition, data platform vendors Databricks and Snowflake each made acquisitions in 2025 to add <a href="https://www.theserverside.com/tip/MySQL-vs-PostgreSQL-Compare-popular-open-source-databases">PostgreSQL databases</a> that can be used to unify data for AI workloads. Now, San Francisco-based Regatta Data is making RegattaDB generally available to similarly provide customers with a data foundation for AI. However, unlike many of its predecessors, RegattaDB was purpose-built for AI workloads rather than retrofitted or rearchitected to unify disparate database workload types. Of particular significance for Regatta Data's users is the potential reductions in cost and complexity when compared with maintaining separate databases for <a href="https://www.techtarget.com/searchdatamanagement/definition/OLAP">analytics</a>, <a href="https://www.techtarget.com/searchdatacenter/definition/OLTP">transactional</a> and vector workloads, according to Stephen Catanzano, an analyst at Omdia, a division of Techtarget. "For organizations deploying AI agents at scale, this means they can finally support the massive concurrency and real-time demands of agent workloads without the fragmentation and latency issues that plague traditional architectures," he told TechTarget. Devin Pratt, an analyst at IDC, similarly noted that potential performance gains are a significant aspect of RegattaDB's launch. "Rebuilding the engine to collapse three systems into one is where the real efficiency comes from," he told TechTarget. <section class="section main-article-chapter" data-menu-title="Made to meet modern demands"> <h2 class="section-title">Made to meet modern demands</h2> Most databases were designed for a bygone era. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> For organizations deploying AI agents at scale, this means they can finally support the massive concurrency and real-time demands of agent workloads without the fragmentation and latency issues that plague traditional architectures. </figure> <figcaption> Stephen CatanzanoAnalyst, Omdia </figcaption> </div> </blockquote> They were built for business intelligence, feeding dashboards and reports that displayed monthly or quarterly information rather than autonomous agentic AI applications that demand large volumes of fresh, relevant, <a href="https://www.techtarget.com/searchbusinessanalytics/feature/GenAI-demands-greater-emphasis-on-data-quality">high-quality data</a> to properly perform. Even the data management platforms that launched last decade, such as Databricks and Snowflake, weren't originally designed to meet the demands of autonomous AI applications. With enterprises now <a 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">investing heavily</a> in building agents, but <a href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE">often struggling</a> to develop AI tools that can be trusted in production, user feedback led Regatta Data to design a database that unifies usually disparate data workloads, according to Regatta Data CEO Boaz Palgi. "Customers demanded [RegattaDB's] capabilities," he told TechTarget, noting that much of the innovation many databases still rely upon dates back to the 20th century. "Modern data … is very different from data in the 1950s, 1960s, and 1970s." RegattaDB is a SQL database built on a concurrency model developed by Regatta Data to unify data and deliver consistent performance for analytical, transactional and vector search workloads. In addition, the concurrency eliminates the need for Regatta Data customers to build pipelines that <a href="https://www.techtarget.com/searchdatamanagement/feature/5-data-integration-challenges-and-how-to-overcome-them">unify data</a> or create synchronization layers. The result is performance at scale, reducing workloads that normally take hours down to less than 5 minutes, according to Regatta Data but not independently verified. "Many agents, one correct picture, in real time -- that's the target, and the concurrency model is built to hit it," Pratt said. Catanzano likewise noted that, based on Regatta Data's performance claims, the vendor's concurrency model appears appropriately constructed to power unified <a target="_blank" href="https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-next-big-shifts-in-ai-workloads-and-hyperscaler-strategies" rel="noopener">workloads at scale</a>. "RegattaDB does appear properly built for its intended purpose, as its patented distributed concurrency control protocol enables it to deliver serializable cross-node consistency across all three workload types simultaneously, and … it can handle [high volumes] of ACID-compliant transactions while executing complex analytics." Meanwhile, with many database providers now unifying previously separate workloads, RegattaDB is somewhat distinguished by an underlying architecture purpose-built for AI rather than one retrofitted or redesigned to handle AI workloads, Catanzano continued. "The company's emphasis on eliminating pipelines entirely and delivering true unification across workload types, rather than just co-locating them, seems to be a key differentiator," he said. Pratt similarly noted that unified databases are becoming more common with <a href="https://www.techtarget.com/searchdatamanagement/news/366583139/Oracle-adds-vector-search-capabilities-to-database-platform">Oracle adding native vector search</a> to its line of databases, PostgreSQL similarly adding support for vector search with pgvector, and the acquisitions made by <a href="https://www.techtarget.com/searchdatamanagement/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition">Databricks</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake</a>. All, however, are adding capabilities to an engine originally built for one workload. "Everyone says converged, [but]Regatta actually means one engine," Pratt said. </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title">Next steps</h2> Just as RegattaDB was built in response to customer demand, user feedback will shape Regatta Data's product development roadmap, according to Palgi. "We will continue to … prioritize the delivery of capabilities that our customers request," he said. Whether asked for by customers or not, Regatta Data would be wise to add integrations with AI frameworks and <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">agent orchestration platforms</a>, according to Catanzano. By doing so, the vendor can reduce the complexity required to connect RegattaDB with the environments developers and engineers use to build agents. In addition, there is room for Regatta Data to expand its offering by adding new capabilities, Catanzano continued. "Regatta can continue serving current users and attract new ones by expanding its ecosystem integrations with popular AI frameworks and agent orchestration platforms, providing comprehensive tooling and observability features that make it easier for developers to build and monitor agent systems, and demonstrating clear ROI through case studies," he said. Likewise, Pratt suggested that Regatta can grow by doing case studies of companies benefiting from its capabilities. In addition, demonstrating RegattaDB's performance and cost-savings potential through independent <a href="https://www.techtarget.com/searchcio/definition/benchmark">benchmark</a> testing, he added. "Land a workload, prove the savings, and the rest of the estate tends to follow," Pratt 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 database vendor is entering a crowded market with the launch of a database purpose-built for agents by unifying disparate data workloads. https://cdn.ttgtmedia.com/rms/onlineimages/iot_g1224942277.jpg https://www.techtarget.com/data-technologies/news/366645786/Regatta-Data-launches-unified-base-for-AI-with-RegattaDB Wed, 15 Jul 2026 11:00:00 GMT Regatta Data launches unified base for AI with RegattaDB Alation is taking on AI governance to aid enterprises attempting to build agents that can be trusted in production. Many organizations, despite investing heavily in agentic AI development, <a href="https://www.techtarget.com/searchdatamanagement/feature/Good-governance-key-to-reducing-high-AI-project-failure-rate">still struggle</a> to move AI experiments beyond the pilot stage. While myriad reasons contribute to failed experiments, including outdated technology infrastructures and lack of buy-in from executives, data-related problems are prominent among them. In particular, the inability to connect agents with the appropriate data and <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> that gives them the context to deliver accurate outputs has hampered agentic AI development initiatives. Alation, which already provides data catalog capabilities that enable organizations to govern data, on Tuesday launched the Alation Intelligence Operating System (AIOS). The OS, which works in conjunction with existing Alation governance