Data Product

What is a data product? 

A data product is a carefully designed data asset (or collection of assets) that helps organizations make better business decisions. Unlike raw data sitting in a data warehouse or data lakes, data products are built to be discoverable, reusable, and governed. They give data consumers—whether humans, apps, or AI systems—trusted access to the information they need.

Think of data products as the bridge between your data platform and the people who need insights. While traditional data assets often live in silos, data products are designed for multiple business domains and can serve different stakeholders across your organization to drive business value.

Most organizations today understand that data drives success. According to research by S&P Global Market Intelligence, 96% of organizations highlight the importance of data utilization in their decision-making processes. Companies that actually use data for strategic decisions are three times more likely to see major improvements compared to their competitors.

But here's the challenge: getting the right data to the right people at the right time. That's where treating data as a product becomes game-changing. Gartner research shows that 50% of organizations have already deployed data products, with another 29% exploring their potential.

Data products vs. data as a product

While often used together, these terms mean different things:

• Data products are the actual deliverables—reports, datasets, APIs, and apps that end users interact with

• Data as a product is the methodology—the overall approach and product thinking that guides how you build and manage these assets at scale

Think of it this way: data products are like individual cars, while "data as a product" is the manufacturing process that builds cars efficiently in a factory. While often used interchangeably, "data products" and "data as a product" represent distinct concepts.

Key attributes of data products

Successful data products share important characteristics that ensure they deliver business value:

• Value-first design: Every data product should solve a specific business problem. Whether it's helping data scientists build better models or giving executives real-time insights, the value should be clear and measurable.

• Discoverable: Data products need good metadata and search features. If your team can't find them in your data catalog, they won't get used.

• Linked to a data contract: Each product should have a data contract that defines expectations, ownership, and the schema. This builds trust between data producers and consumers.

• Well-explained: Good documentation includes business definitions, usage instructions, and examples. This helps users understand how to access and use the data correctly.

• Globally unique: Each data product needs a unique ID to avoid confusion with other assets in your data architecture.

• Trustworthy: Users need confidence in data quality and accuracy. This comes from good governance, validation, and data lineage tracking.

• Accessible: Products must balance accessibility with security through access controls and role-based permissions.

• Modular and reusable: Well-built data products can be reused across different use cases, reducing duplication and increasing efficiency.

• Composable and interoperable: Data products should integrate easily with existing tools, data pipelines, and workflows.

• Secure: Security and compliance should be embedded into the data product lifecycle, ensuring that data is protected against unauthorized access and that regulatory requirements are met.

By adhering to these attributes, organizations can create scalable, high-impact data products that drive business value, enhance data governance, and enable AI and analytics at scale.

Benefits of data products

Organizations that adopt data products see several key advantages:

Accelerated data delivery: Streamlined access to trusted data reduces implementation time for new projects and analytics initiatives.

Lower costs: Reusable data products can reduce technology and development costs by up to 30% through standardization and less duplication.

Better governance: Built-in security and access considerations improve data governance and reduce compliance risks.

Business alignment: Data products provide a structured approach that aligns data efforts with business goals across different domains.

Improved AI performance: High-quality, well-governed data products lead to better machine learning models and AI applications.

Self-service analytics: Business users can access data independently without always relying on technical teams.

By implementing data products, organizations can ensure that data efforts are more efficient, governed, and aligned with business needs.

How data products work: From concept to consumer

Building effective data products requires a systematic approach:

Technical development: Data engineers and developers build the actual products, including APIs, interfaces, and integration with existing data platforms like Databricks or cloud data warehouses.

Governance integration: Products must work within your existing data governance framework, including access controls, data quality checks, and compliance requirements.

User experience: The best data products have intuitive interfaces that make it easy for different types of users—from SQL experts to business analysts—to get value.

Lifecycle management: Like any product, data products need ongoing maintenance, updates, and eventual retirement. 

Product management: Data product managers bridge business needs with technical capabilities. They understand both the business problems and available data sources.

What is a data product operating model?

Supporting data products and data product managers is a data product operating model, which can either be centralized to consolidate data expertise and delivery or decentralized to bring data expertise together with business knowledge at the point of need. 

