The Open Data Products standards family gives data products a shared structure. Each standard has a clear role. Together, they help humans, platforms, and AI agents understand how data products are defined, organized, connected, and interpreted.
Open Data Product Specification. Defines the data product, including metadata, access, quality, SLA, license, pricing, and strategy.
Read ODPS 4.1Open Data Product Catalogs. Organizes products into catalogs, portfolios, use cases, objectives, KPIs, and signals.
Read ODPC 1.0Open Data Product Graphs. Connects products to use cases, business objectives, KPIs, signals, and relationships.
Read ODPG 1.0Open Data Product Vocabulary. Provides the shared terms and semantics used across the standards family.
Read ODPV 1.0The family creates a context layer for data products. It helps people understand the portfolio, platforms exchange metadata, and AI agents reason across products, use cases, objectives, KPIs, and signals.
Business, technical, governance, quality, access, pricing, and strategy metadata for one data product.
Portfolio objects that group products by catalogs, use cases, objectives, KPIs, and signals.
Graph relationships that show how products, use cases, objectives, policies, and agents depend on each other.
Controlled terms and relationship vocabulary so the family is interpreted consistently.
AI agents need more than access to data. They need structured context that explains what a data product is, where it belongs, how it connects to business value, and which terms mean what.
The Open Data Products family supports this through machine-readable YAML and JSON, reusable schemas, shared vocabulary, graph relationships, examples, and tooling.
The Open Data Products Python SDK makes ODPS, ODPC, ODPG, and ODPV operational. Validate source files, detect document types, explain structures, search artifacts, traverse relationships, and expose the same standards-aware actions to AI agents through MCP.
$ pip install open-data-products
ODPS product specs, ODPC catalogs, ODPG graphs, ODPV vocabulary, schemas, examples, and data contract artifacts.
Validated metadata, readable explanations, searchable structures, relationship paths, and summarized product context.
Don't just take our word for it. Hear from the many organizations and professionals who have transformed their data strategies with Open Data Products, starting from the Open Data Product Specification (ODPS) foundation.
Here's what some of our satisfied users have to say about their experience:
"ODPS makes sense if you have multiple data products requiring consistent governance, want to scale governance without headcount, work in data mesh architectures, or want to provide structured metadata to your AI agents"
Michael Eichenseer
Data Engineer, Codecentric
"I'm building a UX PoC, v4.1 is a perfect balance between business needs & goals, formalizing a data interfaces/data layer and create a interoperable dataproducts."
Eugene Istomin
Founder, Memex.Team
"ODPS makes product thinking concrete for data and AI — explicit metadata, clear interfaces, lifecycle discipline, and governance by design."
Ron Tolido
CTO and Executive VP, Techstraordinary
"We at BASF built our group-wide Data Product concept largely on ODPS and extended it to our specific needs. Reasons to choose ODPS were its maturity especially in complex non-technical aspects."
Manfred Sorg
Group Data Office, BASF
"Infrastructure, security, persistency, services and processes all have their 'as-code' open standards within the Ferris platform. Now this stack is complete in adding ODPS as a unified standard for regionally distributed data owners."
Thomas Debus
Founder & CTO, Ferris AI
"ODPS fosters collaboration. With standardized data descriptions, we can easily integrate with data marketplaces, reaching a wider audience and accelerating innovation"
Rajanikanth Thouta
Executive Director, iSpatial Techno
Solutions
"Extending X-Road OAS service descriptions with ODPS makes data product-related metadata available in the service catalogue more unified, improves its quality, and enables more automation in the maintenance process."
Petteri Kivimäki
CTO, Nordic Institute for Interoperability
Solutions
"ODPS helps our platform to bridge the gap of discoverability and use of data products in the context of data spaces."
Lukas Hohl
Co-Founder & CPO, Beyond Civic
"ODPS provides a solid foundation for building robust and efficient generative Al workflows and pipelines"
Elias Helou
Founder, VesoAI
We'd love to hear your story! Share your insights and let us know how the standards family has made a difference for
you.
Your experiences can inspire others and help us enhance our offerings.
Find the specifications, schemas, examples, repositories, and SDK resources for the Open Data Products standards family.