capabilities, is aimed at better enabling customers to build AI tools that can be <a href="https://www.techtarget.com/searchcio/feature/Startup-founder-says-trust-is-biggest-barrier-to-AI-agents">trusted in production</a> by connecting data, context and agents in a unified environment. Features include Agent Studio to develop agents and agentic governance capabilities, such as lineage and access controls, that automate manual work. "AIOS is a meaningful expansion for Alation customers because it delivers needed AI governance atop the existing foundation rather than asking enterprises to start another isolated AI-governance program to manage agent data intelligence," Michael Ni, an analyst at Constellation Research, told TechTarget. Alation's introduction of AIOS follows its <a href="https://www.techtarget.com/searchdatamanagement/news/366642977/Alation-intros-AI-governance-suite-to-ensure-compliance">unveiling of AI Governance</a> to help organizations remain regulatory compliant as they deploy agents, <a href="https://www.techtarget.com/searchdatamanagement/news/366639805/Alation-automates-governance-with-latest-AI-powered-suite">outcome-based governance</a> to govern data using agents, and <a href="https://www.techtarget.com/searchdatamanagement/news/366634209/Alation-unveils-agentic-AI-suite-for-governing-critical-data">CDE Manager</a> to better oversee data related to critical operations such as reporting and risk management. For AIOS to deliver on its promise, it needs to easily integrate with Alation's other agentic AI governance capabilities, according to Ni. "Alation has historic strength in data discovery and governance but now has to show that its new solution areas of Agent Studio, AI governance, compliance and feedback deliver one integrated system at enterprise scale," he said. <section class="section main-article-chapter" data-menu-title="Building for success"> <h2 class="section-title">Building for success</h2> With the failure rate of AI projects <a target="_blank" href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE" rel="noopener">still higher than the success rate</a>, connecting agents with context has emerged as the dominant trend in data management and AI development. In June alone, AWS, Databricks, Microsoft and Snowflake unveiled new capabilities to connect agents with context. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> AIOS is a meaningful expansion for Alation customers because it delivers needed AI governance atop the existing foundation rather than asking enterprises to start another isolated AI-governance program to manage agent data intelligence. </figure> <figcaption> Michael NiAnalyst, Constellation Research </figcaption> </div> </blockquote> Vendors have taken different approaches, such as improving the data retrieval process, upgrading vector search and <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems">adding semantic layers</a>. Alation, through agent-powered governance, is similarly trying to help customers build agents they can put into production. "We developed AIOS from a mix of customer demand and a fundamental shift we observed in how AI is redefining data management," Satyen Sangani, Alation's co-founder and CEO, told TechTarget. "AI provides so much power to do the work that historically has been side-of-desk, things like stewardship, quality, lineage. [Additionally], the result of this work is now so much more critical and impactful." In particular, the work is needed to catch <a target="_blank" href="https://www.evidentlyai.com/blog/ai-hallucinations-examples" rel="noopener">incorrect outputs</a> that agents are confident are correct, he continued. "When software breaks, it throws an error, but when an AI agent breaks, it produces a confident, incorrect answer, and most organizations have no system of record to catch it," Sangani said. "AIOS closes that gap … so enterprises can trust the AI they're already running instead of finding out after the fact." Data-related problems that frequently stall agentic AI projects include bad data causing an agent to act on improper context, agents misreading context that isn't clear, and <a href="https://www.techtarget.com/searcherp/feature/How-leaders-can-spot-AI-drift-before-it-hurts-the-business">agent drift</a> with instructions, tools and training degrading over time and causing the agent to diverge from its intended goal. Alation's new operating system is designed to address each problem by providing the following capabilities: <ul type="disc" class="default-list"> <li>Agent Studio, an environment aimed at enabling users to develop trustworthy agents that call on governed proprietary data and business logic for context.</li> <li>Governed data and AI workflows to ensure that agents remain in compliance with <a href="https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker">changing regulations</a>.</li> <li>Control over context, access and lineage so that each agent-powered decision can be traced and certified.</li> <li>Conversational analytics so users can query and analyze governed data.</li> </ul> "The AIOS represents a notable shift toward automation by using agents to proactively curate metadata, map data lineage and scale enterprise governance," William McKnight, president of McKnight Consulting, told TechTarget. "It's a clever way to prevent confident fails [by agents, but] introduces some vendor lock-in for the sake of faster AI deployment." By pre-certifying <a href="https://www.techtarget.com/searchdatamanagement/tip/Metadata-management-standards-examples-that-guide-success">metadata</a> and enforcing governance policies, AIOS appears appropriately constructed to reduce agent hallucinations, he added. Meanwhile, AIOS is in step with current AI governance trends rather than pacing the market, according to McKnight. "The entire enterprise data governance industry has simultaneously pivoted to launch specialized suites for governing autonomous AI agents," he said. "They are all racing to solve the exact same enterprise problem -- ensuring that autonomous AI does not act on bad data or violate compliance boundaries." Ni likewise noted that by connecting AIOS to <a href="https://www.techtarget.com/searchdatamanagement/news/252499007/Alation-brings-data-catalog-technology-to-the-public-cloud">Alation's catalog</a> and other governance capabilities, the new operating system seems logically put together to aid customers as they build and manage agents. However, for it to truly operate as intended, AIOS needs to not only help govern agents under development but also once they're deployed, he continued. "AIOS has the foundational components," Ni said. "The open question is how far governance extends into production execution. A complete offering should continuously monitor agent behavior, propagate the correct user identity and entitlements, enforce policies at the point of action, and provide a runtime audit trail." From a competitive perspective, Ni noted that Actian, Atlan, Credo AI, IBM, Microsoft and ServiceNow are all addressing <a href="https://www.techtarget.com/searchenterpriseai/feature/Agentic-governance-must-go-beyond-traditional-IT-practice">agent governance</a>. However, Alation is among the first to unify capabilities such as context, lineage and conversational analysis in an open architecture. "The real test will be if context advantage can demonstrably translate into stronger runtime decisions and fewer operational failures," Ni said. </section> <section class="section main-article-chapter" data-menu-title="Plotting the future"> <h2 class="section-title">Plotting the future</h2> With AIOS now generally available, Alation is preparing to release new ontology capabilities – governed definitions of data and relationships similar to semantic models – and improving its existing lineage, data quality, data observability and AI governance tools, according to Sangani. Following the introduction of AIOS, Ni noted that there's an opportunity for Alation to turn the new OS into <a href="https://www.techtarget.com/searchdatamanagement/opinion/Context-is-the-make-or-break-layer-for-AI-in-production">a context layer</a> for agents working across environments such as databases, multiple cloud data platforms and an array of SaaS applications. "Alation's opportunity is to connect what the enterprise knows with what its agents are doing right now, while opening a much larger market among AI platform and operations teams," he said. McKnight, meanwhile, suggested that Alation could better serve existing customers and perhaps attract new ones by integrating AIOS with <a href="https://www.techtarget.com/searchdatamanagement/opinion/Data-observability-for-AI-helps-curb-poor-model-performance">data observability</a> capabilities and advancing its agentic AI tools to include more autonomous insight generation and automation. "Furthermore, expanding the platform to support unstructured and multimodal data is considered crucial for building a comprehensive AI ecosystem," 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> While not the first AI governance suite, the vendor's Intelligence Operating System is somewhat unique by unifying data, context and agents in one environment. