What is Open (Source) Data Product Specification?

Suitable for both open and commercial data products , ODPS offers 12 standardized data product pricing plans to cater to diverse business models. The "Open" represents the accessibility of the specification, enabling organizations to manage and monetize data effectively. Whether providing data for free or as a commercial offering, ODPS ensures flexible and scalable solutions to meet varied pricing strategies.

Standardized Data Quality, SLA, and Data Contracts support: This feature automates the processes of monitoring, compliance, and governance. It ensures that agreements on data structure, quality, and governance are consistently enforced, streamlining collaboration between data producers and consumers.

Computational Approach: Embracing an Everything-as-Code philosophy, ODPS facilitates automated workflows and programmable data management for Data Quality, SLA and Pricing Plans. This model enhances scalability while optimizing operational efficiency. Support for all major Platforms such as Stripe, Checkout, Paypal, SodaCL, MonteCarlo, DQOps, and Prometheus.

Hosted by the Linux Foundation since May 14th, 2024, ODPS has been a transformative initiative aimed at reshaping how organizations manage and monetize data. Its core mission is to provide a vendor-neutral, fully computational, open-source, machine-readable YAML based data product metadata model.

12 Highlighted Features of Open Data Product Specification

Vendor-neutral
Machine-readable
Related use cases
Data Contracts support (DCS and ODCS)

Extendable
Localization (Multilingual support)
Data Quality (SocaCL, MonteCarlo, DQOps)
Computational (Everything as Code)

Licensing attributes
Data Access
Related Products
Pricing Plans (Stripe, Checkout, Paypal)

These features highlight the versatility, accessibility, and comprehensive capabilities of ODPS in managing and sharing data products.

Get familiar with the latest ODPS production version

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Testimonials

Don't just take our word for it—hear from the many organizations and professionals who have transformed their data strategies with the Open Data Product Specification (ODPS). Our users consistently highlight the value of ODPS in breaking down data silos, boosting collaboration, enhancing data monetization, and driving business growth.

Here's what some of our satisfied users have to say about their experience with ODPS:

"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

Versions and resources

The Open Data Product Specification (ODPS) provides a structured approach to creating, managing, and utilizing data products. Below is a summary of the various versions of ODPS, highlighting their release dates, descriptions, and links to specifications, schemas, and source files.

The latest production version, ODPS 3.1, released on November 27th, 2024, brings enhanced data product capabilities, improved computational model features, and expanded support for data contracts and quality frameworks.

Open Data Product Specification 3.1

Release date : 27th Nov 2024
Description: Production Version 3.1
Specification: See the full specification.
YAML Schema: YAML Schema.
Source: See the source files.
Issues: See the issues.

Open Data Product Specification - Development version

Release date : Continuous preview to future releases
Description: Development version (dev) is for the development which aims for the next released version. Add your needs to this version, unless it's a bug in previous versions. In those cases, use issue lists of the version.
Specification: See the full specification, development version.
Source: See the source files.
Issues: See the issues.

Open Data Product Specification 3.0

Release date : 22nd May 2024
Description: Production Version 3.0
Specification: See the full specification.
YAML Schema: YAML Schema.
Source: See the source files.
Issues: See the issues.

Open Data Product Specification 2.1

Release date : 22nd Aug 2023
Description: Production Version 2.1
Specification: See the full specification.
Data Product Examples and JSON Schema: Some examples created with ODPS and JSON Schema.
Source: See the source files.
Issues: See the issues.

Open Data Product Specification 2.0

Release date : 1st April 2023
Description: Production Version 2.0
Specification: See the full specification.
Source: See the source files.
Issues: See the issues.

Open Data Product Specification 1.0

Release date : 1st Feb 2022
Description: This is the MVP production version.
Specification: See the full specification.
Source: See the source files.
Issues: See the issues.

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Use cases

The following use cases can be addressed by various vendor-specific and isolated solutions. While these isolated solutions have their merits, the Open Data Product Specification (ODPS) is a comprehensive, full-stack data product model that provides solutions for all these use cases in a single, unified package.

Use Cases for Open Data Product Specification (ODPS)

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.

Development

At Open Data Product Specification (ODPS), our development is driven by a governance model that aligns with the Linux Foundation's principles of openness and collaboration. As an open-source initiative, ODPS is built on transparency, inclusivity, and community-driven innovation.

Technical Steering Committee (TSC) is open to all—inviting developers, data experts, and organizations worldwide to contribute, share insights, and shape the future of data management. Whether you're a seasoned professional or just starting your journey, your voice and expertise are valued here.

Be part of a growing community dedicated to creating the next generation of data product standards. Join ODPS Technical Steering Committee by sending an intro message who you are and why you are interested to join and help drive the future of data products forward!