Given the size and complexity of most mature organizations and their infrastructure, any effort towards data modernization can be a daunting task.

Even smaller, newer, nimbler businesses can experience this pain, as, in their haste to scale, they often cobble together multiple constituencies in a rush to build “whatever works.” But in both cases, when it comes time to either modernize a long-standing infrastructure or stitch together a disparate set of resources into a cohesive whole, the question becomes, “What do we do first?”

A Data Catalog Lays the Foundation for Your Stack

Many organizations feel the pressure to have their data stack completely pristine and perfect before they try to build the discovery, metadata, and automation around it; this is a mistake. Data governance is a messy, iterative process that is never “done,” and trying to navigate your way to self-service without a roadmap is akin to performing a high wire act without a net; Sure you can do it, but it’s really not a good idea. And it can easily end in disaster.

Most data catalogs treat data as individual, disconnected, static items to be cataloged and counted, rather than what they are: constantly evolving resources that have context and relationships, just like people. (If you haven’t read the piece by Barr Moses about the failings of traditional data catalogs, it’s well worth the read.)

The good news is that the next generation of data catalogs thinks about data in a different way. And a modern data catalog is built on top of a knowledge graph that values the relationships between data just as much, if not more, than the data itself. 

A Data Catalog Lets You Work With Your Data, Wherever it Is

Modernizing your data stack often means moving data to new systems, changing process and workflow, and disrupting much of the institutional knowledge around where to find data and how to use it. The good news is that by having a data catalog in place, your users don’t have to be impacted by these changes. The underlying data can move, and as long as you update where the catalog looks to find that resource, the end user can continue to seamlessly discover and use the data as it evolves.

While your data infrastructure needs to continually evolve to meet the needs of the organization, this shouldn’t mean that your users need to live in a constant state of tool fatigue. The goal of data governance should be to guide and enable your data workforce, not restrict and contain it.

A data catalog  meets your users where they are. And it serves as a constant information hub that connects to their preferred tools — whatever they are now or will be tomorrow — rather than itself being yet another “all-in-wonder” tool that needs to be learned, throwing out years of accumulated knowledge and skill.

A modern data catalog enables an agile approach to data governance that provides your team with quick access to clear, usable, and reliable data, empowering every member of your organization to make better, data-informed decisions, uncover greater insights, and drive continuous improvement across all facets of your business.

Agile data governance is a key area of focus at Our cloud-native SaaS platform combines a consumer-grade user experience with a powerful knowledge graph to deliver enhanced data discovery, agile data governance, and actionable insights. And, most importantly when you’re laying the foundation for your data stack, makes it easy to connect to and query live data across disparate sources via data virtualization and federated query.

Eureka™ Makes Cataloging Your Data Even Easier recently introduced Eureka™, an innovative suite of catalog capabilities that uses the power of the knowledge graph to deliver intelligent, scalable automations that help people more effectively and efficiently develop, discover, understand, and use trusted data products.

Eureka Bots™ makes it even easier to build your data catalog quickly and at scale. With other data catalogs, you have to invest in significant hands-on work to set up your catalog, adding relationships, tags, policies, stewards, and more through monotonous and time-intensive manual efforts.'s knowledge graph and semantic query engine help deliver the highest efficiency, even as you add and change data in your systems. Using SPARQL automations and transforms, you can define how relationships are built in order to streamline the process of metadata onboarding, data connections, and governance. The end result is powerful context and search for all of your users in a fraction of the time.

Learn More About Building Your Data Stack With a Data Catalog Foundation

To learn more about how’s cloud-native enterprise data catalog lays the foundation of your modern data stack, download a product overview now.