A data product operating model aligns people, processes, and technology to create a familiar producer/consumer model that offers data products in a data product marketplace and enables organizations to start small and scale effectively. It focuses on collaboration between data product managers who understand the need and data engineers who can build the data products. 

Agile development methods have become more prevalent in data product operating models to improve efficiency, accelerate delivery, and streamline processes. And, as business applications and AI innovations need more and more data, data products increasingly rely on metadata to provide context that improves AI outcomes.

Ownership models for data products

Effective governance is crucial for data product success. Organizations typically choose from three models:

Centralized governance: A single team manages all data governance policies. This ensures consistency but can create bottlenecks.

Decentralized governance: Individual business units manage their own data governance. This offers flexibility but may lead to inconsistencies.

Federated governance: Combines central oversight with local autonomy. A central team sets standards while domain teams have flexibility in implementation.

The federated model often works best for data products because it balances control with the agility that business domains need.

The value of data governance to data products

Modern data products rely heavily on metadata to function effectively. Metadata serves several critical functions:

Discoverability: Rich metadata makes it easy to find relevant data products through search and browsing.

Trust and quality: Metadata tracks data lineage, quality metrics, and validation results, helping users understand trustworthiness.

Compliance: Automated metadata collection helps organizations meet regulatory requirements and audit needs.

Interoperability: Semantic metadata helps different systems and teams understand and use data products consistently.

Usage analytics: Metadata about usage patterns helps product managers understand value and areas for improvement.

Common pitfalls to avoid when implementing data products

As with any change in approach, implementing a data product operating model and shifting to a data-as-a-product mindset can cause some issues along the way. Common challenges when deploying data products include: 

• Building data products without a framework. Creating ad-hoc data products without a systematic data-as-a-product approach leads to inefficiency and poor scalability.

• Ignoring change management. Getting leaders involved, aligning with corporate goals, and generating excitement for data products is crucial to success.

• No discovery tools. Without a proper data products marketplace or catalog, even great data products won't get used.

• Repurposing legacy assets. Sometimes it's easier to build new, modern data products than to retrofit old data models and systems.

• Missing value measurement: If you can't measure and demonstrate ROI, it's hard to sustain investment in data products.

Data products are still a new concept for many organizations, requiring a focused effort and change management. However, starting with a data product operating model will help unlock the power of data products.

Measuring data product success: Metrics and outcomes

Evaluating the success of data products requires a multifaceted approach, focusing on adoption, business impact, return on investment (ROI), and quality.

Track these key metrics to ensure your data products deliver value:

Adoption metrics:

• Active users (daily/monthly)

• Session duration and frequency

• Self-service usage vs. support requests

Business impact:

• Revenue growth from data-driven decisions

• Cost reduction through automation

• Time-to-insight improvements

• Business problem resolution rates

Quality metrics:

• Data accuracy and completeness

• Real-time data freshness

• User satisfaction scores

• Error rates and support tickets

By tracking these metrics, organizations can not only validate the effectiveness of their data products but also identify areas for improvement.

Alation eases data product efforts

Business users need fast, trusted access to data to drive smarter decisions, AI applications, and business processes. Alation’s Data Products Marketplace enables organizations to publish, discover, and use high-quality, reusable data products—ensuring teams, systems, and AI agents have the reliable data they need.

Key capabilities include:

• A centralized marketplace for trusted data products – The Data Products Marketplace transforms data management into a systematic, repeatable framework. Business users can shop for ready-to-use, governed data products—eliminating the complexity of searching through raw datasets.

• Streamlined data product discovery & governance – Data producers can track usage, improve discoverability, and ensure compliance through built-in governance features, fostering trust and reducing risk.

• Scalable data-as-a-product approach – Alation helps organizations scale the data product operating model, accelerating delivery, minimizing duplication, and ensuring that data products support AI, analytics, and automation initiatives.

With Alation’s Data Products Marketplace, businesses no longer have to choose between speed and trust. By providing a governed, easy-to-navigate platform for data products, Alation enables organizations to unlock the full value of their data, accelerate decision-making, and drive AI success at scale.

Next steps: learn more about data products

Discover how data products can help organizations gain agility, move faster, and become more data-driven with the following resources: