Data Product Ontology (DPROD)
An OMG standard for describing Data Products using W3C Linked Data technologies, enabling interoperability and discoverability in decentralized data ecosystems
What is DPROD?
The Data Product Ontology (DPROD) is an Object Management Group (OMG) standard that profiles the W3C Data Catalog Vocabulary (DCAT) to specifically describe Data Products. As organizations increasingly adopt decentralized data architectures like Data Mesh, DPROD provides the standardization needed to ensure interoperability and unlock the full potential of distributed data ecosystems.
Built on established W3C technologies including DCAT, RDF, OWL, SHACL, and PROV, DPROD offers a clear schema for describing data products, ensuring they are discoverable, interoperable, and treated with the same level of accountability as traditional products.
Origins & Standardization
Key Benefits
Articles & Talks
Tony Seale: Data Products & Ontologies (DPROD)
A practical introduction to DPROD as a “first step” towards a distributed knowledge graph—covering JSON-LD contexts, linkable product identifiers, and connecting outputs to shared semantic schemas.
Read the articleOMG announcement: DPROD published for public comment
The official OMG news release explains the motivation for DPROD, the Request for Comments process, and the problems it targets (inconsistent metadata, limited discoverability, and interoperability).
Read the OMG releaseWorkshop video: AI agents with reusable Data Products (DPROD)
A practical session on building reusable semantic data products with DPROD and connecting them into a decentralized knowledge graph for AI/agent use cases.
Watch the videoagnos.ai: Beyond Data Mesh—how Virtual Knowledge Graphs prevent “Data Mess”
A perspective on why “data products” alone are not enough—without a semantic foundation, decentralized ownership tends to create fragmentation. Links Data Mesh concepts to operational knowledge graphs and governance.
Read the articlePodcast: Knowledge-first Data Products & the Data Economy (Jacobus Geluk)
A discussion of use case-driven approaches and semantic coordination as foundations for scalable data product marketplaces—useful context for why standards like DPROD matter.
Open the podcast pageOntologies & LLMs (Tony Seale)
Background reading on why formal semantics matter for AI—and why linking data products to shared concepts helps make data more machine-understandable.
Read the articleCore Concepts
Data Product
A rational, managed, and governed collection of data with purpose, value, and ownership, meeting consumer needs over a planned lifecycle. Data products have input and output ports, code, and metadata.
Ports (Data Services)
Digital interfaces that provide access to datasets. Input ports bring data into the product, while output ports share generated data. Ports specify connection details, formats, and link to datasets with shared schemas.
Distributions & Datasets
Distributions specify physical representations (CSV, JSON, Parquet) and access methods. Datasets provide logical models and can conform to shared standards like FIBO, CDM, or custom ontologies using SHACL or OWL.
Use Cases
Data Mesh Implementation
Enable domain-oriented decentralized data ownership with standardized product descriptions
Data Marketplaces
Build internal or external data marketplaces with discoverable, well-described data products
Multi-Cloud Integration
Integrate data products across different cloud platforms and vendors with vendor-neutral descriptions
Data Governance & Compliance
Track lineage, enforce policies, and maintain quality metrics across all data products
- •Complete ontology with classes and properties
- •SHACL shapes for validation
- •JSON-LD context for easy JSON integration
- •Worked examples and best practices
Get Started with DPROD
Ready to standardize your data products? Explore the documentation or reach out to the EKGF community for support.