Data Products: Design, document, and govern reusable data products

Define data products, document contracts and schemas, and expose them in the Dataedo Portal so teams can safely reuse curated, high-value datasets.

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Vanderbilt University
Bridgestone
Garmin
Roche
Federal Reserve Board
Fujitec
Hunter Engineering
KPMG
ArcelorMittal
National Film Board of Canada
NHS
SportClips
IOWA
M&T Bank
Dublin City Council
PBS

Turn raw tables into trusted, discoverable data assets

Data products should be self-describing, discoverable assets that teams can trust and reuse without asking "who built this?" Dataedo helps domain teams define data products with clear contracts, publish them to a central portal, and govern quality through their lifecycle.

The challenge

Reinventing the wheel

Reinventing the wheel

Teams repeatedly recreate the same customer or product datasets instead of reusing trusted data products.

No shared standards

No shared standards

Without common definitions or data contracts, teams struggle to create consistent and reliable data products.

Unclear dataset statuses

Unclear dataset statuses

Data consumers can’t easily tell which datasets are production-ready and which are experimental or incomplete.

Cascading quality issues

Cascading quality issues

Quality issues in upstream tables cascade through dozens of downstream reports.

How Dataedo helps

Define data products with clear ownership

Use Dataedo Ownership to assign domain stewards to each data product. Document SLAs, freshness guarantees, and support contacts.

Define data products with clear ownership

Create data product contracts

Use the Business Glossary to define core entities with business rules, field definitions, and usage guidelines. Link terms to physical implementations.

Create data product contracts

Catalog source-to-product lineage

Use Automatic Data Lineage to show how raw operational tables transform into curated data products through ETL pipelines and quality checks.

Catalog source-to-product lineage

Tag data products for discoverability

Apply badges and classification tags like "Certified", "Production Ready", "Self-Service Approved" to distinguish trusted assets. Document deprecation plans.

Tag data products for discoverability

What you get

Reduced duplicate work

Reduced duplicate work

Teams reuse existing data products instead of rebuilding the same datasets from scratch.

Faster analytics delivery

Faster analytics delivery

Consumers can immediately trust and adopt certified datasets for reporting and analysis.

Clear domain ownership

Clear domain ownership

Each team manages its data products end-to-end, improving accountability and consistency.

Key features
Badges

Badges

Visually highlight trusted, maintained, or recommended datasets, helping users quickly choose reliable data products without additional validation.

Business Glossary

Business Glossary

Standardize business terminology across domains so data products use consistent definitions that are easy for both producers and consumers to understand.

Data Lineage Diagram

Data Lineage

Provide transparency into data dependencies and downstream impact, helping teams understand how changes affect analytics and reusable data assets.

FAQs

What is a data product?
A data product is a curated, reusable dataset designed for consumption by analytics, reporting, or AI teams. It includes clear definitions, ownership, documentation, and quality standards so users can confidently rely on the data.
Why are data products important for modern data teams?
Data products reduce duplicated work by allowing teams to reuse trusted datasets instead of rebuilding them. They improve consistency, accelerate analytics, and make data easier to discover and understand across the organization.
How do data products differ from traditional datasets?
Traditional datasets are often created for a single use case, while data products are intentionally designed for reuse. They include documentation, ownership, governance rules, and defined expectations for quality and reliability.
How do users know which data products are trustworthy?
Certification badges, documentation, and governance metadata help distinguish production-ready data products from experimental or deprecated datasets, enabling faster and safer adoption.
How do data products support a data mesh approach?
In a data mesh model, domain teams own and manage their data as products. Clearly defined ownership, shared standards, and discoverability enable decentralized teams to publish reusable data assets responsibly.
Can data products improve analytics and reporting speed?
Yes. When teams can quickly find certified, well-documented data products, they spend less time preparing data and more time generating insights.
Why our customers love Dataedo?
Piotr Kononow

Piotr Kononow

Founder

Turn raw tables into trusted, discoverable data assets

Explore Dataedo through a preconfigured data catalog with sample data, try it with your own data during a 14-day free trial, or book a demo.