How to build a data catalog: 10 key steps

Why are data catalogs important?

The data catalog is a core component of modern data management environments. It's a reference application that enables business users, data scientists, BI analysts, data stewards and other workers to explore data sets and understand their contents. Ideally, a catalog helps users become more self-sufficient at finding and accessing relevant data for operational and analytics applications. It also facilitates collaboration and knowledge sharing about data assets among users.

Data catalogs collect metadata from databases, data warehouses, data lakes, BI systems and other sources and use it to create a unified inventory of data assets with built-in search and data discovery functions. Catalogs also provide a single point of reference for enterprise metadata management, which they can handle more efficiently and effectively than older types of metadata management systems.

They automate aspects of the data management process, too. For example, commercial data catalog tools from various vendors use AI and machine learning technology to create data profiles, check data quality, curate data sets and handle other tasks. Mechanisms for enforcing data governance policies, as well as data security and privacy controls, can also be embedded in data catalogs to ensure data is protected and used properly.

Key steps to build a data catalog

Here are 10 important steps to take when planning and building a data catalog for your organization.

1. Document metadata management's value to data governance

All effective data governance programs are supported by efforts to manage both business and technical metadata. The two forms of metadata add context to the contents of data sets and provide information that makes the data usable and understandable across an organization. Properly managing metadata helps organizations improve data quality and operational effectiveness through their enterprise data governance policies, practices and standards. Documenting these expected governance benefits should be part of your business case for a data catalog.

2. Identify data stewardship uses for the catalog and related metadata tools

Organizations commonly supplement their data catalogs with business glossaries and data dictionaries, which provide additional information to help users understand data and its business context. Although the three metadata tools are sometimes referred to interchangeably, they're not the same thing:

• A business glossary defines and provides an authoritative source for the business terms used in an organization.
• A data dictionary contains technical information about data, including the properties of attributes such as data type, length and constraints; valid and default values; relationships with other data fields; and both data transformation and business rules. Dictionaries also support physical metadata detailing where and how data is stored.

Business glossaries align with the business aspects of data stewardship, while data dictionaries are primarily the domain of technical data stewards. Both business and technical data stewards can use a data catalog for tasks such as managing metadata and enforcing data standards. Mapping out data stewardship actions and responsibilities across the different tools is another upfront step in the process of building a data catalog.

3. Design a subject area model for your organization's data

An effective data catalog reflects the business use of data, not simply the technical implementation of the IT systems where the data is created and stored. To enable this, a subject area model (SAM) defines different subject areas for an organization's data, such as products, customers and sales, as well as the business concepts each one encompasses.

The SAM serves as the underlying foundation of your overall data architecture, and both the data catalog and a business glossary should be based on it. A well-constructed SAM includes descriptions of the individual subject areas and the types of data associated with them. This helps catalog users locate data unconstrained by applications, files or databases.

4. Build a data glossary

An organization should have an enterprise business glossary instead of one for each functional area or -- even worse -- application. A comprehensive business glossary is an essential component of effective data stewardship and business metadata management. It also provides valuable content for use in the data catalog.

The data governance team and business data stewards should collaborate to design and populate the business glossary. The data stewards must be involved because they have the best understanding of their subject area's data and its associated business metadata, which includes definitions of key terms in business language and common usage examples.

5. Build a data dictionary

The data dictionary should contain descriptions and mappings of every data table or file, along with their metadata entities. It then becomes the basis for pulling the metadata into the data catalog. Again, business data stewards are essential here: They'll provide guidance on the business metadata to include in the data catalog, by source, concept and subject area. However, teams of technical data stewards and other IT staff members should take the lead in building the data dictionary to ensure that the technical metadata is accurately represented.

6. Discover metadata in databases and other data sources

Data catalogs use stored metadata to identify relevant data tables and files for users. A catalog searches databases and other data repositories, then loads the associated metadata into its inventory of data assets. Before building the data catalog, identify and document these metadata sources. This step, like the previous two, requires a solid data stewardship program. In this case, business data stewards must provide insight into the correct data sources to use, usually in conjunction with the appropriate technical teams.

7. Profile data to provide helpful statistics for catalog users

The data profiles produced in this step are informative summaries that explain the metadata collected in the data catalog to users. A database profile often includes the number of tables, files and rows, plus other statistics such as counts of valid, invalid and duplicate rows. Data profiling also captures information on data types, patterns in data sets and the distribution of data values, among other items. Corresponding data profiles in the business glossary focus on the business metadata contained there and its use across the organization by business data stewards and users.

8. Identify relationships among data sets in different sources

Build information about related data sets across multiple data stores into the data catalog so users can see and understand the relationships. As part of this step, business data stewards should identify common definitions and potential uses of related data to help analysts and other users choose the optimal sources. For example, a data analyst may need consolidated customer data for an analytics application. Through the data catalog and the data dictionary, the analyst might find that five files in five different systems contain relevant data.

9. Capture information on data lineage in the catalog

Extract, transform and load (ETL) tools are used to extract data from source systems, transform and cleanse it, and load it into a target data repository. The metadata collected during the ETL process includes data lineage documentation, which tracks information such as the data's origin and how it flows through systems. Incorporate data lineage info into the data catalog to help end users understand data assets. It also enables data stewards, IT staffers, analytics teams and data quality analysts to examine data flows and trace data errors back to their root cause in source systems.

10. Organize the catalog for use by data consumers

Most databases and file systems are designed for hands-on use by the IT teams that manage them. However, design data catalogs and business glossaries for business users, data analysts and other data consumers as much as for technologists. Base their structure on the subject area model designed earlier in the process. Catalogs and glossaries should also be accessible from both PCs and mobile devices for user convenience. A data dictionary, by comparison, can be organized by functional area and application, given its technical nature.

Best practices for building a data catalog

Building a data catalog and then collecting and organizing metadata in it are tasks that should involve teams from both IT and the business. Doing so ensures that the metadata focuses on the needs of business users. It also drives consistent metadata management across the enterprise. This IT-business collaboration is also required for building a business glossary and a data dictionary.

The following are other data catalog best practices that data leaders should keep in mind:

• Incorporate user permissions, usage monitoring, sensitive data tagging and other data security and privacy protections into the data catalog.
• Enable collaboration among catalog users through features such as rating and commenting on data and a built-in chat function.
• Develop an end-user training program to ensure people are familiar with the data catalog and can use it effectively.
• Create a process to keep the catalog up to date as data assets and business requirements change.

Business value of a data catalog for AI and analytics

If end users can't find relevant data, both business operations and analytics initiatives will be less effective. But data catalogs play a particularly crucial role in enabling AI and analytics applications. By providing a unified repository for metadata as well as data lineage and data governance information, catalogs help data scientists and analysts discover, understand and trust data assets. This ensures that they can quickly locate relevant data sets for developing AI and analytical models.

Integrating data catalogs with AI workflows enables data management and governance teams to maintain data consistency, improve data quality and enforce compliance standards. Additionally, catalogs facilitate collaboration across departments by offering clear data documentation and context, which accelerates decision-making and enhances the overall efficiency of AI and analytics initiatives.

Final takeaways

Successfully planning, developing and implementing a data catalog supports effective data governance and metadata management. A comprehensive, well-organized catalog empowers data stewards and makes data easier to find, access and use for strategic planning, operational decision-making, analytics and other business purposes. Ultimately, it provides long-term business value by fostering a deeper understanding of data assets enterprise-wide.

Editor's note: This article was updated in December 2025 for timeliness and to add new information.

Anne Marie Smith, Ph.D., is an information management professional and consultant with broad experience across industries. She has also designed and delivered numerous data management courses and educational programs.