In the modern AI era, the enterprise data catalog market is undergoing dramatic changes.
Traditional catalogs were static repositories where users searched for data and documentation. Many vendors branded the technology as data intelligence platform.
Early AI improvements to data catalog implementations were designed to revolutionize data accessibility, but they often produced inconsistent results that enterprises could not trust when making critical decisions.
A new generation of metadata aware AI agents promises to bridge the gap by maintaining business context throughout conversations and providing the accuracy levels enterprises require.
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Alationis one of the largest independent vendors of data intelligence platformswith 40% of Fortune 100 as customers. It has been steadily increasing its AI capabilities in response to the changing needs for data.
The company announced today its latest set AI capabilities with a data query enhancement capability called ‘Chat with Your Data.’ This enhanced capability claims to increase answer accuracy by as much as 30%.
A fundamental shift in enterprise expectations is reflected in the transformation of data catalogs. Organizations do not want separate systems for data governance, analysis and discovery. They demand unified platforms which democratize access to data while maintaining the precision needed for business-critical decision making.
Satyen Sangani is CEO and cofounder of Alation. He told VentureBeat that generative AI has a significant impact on data management.
Traditional catalogs of data operated on the destination model. Users navigated to a platform, searched for data and browsed results. This approach worked well when data teams acted as intermediaries between data systems and business users. Sangani stated that Alation was previously sold primarily to data management professionals. “We’re finding that more and more CIOs,CTOs, and CPOs are using Alation to build agents,as well as to roll out technology, in order to be capable of building agents, while simultaneously making sure that these agents are appropriately managed and governed.” AI can make a huge difference for users who want to access the data and answers they need without having to worry about the complexity of data platforms.
Sangani said, “I think that the world has been turned on its head, and chat is the new medium for people to access self-service data. Catalogs were the old medium.”
Alation’s approach is centered on what Sangani refers to as a “knowledge-layer” of curated products and comprehensive metadata. Alation, which has been developing its own data catalogs and governance capabilities over the last decade, recently acquired Numbers Station in order to build agentic AI capabilities. Sangani explained that Numbers Station built agents on structured data. “They realized that the building of these agents was not so much an AI issue as it was a problem with metadata and evaluation.”
Numbers Station’s technology is now at the core of Alation’s chat capabilities. This integration allows users the ability to query their data via chat, making it more accessible and queryable on a large scale. The technology is aimed at ensuring the availability of the right metadata, the evaluation of agent precision, and the correct tuning and instructions for agents.
The data intelligence market is a highly competitive market.
The traditional data catalog market is not lacking in competition.
Each of the large data platform vendors, including Databricks, Snowflake and Collibra have their own technology. Informatica is also active in this space, as are Collibra and Atlan. Informatica is currently being acquired by Salesforce. Analyst firm Forrester, in the midst the competition, positioned Alation a leader in their Q3 2025 evaluations of data governance solutions.
Alation differentiates itself by remaining compute-agnostic, and focusing more on the metadata and evaluation layers than building a vertically-integrated stack. Sangani said, “We don’t view ourselves as a vendor of compute.” We allow you build these precision agents and test and evaluate them. And, just as important, we allow this agnostic to any underlying compute.
The approach addresses enterprise concerns over vendor lock-in and solves the precision problem which has limited AI adoption for structured data scenarios.
We believe that data management has become a fundamental part of the construction of business process, and we find that exciting.
How AI-powered data catalogs power real world intelligence
Market intelligence company Alation is integrating Alation’s conversational data intelligence into its Passport platform which serves more than 2,500 organizations around the world.
Euromonitor’s data stack includes a cloud native data warehouse for structured information, which is fed from a variety sources, including operational database, third-party apps and internal systems, through data integration and ETL.
Business Intelligence and Analytics tools are layered on top. This allows analysts to create reports and dashboards that are available in Passport. Cloud-based machine learning is used by the company’s data scientists to build predictive models and perform advanced analytics. Euromonitor engaged Alation in order to enhance Passport AI by adding natural language insights to its statistical data. Lamine Lahouasnia is Director of Gen AI for Euromonitor International. She told VentureBeat that this capability enables our customers to quickly access insights using natural language queries, without having to configure complicated filters. It allows our users to discover insights and data that may have been hidden previously.”
Lahouasnia explained the previous workflow, which required clients to navigate through multiple pages and complex filtering to find specific market information. Users had to restart searches after refining their criteria. This led to bottlenecks and slowed down customer decision-making.
With the conversational interface, clients can ask questions in plain English. They will receive immediate answers and full transparency. The system displays the data sources, calculations, and reasoning behind every response.
This implementation also allows flexible data aggregation. Lahouasnia, for example, said that Euromonitor Passport includes pre-calculated region groupings such as the Middle East and Africa. He said that clients define regions differently depending on their business needs. The conversational interface allows for custom aggregation using client-specific definitions, without the need for manual processing and data extraction.
How enterprises should implement data intelligence
Euromonitor underwent what Lahouasnia called a “rigorous” process when selecting a vendor.
This process and the overall journey revealed several key lessons and best practice:
The foundation is trust: Never sacrifice accuracy, especially if your data is a service. Look for a solution with clear definitions and metrics of quality. When users are able to see where and how data was gathered, they will be more likely to trust the results and use them for important decisions.
Focus people and processes: Data intelligence platform is cultural shift. You must put in time and effort into change management. Assign data champions to different business units. Establish clear governance roles and provide ongoing training. Your people will be the ones to drive success, not the technology.
Don’t let your data overtake your governance. Implement a solution to enforce your existing security policies right from the beginning. This proactive approach ensures that data is always protected, and reduces risks. Strategic partnerships are important: The technology alone is not enough.
Our partnership with Alation was a key component of our success. This is especially true for legacy data structures which don’t always work in standard configurations. It was very helpful to have a partner who guided us through our data and gave recommendations on how AI agents could best work with it.
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