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a264431831.jpg https://www.techtarget.com/data-technologies/news/366645666/Alation-launches-OS-for-building-governing-AI-agents Tue, 14 Jul 2026 10:03:00 GMT Alation launches OS for building, governing AI agents When a CDO departs, organizations often discover that their data strategy was little more than the vision of a single executive, never embedded in the organization's culture and fleeting in its efficacy. When that's the case, vendor relationships strain, governance initiatives stall and undocumented processes that existed only in the CDO's mental roadmap surface as gaps -- a pattern that plays out before, during and after a departure. Any data strategy centered on a single executive is an enterprise liability, and the average CDO <a href="https://www.cdomagazine.tech/opinion-analysis/the-chief-data-officer-role-at-a-crossroads-what-lies-ahead">tenure</a> of 2.5 years makes that liability a recurring one. <section class="section main-article-chapter" data-menu-title="The early warning signs"> <h2 class="section-title">The early warning signs</h2> The warning signs that a data strategy is tied to one executive appear well before a departure. Most stem from a lack of <a href="https://www.techtarget.com/searchdatamanagement/tip/The-data-ownership-blind-spots-putting-organizations-at-risk">formalized governance and ownership</a>. According to Dominic Sartorio, vice president of product marketing at Denodo, CDOs often "come in with a great vision, but the issue is they lack the ability to change-manage the rest of the organization. They lack the ability to operationalize. After they leave, the vision goes with them and falls apart." Organizations also lack quantifiable measures to determine <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-governance-metrics-Data-quality-data-literacy-and-more">whether the data strategy is working</a>, such as dashboards that track data usage and its impact on business goals. "The early warning signs [include] a lack of data contracts that are agreed to by both sides," Sartorio said. "There is a lack of identified business users who take some ownership stake that the data is supporting the outcome." Governance initiatives that remain one executive's priority rather than a shared responsibility, have no designated data governance stakeholders "in terms of data owners, data stewards and data custodians," said Thomas Phelps, Laserfiche CIO and SVP of corporate strategy. Litigation, regulatory penalties and data breaches follow when governance, data access and security lack clear ownership. </section> <section class="section main-article-chapter" data-menu-title="What a departure exposes"> <h2 class="section-title">What a departure exposes</h2> When CDOs depart an organization, it becomes apparent if their data strategies were attempts to operationalize their visions. During knowledge transfer sessions, significant gaps emerge between how the data strategy was supposed to work and how it actually does. Those disparities include what Swaroop Jagadish, CEO of DataHub, called "the work around fragile dependencies; the institutional judgments that got made along the way and never got written down." Much of the CDO's data strategy never leaves slide decks and meetings. "Every CDO carries an internal map of which business use case to prioritize, which data is trustworthy, where the fragility is, and why certain decisions were made," Jagadish observed. "All of that knowledge can disappear. It's too focused on an individual." </section> <section class="section main-article-chapter" data-menu-title="What breaks after departure"> <h2 class="section-title">What breaks after departure</h2> After CDOs leave an organization, points of failure can include strained vendor relationships and a lack of production in data-driven processes. Without the personal knowledge of which data pipelines to prioritize and how to optimize them, operational output drops. Pega CTO Don Schuerman said, "end user customers are falling off of the product usage cycle." Moreover, vendor relationships built on the CDO's personal trust fray, reinforcing that "your data strategy is not to implement Snowflake; it's to surface insights," Schuerman said. For data governance initiatives, there may be a lack of ownership throughout the organization when data breaches or regulatory compliance penalties are incurred. All of these woes are evidence that the data strategy designed to support an organization never actually left the purview of the individual championing it. Such a shortfall is a multifaceted liability. "Three things walk out the door when CDOs leave," Jagadish explained. "First, the vendor relationships built on personal trust. Second is the initiatives: CDOs in particular have talked about governance programs because it's their priority, but they were not institutionalized. The third thing is the mental model of the data landscape." </section> <section class="section main-article-chapter" data-menu-title="Building a data strategy framework that lasts"> <h2 class="section-title">Building a data strategy framework that lasts</h2> Organizations can avoid the woes that typically accompany a CDO's departure by <a href="https://www.techtarget.com/searchdatamanagement/tip/Developing-an-enterprise-data-strategy-10-steps-to-take">institutionalizing a data strategy</a> that can outlast the executive's tenure. "A data strategy is too big to be one person's job," Schuerman said. "It demands that all of the leaders who would be consumers of the data strategy have enough of a hand in and understanding of it, so they're not dependent on one person to execute it." That means aligning the strategy with the business processes it supports. Phelps said that alignment is essential to tackling specific use cases for the business. Documentation is the other half of institutionalization. Strategies formalized through content repositories, knowledge management platforms, data catalogs, vector databases, NLI interfaces and data marketplaces survive executive turnover. Sartorio's point on the necessity of data contracts is part of the broader <a href="https://www.techtarget.com/searchdatamanagement/opinion/Data-contracts-help-build-trustworthy-data-products-for-AI">data product</a> framework. Documentation includes "governance and security policies tied to it, like what the quality target should be, whether it is sensitive data or not, and latency considerations," Sartorio explained. "These are the SLAs and policies a platform can serve up." Jelani Harper is a data industry analyst and journalist covering data management, AI and enterprise IT for more than a decade. He is a research lead at Blue Badge Insights, writing analyst reports for GigaOm and articles for VentureBeat and The New Stack.  </section> The failure pattern of a leadership transition reveals what data strategy institutionalization requires. Without it, a data strategy framework is a dependency, not a strategy. https://cdn.ttgtmedia.com/rms/onlineimages/folder-files07.jpg https://www.techtarget.com/data-technologies/feature/What-a-leadership-transition-reveals-about-your-data-strategy Thu, 09 Jul 2026 11:36:00 GMT What a leadership transition reveals about your data strategy The pressure to move quickly on AI is causing many enterprises to choose speed over governance. It may accelerate a pilot, but it won't scale AI. Without confidence in the underlying data, agents can't act responsibly or reliably.  In Collibra's recent <a href="https://url.us.m.mimecastprotect.com/s/eep0CmZ2Eot6o3mD2hGfDcRWYTL?domain=collibra.com">research with The Harris Poll</a>, nearly 90% of tech decision-makers said they can't fully trust AI-driven insights until the data behind them is verified through formal governance. This is the trust gap in AI, and it's one of the reasons organizations struggle to move AI from pilots into production. And despite significant investment in AI, the consequences are already visible. Recent <a href="https://url.us.m.mimecastprotect.com/s/FKwXCn5YzpcxZ2Jj5UJhBcJwEG5?domain=mckinsey.com">McKinsey research</a> found that only 7% of companies have fully scaled AI across their businesses. That number won't change until enterprises unlock one of their most overlooked assets: unstructured data.   Unstructured data makes up between 80% and 90% of enterprise data and holds much of the business context AI agents depend on to reason, make decisions and act. Yet much of it remains unused. If that doesn't change, agents won't just fail at the build and design stage. <a href="https://www.techtarget.com/searchdatamanagement/opinion/AI-doesnt-need-an-unstructured-data-dump-move-what-matters">They'll fail at deployment.