Data Product vs. Data Contract: What’s the Difference?

Data contracts and data products are like inseparable cousins — always working together, always aligned, and always making sure things run smoothly. One ensures that data is structured, reliable, and consistent, while the other packages and delivers that data for valuable consumption. If data were a factory, data contracts would be the quality control process, ensuring that only properly formatted, high-quality materials enter production. Meanwhile, data products would be the final packaged goods, ready for consumers to use and trust.

In this article, we’ll unravel the key differences between data products and data contracts, why we need both, and how they work together to create reliable, scalable, and self-service data ecosystems. Whether you’re a data engineer, business leader, or analytics professional, understanding this relationship will help you unlock the full potential of your data architecture.

What Is a Data Contract?

A data contract is a formal agreement between (usually) one data producer and data consumers that defines expectations around data structure, quality, meaning, and availability. It ensures that data meets technical guarantees and aligns with business definitions, reducing inconsistencies across teams.

Key Characteristics of a Data Contract

Data contracts should contain:

1. Schema (or Structure) Definition: Specifies what data is included, its structure, and format.
2. Business Alignment: Links the physical implementation (tables, columns, types) to the business meaning (e.g., “customer” in a database aligns with the business definition of a customer).
3. Service Level Agreements (SLAs): Defines reliability, update frequency, availability, and more. More reading: SLA in Data.

Following these characteristics, a data contract is the source of truth for your metadata.

Data contracts are represented by a 90º-rotated equilateral triangle, symbolizing its three foundational components.

Why Do We Need Data Contracts?

Data contracts prevent misalignment between technical teams and business stakeholders by explicitly defining what the data represents and how it should behave. They reduce schema drift, ensure compliance, and foster trust in data pipelines.

Example: Imagine an analytics team relying on a database’s “customer_status” field. A data contract guarantees that the field exists and defines what each status means from a business perspective, ensuring consistency across teams.

Data contracts are defined using the Open Data Contract Standard (ODCS), a mature standard that ensures structured agreements between data producers and consumers.

What Is a Data Product?

A data product is a reusable, active, and standardized data asset designed to deliver measurable value by applying product thinking to data management. Unlike a data contract, which is a formal agreement, a data product is an actual deliverable — something users interact with and derive value from—more reading: Defining Data Products: A Community Effort.

Key Characteristics of a Data Product

Here are some characteristics of a data product:

• Composed of data artifacts — Includes datasets, models, pipelines, and probably more. Artifacts can have multiple versions.
• Output ports — A data product exposes its data through well-defined output ports, ensuring downstream users or systems can easily consume it.
• One (output) data contract per output port — As a data product may expose multiple output ports, it will require one data contract per output port.
• Input data contracts — They are recommended to ensure that what is coming in the data product can be trusted.
• Software Bill of Material (SBOM) — Designed to document precisely how the transformation is performed.

Its shape? A horizontal hexagon represents the interconnected nature of hexagonal architecture.

Why Do We Need Data Products?

Data products transform raw data into structured, reusable, and valuable assets, ensuring discoverability, trust, and usability across teams and systems. They enable self-service data access, reduce duplication and silos, improve data quality and governance, and support scalable automation for AI and analytics. By aligning data with business value and modern architectures like Data Mesh, data products empower organizations to leverage data efficiently, eliminate bottlenecks, and drive innovation at scale.

Example: A Transaction data product can have a dataset containing transactions as they happen, with a very short latency of 4ms, defined by the data contract. The data product can also have curated transactions after the consumer’s withdrawal delay; the latency could be 2 days. Finally, a third dataset could be the transactions aggregated by countries. In this scenario, our data product would have three datasets.

Data products are moving toward a standard definition under Bitol‘s ODPS (Open Data Product Standard), which aims to standardize metadata, ownership, lifecycle management, and output ports. Other standards exist, but ODPS is designed to integrate nicely with ODCS.

Key Differences Between Data Products and Data Contracts

Let’s have a quick view of the essential differences between the two elements.

How Data Products and Data Contracts Work Together

Data contracts guarantee that your data product is behaving in the expected way.

The output data contract, aligned to an output port, defines precisely the expectations of the product. The consumer can access data in a trusted way, as the contract guarantees.

The role of the input data contract is to prevent any inner failing of the pipeline, just as raw materials are tested before they enter a manufacturing process. In the case where the data source is another data product, the ascending data product’s output contract will be the same as the input data contract.

Why Do We Need Two Standards?

At first glance, you might wonder: Why do we need both Open Data Contract Standard (ODCS) and Open Data Product Standard (ODPS)? Can’t a single standard cover both? The reality is that data contracts and data products serve distinct but complementary roles, much like HTML and CSS in web development.

• HTML defines the structure of a webpage — what elements exist, how content is organized, and what data should be displayed.
• CSS controls the presentation — how the content looks, how it adapts to different screens, and how it enhances the user experience.

Similarly, ODCS and ODPS play complementary roles in the data ecosystem:

• ODCS ensures that data is well-defined, structured, and reliable at the source. It formalizes the contract between data producers and consumers, setting expectations for schema, data meaning, and quality guarantees.
• ODPS standardizes how data is packaged, governed, and made available for consumption. It defines data products’ metadata, lifecycle, and discoverability, ensuring organizations can manage and scale their data assets effectively.

Just as HTML without CSS would lead to structurally correct but ugly and unusable websites, data products without contracts would be less findable and reliable, causing broken pipelines, inconsistent definitions, and poor data trust. Likewise, data contracts without products would be isolated agreements rather than valuable, consumable assets.

Having two distinct but interconnected standards allows organizations to build data ecosystems that are both reliable and scalable — ensuring that data is well-defined, well-managed, and accessible for real-world use cases.

Together, these two standards create the foundation for modern, well-governed, and business-aligned data management.

Before You Run

Before you run away implementing data contracts and data products at your organization, remember:

• Data contracts ensure reliability, while data products enable usability.
• Data contracts act as quality control for data products.
• Data products have output ports, and each requires a data contract.
• Data products and data contracts are complementary but distinct.
• We need two standards: ODCS for data contracts and ODPS for data products.
• Organizations that implement both gain scalable, reliable, governable, and self-service data ecosystems.

I hope this was useful to you; please leave a comment & clap. This is the currency of fame those days… I should probably define a contract around this.

Common Misconceptions & FAQs

Is a data contract a data product?
No. A data contract is an agreement; a data product is an actual asset.

Does every data product require a data contract?
Yes. Data contracts ensure trust and stability in data products.

Can a data contract be part of a data product?
Yes! Well-governed data products include data contracts to guarantee data consistency and quality.

How does this fit into Data Mesh?
In a Data Mesh architecture, data products are the foundation of domain-driven data management. Data contracts ensure trustworthy, reliable data flows between producers and consumers.

Where can I learn more about ODCS and ODPS?
Open Data Contract Standard — Learn more about ODCS.
Open Data Product Standard — Explore ODPS.
Bitol — Discover the Linux Foundation project behind the open standards.

More Resources

• Data Contract vs. Data Product Specifications: in his article, Andrea Gioia combines the usage of ODCS with another data product specification, DPDS, showing interoperability and the importance of data contracts.
• Implementing Data Mesh (O’Reilly, 2024): in our book, Eric Broda and I focus on the Data Mesh concept at large, relying on the ideas described here.
• Bitol is the Linux Foundation project focusing on standardizing ODCS and ODPS.
• In Data Products for all ages, you will find more of those little creatures and do good as half of the profits are given to two charities, Girls Who Code and Black Girls Code.

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Data Product vs. Data Contract: What’s the Difference? was originally published in Data Mesh Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.