As a full-stack developer, Roman Shmyhelskyi has been dealing with all aspects of processing and storing data from the first days of his career. Over the recent years, the importance of data has grown dramatically, with every business trying to use it in a smarter, more sustainable way. At the same time, data catalogs—employed in virtually every business system—have evolved from static metadata repositories into dynamic, AI-powered platforms that drive operational efficiency.
In this interview, Roman explores the evolving role of data catalogs in today’s cloud environments and their critical function in enhancing data quality, compliance, and AI readiness. Join us as we delve into the strategic impact of modern data catalogs on enterprise data ecosystems and the exciting possibilities ahead.
How important are data catalogs in today’s data-driven landscape, and what role do they play in modern enterprise environments?
Data catalogs are becoming super important these days because companies are dealing with huge amounts of data. They collect and process petabytes of information in various formats, scattered across different systems, servers, and cloud platforms. This immense diversity and scale of data present big challenges when it comes to locating, understanding, and using information efficiently.
Data catalogs are the remedy here because they provide a centralized inventory of data assets that helps combine and contextualize data in one place. By cataloging metadata—information about data such as its source, structure, usage, and quality—these tools help users quickly discover and trust the data they need. This saves time and reduces errors that occur when working with incomplete or misunderstood data.
Data catalogs also improve data governance by tracking data lineage and enforcing compliance policies, which is critical in an environment of growing regulatory demands. They enhance data quality visibility by providing insights into data accuracy and reliability.
All in all, data catalogs are indispensable in modern enterprises, transforming fragmented datasets into usable, actionable resources.
In your view, how has the concept of a data catalog evolved in the last few years? What limitations did you see in traditional tools?
Historically, data catalogs were simple, static lists of metadata, usually entered by hand and disconnected from actual data pipelines. Because of this, they were hard to maintain, quickly became outdated, and didn’t reflect real-time changes in data. These legacy catalogs were clunky, not user-friendly, and mostly served as basic documentation tools without integrating into user workflows. They failed to keep up with the growing complexity and volume of data in enterprises.
Modern data catalogs, by contrast, are dynamic platforms that automatically discover metadata through APIs, event streams, and orchestration tools. This automation saves significant time for data analysts and stewards, reduces manual work and improves accuracy. Today’s catalogs provide active metadata, including data usage, quality metrics, and operational logs, offering a 360-degree view of data assets. They also track data lineage, showing the full lifecycle of data from source to consumption, which helps with impact analysis and troubleshooting.
Additionally, modern catalogs enforce governance policies automatically, ensuring compliance and data security. They incorporate versioning and collaborate with data engineering and DevOps tools, making them essential for managing data in real time.
From your perspective, what are the most pressing needs data catalogs are now expected to address beyond traditional metadata indexing?
Enable data quality, provide end-to-end lineage, support API and various data integrations, prepare data for use in AI, enforce and monitor policies, provide insights on metadata, and be flexible with integrations across data sources and ecosystems.
Honestly, data catalogs today have to do way more than just list out metadata. One of the biggest things they need to handle is making sure the data is actually good—accurate, complete, and reliable—because without that, nothing else really works. They also have to track data all the way from where it starts to where it ends up, so teams can figure out what’s going on if something breaks or if they need to check compliance.
On top of that, these catalogs need to connect with tons of different APIs and data sources, so data can flow smoothly between all the tools and systems companies use. And with AI and machine learning becoming huge, catalogs also have to help get the data organized and enriched for better results.
Plus, they’ve got to keep an eye on rules and policies, making sure everything’s secure and compliant, while giving teams useful info about how data’s being used—all in a way that can keep up as things change.
How have increasing demands around data quality, compliance, and AI readiness influenced the design and expectations of modern data catalogs?
Catalogs these days need to do a lot more than just list data—they should actually show quality metrics so you know how good the data really is, spot sensitive information to keep things secure, and help support machine learning and AI workflows.
For example, they use event-driven architecture to constantly collect metadata in real time, so they’re always up to date with what’s going on across data systems. They also use profilers—these are tools that scan the data to measure quality and flag anything sensitive, like personal info or confidential data.
On top of that, catalogs connect through APIs to other specialized tools, including lineage providers, which track where data comes from and how it moves through different processes. When it comes to prepping data for machine learning, catalogs include dataset features that help organize and enrich data so models can learn better and faster.
And to make all this usable, they often offer conversational search—a way to ask questions in plain language and get clear, insightful answers by combining all the data and metadata behind the scenes.
Why do you think organizations are starting to see data catalogs not just as documentation tools, but as operational enablers?
Organizations are starting to see data catalogs as way more than just boring documentation tools because they’ve gotten a lot easier to use and smarter about how they fit into the whole data ecosystem. Instead of being some clunky, standalone thing, modern catalogs slide right into existing systems and workflows, which means they don’t slow anyone down—they actually speed things up. They come packed with features that automate a ton of the manual, repetitive tasks that used to take forever, like tracking where data comes from or checking if it’s high quality.
That automation saves a massive amount of time and reduces mistakes, which lets everyone—from data engineers to business analysts—focus on the work that really moves the needle. At the end of the day, data catalogs boost the productivity of the whole company by making data easier to find, trust, and use in day-to-day decisions and projects.
What role do you see automation and AI playing in the next generation of data cataloging solutions?
Nowadays, AI is pretty much a must-have in any modern data catalog. Most companies start by adding agentic AI that lowers the barrier for non-technical users—things like conversational search make it way easier to find what you need without messing around with complicated queries.
But honestly, that’s just the tip of the iceberg. Down the line, AI could do way more advanced stuff like auto-ingestion and classification, smart recommendations on which datasets to use, anomaly detection, and a whole lot more. We’ve really only just scratched the surface when it comes to AI in data catalogs, so I genuinely believe there’s a ton more potential to unlock.
For example, highly specialized AI models can make way more sense economically compared to huge, generic ones everyone talks about. It’s an exciting space that’s only going to grow smarter and more useful.
How has the shift to hybrid and multi-cloud environments changed how we think about cataloging data assets across complex architectures?
As enterprise systems keep evolving and becoming more complex, data catalogs have to keep up and evolve right alongside them. Nowadays, it’s not just about handling one database or platform — catalogs need to support multiple distributed systems like Snowflake, BigQuery, Redshift, on-premises databases, and even various SaaS sources. The goal is for the catalog to act as a single, unified point of view that aggregates and normalizes metadata from all these different places, so users don’t have to jump around between tools.
Beyond just collecting metadata, it’s really important for catalogs to offer strong external integrations and expose that data through APIs, making it easier for other applications and teams to access and use the information. For instance, integration with Identity and Access Management (IAM) systems is critical because that’s how many enterprises control permissions and keep data secure. Another big piece of the puzzle is performance and scalability—since data volumes are only growing, catalogs can’t afford to slow down; they need to deliver answers quickly and reliably, even as they handle more data every day.
Ultimately, modern data catalogs have to be flexible, fast, and deeply integrated to meet the demands of today’s sprawling, multi-platform environments.