</a> Speed without governance isn't speed. It's simply delaying the problem until production. <section class="section main-article-chapter" data-menu-title="Governance never covered the data that matters most"> <h2 class="section-title">Governance never covered the data that matters most</h2> Historically, enterprises <a href="https://www.techtarget.com/searchdatabackup/tip/Enterprise-data-governance-Frameworks-and-best-practices">have built governance</a> around the data that is easiest to manage: the structured rows and columns stored in databases and warehouses. Unstructured data was left behind.  For two decades, it simply wasn't workable. BI tools couldn't read it. Catalogs couldn't index it. Governance frameworks were never built to cover it. That gap didn't matter much when AI was mostly generating text or summarizing what a person fed it directly. It <a href="https://www.techtarget.com/searchdatamanagement/feature/Unstructured-data-needed-but-often-untapped-for-agentic-AI">matters much more now</a> that agents are expected to find information across systems, connect it and act on it. The quality of those actions depends on more than the model itself. It depends on whether the enterprise has made its knowledge discoverable, governed and available in the right context. The proprietary data that makes an enterprise unique, including contracts, transcripts and internal documents, has gone ungoverned for years. This has meant that agents can't use the very thing that would make them even more valuable. Without it, an agent has no real advantage. It's just another general-knowledge LLM with extra steps.  </section> <section class="section main-article-chapter" data-menu-title="Structured data answers the easy questions"> <h2 class="section-title">Structured data answers the easy questions</h2> Consider a customer support agent investigating a complaint about a defective product. The order record is the easy part. It's structured, stored in a database and any AI system can retrieve it in seconds.  What actually determines the outcome is harder to find. The documentation is buried in a shared drive. The support tickets were filed months ago. The email thread where a field engineer diagnosed this same issue last year. Individually, each source tells only part of the story. Together, they provide the context needed to determine whether the issue is isolated, part of a known pattern, or one the organization has already solved. The answer isn't waiting for better technology. The technology already exists. Today, AI can generate metadata, identify sensitive information, classify content and assemble the specific information an agent needs to do its job. This matters because cost is just as important as capability. An LLM can process enormous amounts of unstructured data, but the token cost is significant and the results are often inconsistent. Feeding an agent everything is not the same as feeding it the right information. When an agent gets the information it actually needs, rather than the entire stack, it delivers better answers at a fraction of the cost. That’s the difference between an agent workload that scales and one that quietly becomes too expensive to sustain. </section> <section class="section main-article-chapter" data-menu-title="Unstructured data can't wait for a perfect roadmap"> <h2 class="section-title">Unstructured data can't wait for a perfect roadmap</h2> Enterprise leaders don't have the luxury of <a href="https://www.techtarget.com/searchdatamanagement/opinion/Most-data-governance-wasnt-built-for-AI">choosing between speed and governance</a>. Manual classification was never going to keep pace with the volume of unstructured content organizations generate every day, let alone across the year. At the same time, waiting for a perfectly governed data set before deploying AI isn't realistic either. The organizations that succeed will be the ones that treat unstructured data as core AI infrastructure, not something to address later. The path forward isn't the same for every organization. Some will build these capabilities internally. Others will buy them or partner with them. What matters is treating unstructured data with the same discipline that's long been applied to structured data. Contracts, call transcripts and internal documents now matter just as much as databases because AI agents depend on both. And token economics shouldn't be an afterthought. The cost of running agents on ungoverned data only compounds as more agents are deployed. None of this requires a perfect data set or a finished roadmap. It requires a decision to stop treating unstructured data as something that can wait.  The organizations that make that decision won't build agents that work in a demo. They'll build agents that understand their business because they can access the knowledge, context and expertise that already exists across the enterprise.  Felix Van de Maele is co-founder and CEO of Collibra and was named EY Technology Entrepreneur of the Year in 2019. </section> Ungoverned, unstructured data blocks enterprises from scaling agentic AI. Contracts, transcripts and internal documents hold the context agents need but can't yet reach. https://cdn.ttgtmedia.com/rms/onlineimages/folder-files11.jpg https://www.techtarget.com/data-technologies/post/Unstructured-data-is-the-bottleneck-for-agentic-AI-success Mon, 06 Jul 2026 16:39:00 GMT Unstructured data is the bottleneck for agentic AI success After unveiling a spate of features during its annual user conference in April, Qlik is now delivering some of the data engineering tools designed to prepare data for AI that had been in preview. New generally available features include agents for <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-quality-fast-failures-and-quick-wins-key-to-AI-success">data quality</a> that enable users to create and edit data quality rules, measure the quality of data points and datasets with <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">trust scores</a> and other metrics, and detect or report anomalies. In addition, among other new capabilities, Qlik launched <a href="https://www.techtarget.com/searchbusinessanalytics/news/252510804/Data-catalogs-fuel-increased-efficiency-speed-to-insight">a catalog</a> that helps users standardize terminology and discover data assets. Collectively, Qlik's capabilities are intended to aid data preparation for AI so that enterprises can more easily close the gap between the desire to build extensive multi-agent networks and the reality of building agents and other AI applications that <a href="https://www.techtarget.com/searchdatamanagement/opinion/Most-data-governance-wasnt-built-for-AI">can be trusted</a> to deliver accurate outputs in production. "This is a good update from Qlik for data engineers using their platform," Donald Farmer, founder and principal of TreeHive Strategy, told TechTarget. "No big breakthroughs, but very useful AI integration. … For the gap that Qlik correctly identifies -- between ambition and readiness -- this is very helpful." Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, similarly noted that Qlik's new features are valuable because they integrate agents to improve data engineering efficiency. "These capabilities move beyond simply using AI to generate code by embedding agentic AI throughout the data engineering lifecycle," he told Techtarget. "Organizations can now discover, validate, govern, and package trusted data products more efficiently, helping reduce engineering backlogs while accelerating delivery of AI-ready data without sacrificing governance or lineage." Based in King of Prussia, Penn., Qlik is a longtime business intelligence and data integration vendor that is evolving as AI becomes the means of generating insights and consuming BI. Mike Capone, who had been Qlik's CEO since January 2018, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366642652/Qliks-Capone-departs-after-eight-years-as-CEO">stepped down suddenly</a> after the vendor's annual Connect user conference. Saugata Saha, who comes to Qlik from S&P Global where he led market intelligence, was <a href="https://www.linkedin.com/posts/saugata-saha_sp-global-announces-leadership-change-for-activity-7465052606105739266-Cutx/">named Qlik's president and CEO</a> a month after Capone's resignation and will officially begin his new roles on July 31. <section class="section main-article-chapter" data-menu-title="Preparing for AI production"> <h2 class="section-title">Preparing for AI production</h2> Although agentic AI development has <a 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">been a major focus</a> for many enterprises over the past couple of years, most AI pilots still <a href="https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html?id=us:2ps:3gl:aisgm26:awa:CONS:em:K0218784:012626:kwd-430833501819:195648817121:794247818306::&gclsrc=aw.ds&gad_source=1&gad_campaignid=23269751971&gclid=CjwKCAjw6MPRBhBTEiwAd-7Mrwla_c_gzzSu2TNPVBWdBS4INV4yNdYgtukO2OU2-zWBAuwZE-s3mBoCwegQAvD_BwE">don't make it into production</a>. Problems with the data that informs agents are not the only reason more AI projects fail than succeed, but they are among the more frequent causes. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This is a good update from Qlik for data engineers using their platform. No big breakthroughs, but very useful AI integration. … For the gap that Qlik correctly identifies -- between ambition and readiness -- this is very helpful. </figure> <figcaption> Donald FarmerFounder and principal, TreeHive Strategy </figcaption> </div> </blockquote> Agents need properly prepared data to perform, and they need it to be ready the instant they call on it to inform an action. Without AI-ready data, agents will make inferences based on the information they do have, which, if not complete, current or correct, leads to poor outputs. Left undetected, these outputs can have severe consequences, including lost revenue and <a href="https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker">regulatory noncompliance</a>. With agents so reliant on AI-ready data, many data management providers' recent product development initiatives have focused on making data available for AI. Most -- including <a href="https://www.techtarget.com/searchdatamanagement/news/366644853/AWS-latest-to-introduce-context-layer-for-agentic-AI">AWS</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">Databricks</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366643955/Microsoft-boosts-Fabric-to-make-it-a-foundation-for-AI">Microsoft</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake</a> just last month -- have centered on making data discoverable for AI. Qlik, along with vendors such as <a href="https://www.techtarget.com/searchdatamanagement/feature/Evolving-Alteryx-focusing-on-AI-ready-data-logic-for-agents">Alteryx</a>, is taking a different approach by focusing on preparing data for AI in a move informed by customer feedback in conjunction with watching market trends, according to Drew Clarke, Qlik's executive vice president of product and technology. "As organizations move from AI pilots to operational AI, the bottleneck is increasingly the data engineering work required to make data trusted, timely, governed and usable by both people and AI agents," he told TechTarget. "Customers told us they need more leverage in that layer, but without giving up governance, lineage or choice." In addition to data quality agents and a catalog to organize data assets, specific Qlik data engineering tools that are now generally available include the following: <ul type="disc" class="default-list"> <li>Data Products, a feature that aids teams as they build, manage and govern <a href="https://www.techtarget.com/searchbusinessanalytics/opinion/The-importance-of-data-products">data products</a> so that curated datasets and other assets are easy to operationalize and reuse for analytics and AI.</li> <li>Declarative Pipelines with Coding, a feature that allows data engineers to work with approved third-party coding agents and development environments to build and manage AI pipelines.</li> <li>Expanded <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol capabilities</a> that enable authorized agents and other AI tools to access proprietary data and business logic stored in Qlik's secure environment, so they have the context to properly perform.</li> </ul> Perhaps the most valuable of the new features is Data Products because it enables enterprises to deliver reusable, trusted data for analytics and AI, according to Catanzano. "Rather than recreating datasets for every initiative, organizations can establish governed data products that become a reliable foundation for multiple AI and business use cases," he said. However, while significant for existing <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Qlik-analytics-suite-helps-cities-combat-climate-change">Qlik customers</a>, tools such as Data Products and data quality agents are not unique, Catanzano continued. "Many data platform vendors are adding AI-assisted development and governance features," he said. "Where Qlik differentiates itself is by combining agentic workflows, governance, open architecture and MCP-enabled interoperability, allowing customers to work with their preferred AI assistants and existing technology stack instead of locking them into a single ecosystem." Farmer likewise highlighted the value of the agents that assist teams in <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Treating-data-as-a-product-a-method-to-grow-analytics-use">building and managing data products</a> so that developers and engineers don't have to create new datasets every time there's a new AI or analytics project. "Qlik has always struggled somewhat with data reusability, especially as the common solution was to create 'data marts' stored in proprietary QlikView Data files," he said. "Data Products are a more mature and agile way of managing that scenario." Regarding Qlik's competitive standing, like Catanzano, Farmer noted that other vendors are offering similar capabilities. However, Qlik stands out by combining capabilities in a unified layer, he continued. "These features don't really differentiate Qlik because every data platform is shipping something similar, [but] Qlik has a defensible position in the combination of capabilities across a single governed platform," Farmer said. </section> <section class="section main-article-chapter" data-menu-title="A peek into the future"> <h2 class="section-title">A peek into the future</h2> Over the second half of 2026, Qlik's continued focus will be to provide capabilities designed to help users move AI initiatives beyond experimentation and into production, according to Clarke. Specifically, the vendor plans to address strengthening <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">the data foundation for AI</a>, building more agents that aid data integration and analytics, and enabling customers to combine Qlik's capabilities with those of other platforms they use for aspects of their data and AI workflows. "The common thread is making AI more operational and reliable by connecting it to trusted data, business context and the controls enterprises need," Clarke said. Farmer suggested that Qlik add more capabilities that monitor AI usage, such as agent-question interactions, consumption patterns, usage frequency and results. "Qlik's strengths in data quality, cataloging and analytics could make this a unique selling point," he said, noting that adding <a href="https://www.techtarget.com/searchnetworking/tip/End-to-end-network-observability-for-AI-workloads">monitoring capabilities</a> would enable users to track data quality issues from the inception of a pipeline, assess their impact on AI agent outputs, and analyze their effects on decisions. "Adding such a feature would significantly distinguish the platform and speak to Qlik's strengths," Farmer added. Likewise, Catanzano named adding <a href="https://www.computerweekly.com/feature/Why-AI-is-forcing-enterprises-to-rethink-observability">operational monitoring</a> tools as a means for Qlik to better serve existing users and perhaps appeal to new ones. "As enterprises deploy more AI agents and production AI applications, capabilities such as AI observability, model and agent monitoring, policy enforcement and business outcome tracking would complement Qlik's strong data governance foundation and further differentiate the platform," 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 capabilities, such as data quality agents and a feature that makes data products more reusable, support engineers to help organizations more easily achieve their AI goals. https://cdn.ttgtmedia.com/rms/onlineimages/code_g136298313.jpg https://www.techtarget.com/data-technologies/news/366645330/Qlik-launches-data-engineering-tools-to-aid-AI-development Thu, 02 Jul 2026 12:04:00 GMT Qlik launches data engineering tools to aid AI development Data platform vendors are lining up behind Apache Iceberg, with several recently announcing new or expanded support for the open table format used to manage large analytics datasets in data lakes and lakehouses. For example, Snowflake made <a href="https://www.techtarget.com/searchdatamanagement/news/366641473/Snowflake-broadens-open-source-embrace-ups-Iceberg-support">support for Version 3 of the Iceberg specification</a> generally available in May, a month after enabling users to store Iceberg tables in its platform. These moves further deepen the full embrace of the table format that Snowflake announced in April 2025, after previously offering only limited support for Iceberg. Also in May, Databricks added a broad set of Iceberg capabilities to its