ODPS remains the foundation, with ODPC, ODPG, ODPV, and the Python SDK extending the family into catalogs, graph relationships, shared vocabulary, and AI agent workflows.
Your playbook for defining, cataloging, connecting, governing, and launching data products, now made practical. Dive into real-world guidance, clear documentation, and step-by-step examples that help you move beyond theory and into execution.
Explore standards family examples to see how data products can be structured, cataloged, connected, and interpreted for human and AI workflows.
The following use cases show why a standards family matters. ODPS provides the product foundation, while ODPC, ODPG, and ODPV extend the model into catalog, graph, and vocabulary contexts.
Increase Internal Transparency and Data Reuse
Many organizations struggle
with internal data silos, limiting data sharing and collaboration. A Harvard Business Review
study shows that companies treating data like a product reduce time to implement new use
cases by up to 90% and decrease costs by up to 30%. ODPS breaks down these silos with a
transparent, machine-readable metadata model, enhancing data discovery, comparison, and
reuse across business units.
Provides a business-level data product
description model with 120+ attributes.Supports adding use case examples and linking
related data products.Defines data access with 9 attributes, enabling
easier data discovery and usage.
Boost Sales by Highlighting Data Value
Data monetization often fails
because customers don't perceive value before purchasing. ODPS improves customer experience
by detailing data quality, usage conditions, governance, and alternatives. It offers a
comprehensive framework to convey the data product's value clearly, supporting informed
purchasing decisions.
Includes 12 pricing plans, including freemium,
to allow value validation.Provides a data quality framework with 8
indicators to build trust.Offers a licensing model with 21 items to
communicate conditions and rights.Facilitate Data Product Team Collaboration
Transitioning to a
data-as-a-product approach changes team structures and responsibilities, requiring
cross-disciplinary collaboration. ODPS provides a standardized model to unify business,
technical, marketing, security, and legal efforts, facilitating effective collaboration and
reducing the need for custom solution
Contains attributes for licensing, pricing, and
governance.Supports marketing with detailed descriptions
and value propositions.Enables automation with DataOps attributes and
standard access definitions.
Advance Open Data to the Next Level
Traditional metadata standards like
DCAT are insufficient for the modern data marketplace. ODPS extends DCAT to support both
open and commercial data, improving discoverability, quality, and monetization
opportunities.
Offers an 8-indicator data quality framework and
SLA specifications.Supports open data monetization with pricing
plans and licensing flexibility.Ensure Consistency with Linting
ODPS enables automated linting to
maintain consistent "look and feel" across data products, improving maintenance and user
experience. A machine-readable specification allows organizations to apply design guidelines
effectively.
Facilitates early error detection and compliance
with design standards.
Enrich API Access with Business Metadata
ODPS integrates business
metadata into API specifications, providing a comprehensive view of data products, enhancing
usability, and supporting multiple languages for broader audience reach.
Provides a standardized framework for API
descriptions with business metadata.Prototyping and Mocking for Product-Market Fit
ODPS enables rapid
prototyping and mockups, allowing businesses to test product-market fit before full
development. It supports A/B testing and customer feedback gathering, minimizing waste and
guiding development.
Allows easy generation of product views and
supports iterative testing and feedback loops.
We want to hear from you! Share your insights with us, and let's collaborate to transform your vision
into reality.
Your feedback could lead to exciting new possibilities!
The Open Data Products standards family is developed for practical adoption by organizations, platforms, consultancies, developers, and AI agent builders.
Use the standards in your data product operating model, implement them in platforms, validate files with the SDK, or contribute feedback through GitHub.
The Open Data Products standards family is developed and maintained by people working across data products, governance, interoperability, AI readiness, and open standards.
Dr. Jarkko Moilanen
Maintainer, ODPS, ODPC, ODPV, and AI Agent SDK
Manfred Sorg of ODPS
Maintainer
DSc. Toni Luhti
Commercial operations
Tekla Wannas
Ecosystem & Marketing
Antti Poikola
Data Architecture Specialist
Zaher Abou Shakra, PMP®
Maintainer, ODPG and graph-based value relationships
Elias Helou, Veso AI
Applying Sovereign, Secure & Useful AI
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