Beyond Blueprints: Turbocharge Your Data with Models and Catalogs

Embarking on the journey of effective data management requires understanding the intricate dance between data models and data catalogs. Picture this: data models as the architects sketching the blueprint for a database, while data catalogs serve as the interactive map guiding explorers through the vast landscape of an organization's datasets. Both are related in their own right yet distinct in their purposes in data management.

Data Models

A data model represents the structure, relationships, and constraints of data within a system. Often, the representation is visual, in the form of a logical or physical entity relationship diagram (ERD). The visual representation describes how the different data elements are organized, how they interact with each other, and the rules governing them. In essence, a data model is the blueprint for constructing a database.

Data Catalogs

A data catalog is a searchable inventory of an organization's datasets, tables, files, and other related information. It enables users to locate and understand the available datasets. The content can (and should!) be comprehensive and wide-ranging, including metadata such as data source, format, quality, ownership, and usage.

Data Model vs Data Catalog

While data models focus on the technical structure and relationships between data, data catalogs, on the other hand, concentrate on providing metadata and managing the accessibility of data assets. Both play vital roles in data management. Here's a summary of how their core purposes differ:

  • Data models provide a detailed representation of data, ensuring consistency and accuracy within databases
  • Data catalogs offer a broader view of available data assets, facilitating information exchange, data governance, and data-driven decision-making

Thus, a data catalog is concerned with discoverability: it enables teams to find datasets. A data catalog helps reduce risk and uncertainty by helping to determine whether particular data is already available. It helps avoid inefficient and confusing duplication by uncovering cases where the same data is curated in multiple places. Including information such as lineage, responsibilities, and classification helps a data catalog underpin data governance and compliance.

A data model helps users understand how to use the datasets, ensuring data quality and integrity, efficient storage, and effective retrieval.

In a geographical information system (GIS) analogy, where the map is the data model, a data catalog acts like a gazetteer.

When to use a Data Model and a Data Catalog

There's a very simple answer to this: always!

To explain, here's a chart created with no data model and no data catalog.

Chart with no data model and no data catalog

What insight can we gain from the above that will help the business? Absolutely nothing! The axes can't be labeled without a data model or a data catalog. The chart is meaningless: there's no way to interpret it.

Obviously, this extreme example is nonsense. But it does illustrate an important point. When one person builds a chart to monitor a KPI or test a hypothesis, they always have a data model and a data catalog in mind since they know what they are trying to measure and how they collected the data.

Documenting and publishing the data model and the data catalog means that other people can find and understand that data, too.

Therefore, Shared data models and catalogs become more critical when more people access the data and when the analytics solution is intended to be long-lived.


In summary, a data model is a design tool ensuring data storage and retrieval integrity and efficiency. Conversely, a data catalog is a comprehensive inventory and metadata repository for an organization's data assets.

Data models and data catalogs are created during the design and development phase of a database or information system. However, to ensure that they remain accurate and relevant and help keep your data team productive, it's vital to maintain them as systems evolve over time.

About the Author

Ian Funnell, Data Alchemist at Matillion, curates The Data Geek weekly newsletter.

Ian Funnell
Ian Funnell

Data Alchemist