data lakehouse platform, including Iceberg V3 support and the ability to create Iceberg tables in its Unity Catalog software. Its support, which began with the 2024 acquisition of a startup <a href="https://www.techtarget.com/searchdatamanagement/news/366588032/Databricks-1B-plus-Tabular-acquisition-adds-Iceberg-support">whose founders included Iceberg's creators</a>, is particularly notable because Databricks initially developed the alternative Delta Lake table format. SAP is also going the acquisition route: In May, it said it's <a href="https://www.techtarget.com/searchdatamanagement/news/366642794/SAP-acquisitions-of-Dremio-Prior-Labs-target-AI-development">buying Iceberg-based data lakehouse vendor Dremio</a>. The deal will extend the reach of SAP's Business Data Cloud to new external data sources without forcing users to move data into the SAP platform. In a Q&A with TechTarget, Donald Farmer, principal of consulting firm TreeHive Strategy, said these and various other vendor announcements demonstrate that Iceberg has become the de facto industry-standard table format, beating out both Delta Lake and Apache Hudi. Vendors really have no choice but to support it, he added. Farmer also discussed Iceberg's capabilities and issues that data leaders and teams need to consider when planning deployments of the table format. Editor's note: This Q&A has been edited for clarity and conciseness. Why are data platform vendors rushing to add or expand support for Iceberg? Donald Farmer: Partly, it's because the question of table format is now settled. Iceberg has won, and it is now the default. Once that happens, as a vendor, you can't sit outside that process. You have to adopt it. <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineImages/farmer_donald.jpg" alt="Photo of Donald Farmer, principal of TreeHive Strategy.">Donald Farmer </div> The first thing is wanting to defend against being excluded [by technology buyers]. You always want to be in on the RFP, so if Iceberg is in the RFP, you need to support it. Also, no matter how big you are as a vendor, once something becomes commoditized in that way, you have to support it. Look at SAP, which acquired Dremio. They didn't have an Iceberg-native engine. They had to find one. If you think of that as a negative framing -- "I don't want to be excluded" -- the positive framing is that the <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-Apache-Iceberg-is-essential-for-modern-data-lakehouses">Iceberg-native data lakehouse</a> is now the substrate for AI agents. One of the problems with agentic AI is that people see the data architecture that underlies the agents as <a href="https://www.techtarget.com/searchdatamanagement/opinion/AI-agents-push-enterprises-toward-unified-data-governance">being fragmented and siloed</a> in their organizations. The Iceberg-native lakehouse pools all that together and provides a fairly neutral environment for agents to run over, rather than trying to build agents over this fragmented, siloed architecture. For data leaders and teams, what capabilities does Iceberg provide that aren't supported by what could perhaps be called traditional data lakes at this point? Farmer: The traditional data lake is more or less just files in an object store -- particularly, more recently, Parquet files in an object store, with a Hive-style directory. Iceberg adds a metadata tier over that. It's not just file storage; there's the metadata tier, and also a sort of transaction layer. To a certain extent, you can have ACID transactions, for example. Iceberg also supports schema evolution, so you can add columns, you can rename them, maybe even change the type of a column, but you don't have to rewrite the data file back to that. And it has similar support for partition evolution, so you can change the partitioning schema without rewriting the existing data. It has capabilities like that that go above what you could also call the kind of dumb data lake, which is just files in an object store with a loose structure around them. Why has Iceberg become the default table format instead of Delta Lake or Hudi, or the three being more equal competitors? Farmer: To be fair, Delta still has a very large installed base. It's the native default format in <a href="https://www.techtarget.com/searchdatamanagement/news/366643955/Microsoft-boosts-Fabric-to-make-it-a-foundation-for-AI">Microsoft Fabric</a>, for example. It's not as if Delta has gone away; it's just not the open industry standard. Part of this is governance. Iceberg is part of the Apache Software Foundation, while Delta came from Databricks -- and even though it's open source, you still get the impression that there's single-vendor control. I think that's an issue -- people are very averse to being locked into any technologies. Also, if you look at the catalog layer, Iceberg has a REST catalog API, while Delta is cataloged through Databricks's Unity Catalog, which is powerful but still vendor-specific. As for Hudi, it's really good in certain scenarios, like high-volume, high-frequency streaming or change data capture. It's got really good record-level indexing that enables that, and a merge-on-read system that enables it to keep data current with very high performance for streaming scenarios. That sounds like a mixed story, but it really isn't. Iceberg is the standard. It's dominant. Apache XTable is an incubating technology that supports interoperability between the three table formats. Delta Lake also has a Universal Format, or UniForm, feature that lets users read its tables in Iceberg and Hudi. Do you expect to see many mixed environments with the different table formats? Farmer: I expect we are going to see convergence. Version 3 of Iceberg has done a pretty good job of unifying the data layer. It's got features like row lineage, for example, and the Variant data type, which is important for storing semistructured data. And Databricks has said pretty clearly that eventually, Delta and Iceberg will just use the same metadata and share tables. Yeah, there are some bridges like UniForm and XTable, which has good backing -- Microsoft and Google are backing it. But I think the pattern [of sharing metadata and tables] is probably the way things are going, rather than having a lasting split between the three different formats with integrations between them. Iceberg becomes the lingua franca of data. Does Iceberg have any limitations or challenges that data teams looking to deploy it should be aware of? Migration costs, for example? Farmer: There is a migration cost, and if you are doing it at scale -- migrating petabytes, which is absolutely possible nowadays -- that's a huge migration. And there are some things that people get stuck on -- issues with partitioning and file paths, for example, that make it messy to migrate. But even without that messiness, it <a href="https://www.techtarget.com/searchdatamanagement/opinion/Is-Apache-Iceberg-worth-a-full-migration">can just be a big job</a>. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> You have to build maintenance into your operational plan for Iceberg. Too many people discover that only after the system has started to degrade. </figure> <figcaption> Donald FarmerPrincipal, TreeHive Strategy </figcaption> </div> </blockquote> I think maintenance is a bigger issue. The way Iceberg works is it writes new metadata and new data files for every change. That creates the potential for performance deterioration over time. You end up with something a little bit like the old days when you had to defragment your disk drive. You have this proliferation of very small files, and you need to schedule maintenance of that. You've got to do file compaction, and you have to do expiration of data snapshots that have been taken. You need to clean all that out. As a result, you have to build maintenance into your operational plan for Iceberg. Too many people discover that only after the system has started to degrade. No doubt this will get fixed over time. But right now, there is a maintenance cost for Iceberg that there is not for Delta. That, I think, is one of the reasons we haven't seen a wholesale migration from Delta to Iceberg. Any additional advice or best practices for data teams on deploying and managing Iceberg? Farmer: My advice to people who ask me about these formats is that it's not just one decision. There's the choice of the table format, there's the catalog, there's a query engine, there's the governance and maintenance you have to do. Iceberg is a pretty straightforward choice now. But the catalog, in particular, is a sticky decision that you need to get right. Iceberg's REST catalog spec is very portable and has a great API. You could use Apache Polaris, which is an open source catalog purpose-built for Iceberg. But you have other options, which could include Unity Catalog, Snowflake Horizon Catalog, Dremio Open Catalog, AWS Glue, Hive Metastore