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By Patrick Elder
Director, ECS Data & AI CoE
Jason Turner
Senior Data Solutions Architect
Thomas Eldering
Data Solution Architect
Charise Arellano
Portfolio Director

The Data Mesh Pyramid

Data governance and intentional maturity provide a backbone for improved visibility into data use and ultimately data products.

The data inventory is a high-level index of the data in the enterprise that is both machine and human readable. Like an index at the end of a textbook, it documents an essential set of facts about the data and acts as a foundational resource for referencing and finding data in the enterprise.

In a mature data mesh, the data inventory acts as an index of raw material that can be developed into data products, which are published in a robust data catalog. Users can access those data products to support mature data services and analytics, positioning agencies to make use of emerging and anticipated technologies like AI/ML, ontology integration, and semantic reasoning.

It’s worth taking a moment to make a clear distinction between the data inventory and data catalog, as it may not be immediately clear when a data consumer would want to seek out data from one versus the other. The key difference lies in the nature of the data being sought and who is seeking it. Data catalogs contain ready-to-use data products. For the vast majority of your enterprise’s consumers, the catalog is where they will go to retrieve the relevant data products they need to drive their services and/or analytics. The inventory, on the other hand, simply confirms that the data exists somewhere in the organization, even if there’s no current way to make use of it. Hence, it’s your enterprise’s domain experts who will use the data from the inventory to curate new data products for the benefit of the enterprise.

Meeting the Federal Data Strategy With ECS

For federal agencies, the transformation into AI-ready, machine-readable, and machine-reasoning organizations is at hand. ECS’ experts are committed to helping your organization evolve toward the enterprise model of the future.

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REFERENCE: Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media, 2022.


Data Mesh — A decentralized data architecture that organizes data by a specific business domain, provides more ownership to the producers of a given dataset, and allows them to set data governance policies, enabling self-service use across an organization.

Data Inventory — An index of all the data within an organization and the foundation for data mesh architecture.

Data Product — A self-contained data “container” that directly solves a business problem or is otherwise monetized; e.g., an application or tool that uses data to help businesses improve their decisions and processes.

Data Catalog — Uses metadata — data that describes or summarizes data — to create an informative and searchable inventory of all data products within an organization.

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