and other catalogs. It's a really important decision. The choice of a catalog is going to define how Iceberg integrates into your enterprise environment. Craig Stedman is an industry editor at TechTarget who edits and writes articles on data technologies and processes. He has covered enterprise IT for more than 40 years. In a Q&A, consultant Donald Farmer explains why vendors are rushing to support Apache Iceberg and discusses its capabilities and deployment issues for data teams. https://cdn.ttgtmedia.com/visuals/searchDataManagement/data_warehouse/datamanagement_article_006.jpg https://www.techtarget.com/data-technologies/feature/Why-Apache-Iceberg-is-the-center-of-attention-in-data-platforms Tue, 30 Jun 2026 22:19:00 GMT Why Apache Iceberg is the center of attention in data platforms When organizations weigh the risks of deploying AI agents, their biggest worries point straight at the data layer. The top three AI agent concerns in <a href="https://omdia.tech.informa.com/om145238/research-report-ai-agents-the-game-changing-generative-ai-use-case">Omdia research</a> were all centered on data protection: data privacy (37%), security vulnerabilities (34%), and compliance and regulatory risk (29%). Those concerns are well-founded because the surface exposed to outside attackers keeps growing. Every new AI agent, integration and automated pipeline opens another path toward enterprise data, widening the attack surface as adversaries get faster at finding their way in. The same capabilities that make <a href="https://www.techtarget.com/searchcio/feature/ais-cybersecurity-paradox-how-CIOs-can-keep-up-with-change">AI useful to defenders</a> also lower the barrier for attackers. Leading AI models can find software weaknesses and exploit them at a speed and scale no manual security team can match. For the data and databases at the heart of every business, that shift is not theoretical. <a href="https://www.darkreading.com/threat-intelligence/2026-agentic-ai-attack-surface-poster-child">It is here</a>, and it is accelerating. <section class="section main-article-chapter" data-menu-title="Why AI has rewritten the data security equation"> <h2 class="section-title">Why AI has rewritten the data security equation</h2> The attack surface keeps expanding while the response window keeps shrinking. Known vulnerabilities, weak configurations, privilege drift and audit gaps can all be exploited before defenders manage to close them. Software vendors are shipping patches more frequently than ever, but the harder question is whether customers can actually apply them with confidence that nothing will break. AI agents can be instructed to leak data. Applications remain exposed to SQL injection and control bypass. Sensitive data can be overexposed across cloud and on-premises systems. Unpatched systems, insecure backups, stolen credentials and excessive privilege further widen the surface. As agentic AI takes on more autonomous work, the impact of any compromise or downtime grows. Patching, assessment, prevention, and recovery can no longer be treated as one-off exercises. Data security must become a continuous, automated, estate-wide discipline. </section> <section class="section main-article-chapter" data-menu-title="Three principles for database security at machine speed"> <h2 class="section-title">Three principles for database security at machine speed</h2> Organizations that treat database security as a continuous discipline rather than a periodic project tend to organize around three priorities: secure at source, secure at speed, and secure through resilience. <h3>Secure at source</h3> The most durable place to protect data is the <a href="https://www.techtarget.com/searchdatamanagement/news/366644845/Agents-are-altering-data-security-needs-Oracle-responds">data layer itself</a>. Security built directly into the database means protection travels with the data wherever AI accesses it. User-specific privacy rules enforced at the database level mean that neither SQL nor any AI agent acting on a user's behalf can access data the user is not authorized to see. In-database SQL firewalls address SQL injection at a layer that cannot be bypassed by bad actors, without introducing additional availability or latency issues. <h3>Secure at speed</h3> To keep pace with AI-accelerated threats, data security and lifecycle management must be automated. That starts with automated patching, threat detection and remediation, and always-on encryption at rest and in transit. Fleet-level tooling turns patching from a stressful project into a repeatable control, helping teams discover gaps, automate updates, govern consistent workflows and prove compliance. Testing and synchronization tools let organizations validate changes, enabling them to patch and upgrade with confidence and minimal downtime. Fleet-wide visibility tools classify sensitive data, assess user and configuration risk, monitor activity and prioritize remediation. <h3>Secure through resilience</h3> Even the best defenses assume something will eventually go wrong, so recovery has to be fast and certain. Tools that use <a href="https://www.techtarget.com/searchitoperations/tip/Combat-ransomware-with-continuous-backup-software-strategy">continuous backup</a>, immutable and air-gapped protection against ransomware lead to a quicker recovery. Distributed database architectures and high-availability designs help organizations survive failures, meet data sovereignty requirements and protect operational continuity. </section> <section class="section main-article-chapter" data-menu-title="Lowering the barrier to getting protected"> <h2 class="section-title">Lowering the barrier to getting protected</h2> Agentic AI is reshaping the threat landscape around the data that powers every organization. The organizations that come through this shift will be the ones that secure their data at the source, at speed and through resilience. And they will do it now, while the window is still open. Stephen Catanzano is a senior analyst at Omdia, where he covers data management and analytics.  Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors. </section> The same AI capabilities that power enterprise innovation also give attackers machine-speed access to database vulnerabilities, demanding a shift in how organizations protect data. https://cdn.ttgtmedia.com/visuals/digdeeper/1.jpg https://www.techtarget.com/data-technologies/opinion/Securing-data-against-AI-attacks-cant-be-a-side-project Tue, 30 Jun 2026 14:05:00 GMT Securing data against AI attacks can't be a side project Once a database specialist, Couchbase is growing beyond its roots toward becoming a data platform for agents with the launch of the AI Data Plane. Made generally available on Tuesday, the AI Data Plane is designed to provide a governed data infrastructure for agents by unifying frequently disparate capabilities such as <a href="https://www.techtarget.com/searchenterpriseai/tip/What-is-AI-agent-memory-Types-tradeoffs-and-implementation">agent memory</a>, real-time data retrieval and consistent data access that, when separate, can stall AI development projects. Specific features include Agent Memory,  which enables agents to store and retrieve information from previous interactions, Agent Catalog to help agents discover the tooling that enables them to execute tasks, and a self-managed <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol server</a> to standardize integrations between agents and models. Beyond the AI Data Plane, Couchbase unveiled a new version of its Enterprise Analytics platform that includes Apache Iceberg federation capabilities, so users can query <a href="https://www.computerweekly.com/news/366640193/Why-real-time-data-is-key-for-enterprise-AI">real-time data</a> from Couchbase and lakehouse tables without having to move or duplicate data. In addition, the vendor updated its Capella iQ natural language assistant and introduced tools that extend the AI Data Plane to edge devices. "Collectively, the AI Data Plane and accompanying features represent a significant update because they collapse previously fragmented data services into a single, governed architecture," William McKnight, president of McKnight Consulting, told TechTarget. "With this update, users can run AI agents more efficiently with lower token costs and higher accuracy." Devin Pratt, an analyst at IDC, likewise called the update significant, noting that what makes it valuable is what it removes -- the need to <a href="https://www.techtarget.com/searchdatamanagement/opinion/Converged-architecture-is-enterprise-AIs-missing-foundation">piece together separate systems</a> to inform agents -- rather than what it adds. "This lands right where the market already is," he said. "Enterprises are cutting down the number of data systems they run, and most already have AI agents in or near production." Based in San Jose, Calif., Couchbase originated as a NoSQL document database platform but, like fellow former database specialists such as MongoDB and Redis, it is expanding to include broader data management and AI development capabilities. Couchbase, which was founded in 2011, <a href="https://www.prnewswire.com/news-releases/haveli-investments-completes-acquisition-of-couchbase-302565846.html">was acquired by Haveli Investments</a> in September 2025 for $1.5 billion and is now led by CEO BJ Schaknowski, who replaced Matt Cain following the acquisition. <section class="section main-article-chapter" data-menu-title="A foundation for AI"> <h2 class="section-title">A foundation for AI</h2> Innovation in data management is largely aimed at helping enterprises build agents and other AI applications. Despite <a href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html">investing heavily in AI development</a>, many organizationscan't move projects past the pilot stage and into production because they struggle to discover and operationalize the relevant data required for AI tools to deliver accurate outputs. <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Collectively, the AI Data Plane and accompanying features represent a significant update because they collapse previously fragmented data services into a single, governed architecture. With this update, users can run AI agents more efficiently with lower token costs and higher accuracy. </figure> <figcaption> William McKnightPresident, McKnight Consulting </figcaption> </div> </blockquote> Providers from tech giants such as <a href="https://www.techtarget.com/searchdatamanagement/news/366644853/AWS-latest-to-introduce-context-layer-for-agentic-AI">AWS</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366643955/Microsoft-boosts-Fabric-to-make-it-a-foundation-for-AI">Microsoft,</a> through full-featured data platform vendors <a href="https://www.techtarget.com/searchdatamanagement/news/366644813/Databricks-intros-new-Genie-data-management-tools-to-aid-AI">Databricks</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366643795/Snowflake-barrage-adds-more-AI-development-analysis-tools">Snowflake,</a> to niche specialists including <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640792/Domo-doubles-down-on-AI-with-latest-platform-additions">Domo</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">Tableau,</a> have all introduced new features this year aimed at enabling customers to better connect agents with the right data. With the AI Data Plane, Couchbase is similarly trying to tackle the problem of providing agents with an accessible data foundation. Customer feedback combined with Couchbase's own observations drive the vendor to create the new capabilities, according to Barry Morris, the vendor's chief product and strategy officer. He noted that customers repeatedly requested integrated, out-of-the-box memory so they didn't have to integrate disparate systems, and the requests came from enterprises ranging from a toy manufacturer to a payments platform. "When companies with almost nothing in common independently ask for the same capability in the same language, that's not a feature request — it's a category forming," Morris said. "So we built the layer that removes those walls with Agent Memory, an MCP Server and Agent Catalog shipping as enterprise-supported components in this release." Agent Memory is designed to provide a unified memory layer within a broader data platform so that teams don't have to piece together tools such as <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector databases</a>, document stores and <a href="https://www.techtarget.com/whatis/definition/caching">caching</a> capabilities. In addition, it is built to be framework-agnostic so that developers don't have to build new memory layers each time they use a different development framework, such as LangGraph or LlamaIndex. Meanwhile, Couchbase's inclusion of Agent Catalog and <a href="https://www.techtarget.com/searchsecurity/tip/Secure-MCP-servers-to-safeguard-AI-and-corporate-data">the MCP server</a> within the AI Data Plane consolidate previously available capabilities into a single architecture. Given that insufficient memory is one of the problems that often stalls AI initiatives, Agent Memory is perhaps the most valuable feature within the AI Data Plane, according to Pratt. "What stops companies from scaling AI agents usually isn't the model, it's the data underneath," he said. "Get memory right and you've solved the real bottleneck." In addition, he noted that although Couchbase is on the right strategic path by adding data platform capabilities and consolidating previously fragmented capabilities in a unified architectural layer, the vendor is <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">in step with competitors</a> rather than pacing the market. Where Couchbase does stand out, however, is with its extension of its capabilities <a href="https://www.techtarget.com/searchitoperations/news/366641741/Edge-and-physical-AI-poised-to-upend-enterprise-networks">to the edge</a> through new features such as Couchbase Lite that syncs devices over Bluetooth and Wi-Fi, Pratt continued. "The idea [of a unified data layer] isn't unique anymore," he said. "Couchbase's real advantage is reach, one platform that runs the same way everywhere, which is how enterprises actually operate." McKnight likewise noted that while Couchbase distinguishes itself from <a href="https://www.techtarget.com/searchdatamanagement/news/366641958/Redis-unveils-Feature-Form-to-improve-AI-ML-workloads">its competitors</a> in some ways, it is largely providing similar features rather than market-moving ones. "Couchbase differentiates itself from competitors by offering high memory performance, a unified infrastructure that eliminates fragmented tools, and advanced edge capabilities like offline, peer-to-peer syncing," he said. "However, its updates also align with broader industry trends by integrating with standard data governance catalogs and supporting infrastructure consolidation." Regarding the strategy to evolve beyond its database roots, McKnight added that that Couchbase is making smart moves as <a href="https://www.techtarget.com/searchdatamanagement/news/366636081/Couchbase-adds-agentic-AI-development-suite-to-Capella-DBaaS">it expands</a>. "Couchbase is clearly on the right track by evolving into a unified operational data platform," he said. "Consolidating previously fragmented vectors, documents and caches into a single architectural layer is what enterprises need to overcome the primary bottleneck of scaling AI agents from simple pilots to production." </section> <section class="section main-article-chapter" data-menu-title="Future features"> <h2 class="section-title">Future features</h2> Just as the AI Data Plane is designed to aid AI development, Couchbase's upcoming product development plans focus on enabling customers to move AI projects into production at scale, according to Morris. Toward that end are the evolution from a database to a data foundation for AI, more <a href="https://www.techtarget.com/searchdatamanagement/opinion/The-race-to-build-the-ultimate-data-platform">consolidation of capabilities,</a> and extension beyond a single environment to include the many systems and devices where users do their work. "We believe a new category is forming, and that while our heritage … positions us to define it, it's much more than a growth beyond our roots," Morris said. "The database is part of what we do as the operational data platform, but only part of it." As Couchbase expands, it would be wise to add <a href="https://www.techtarget.com/searchbusinessanalytics/news/252507769/Gartner-predicts-exponential-growth-of-graph-technology">graph capabilities</a> that discover and connect data in different ways than others, according to McKnight. "To truly succeed in the next wave of agentic AI, combining this new AI Data Plane with Graph retrieval-augmented generation capabilities would be a competitive differentiator," he said. Pratt, meanwhile, suggested that Couchbase <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">make governance a focus</a> as it concentrates on enabling AI development. "The next step isn't storing AI memory, it's making it something companies can trust and afford," he said. "That's what our research keeps pointing to as the real holdup." 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 capabilities including Agent Memory and extension to edge devices help the vendor compete for market share as it grows beyond its database roots. https://cdn.ttgtmedia.com/rms/onlineimages/arvr_g1273484747.jpg https://www.techtarget.com/data-technologies/news/366645317/Couchbase-evolution-continues-with-new-data-layer-for-AI Tue, 30 Jun 2026 09:00:00 GMT Couchbase evolution continues with new data layer for AI Search Data Management Resources and Information from TechTarget 60 webmaster@techtarget.com