From Datasets to Data Products: How to Build Business Value with Standardized Specifications

The data landscape is evolving—and not just incrementally. We are in the midst of a fundamental shift in how organizations think about data. For too long, we’ve treated datasets as the end product: raw collections of tables, CSVs, or queries to be cataloged. But in truth, these are just building blocks. The real opportunity lies in transforming them into purposeful, usable, and valuable data products.

As the lead maintainer of the Open Data Product Specification (ODPS), I’ve had the privilege of guiding this transformation across industries. In a recent webinar hosted by Alation, I shared how standardized specifications are enabling this shift—and why now is the time for organizations to operationalize data product thinking at scale.

What makes a data product different?

A dataset is inert. It exists, but it lacks intention. It doesn’t communicate why it exists, for whom, or how it should be used.

A data product, by contrast, is designed. It’s business-oriented. It solves a specific problem, serves a defined audience, and has a clear value proposition. This distinction isn’t just semantics—it’s strategic. Without that shift in mindset, organizations will never fully realize the value of their data.

Jake Magner, Senior Director of Product Management at Alation, put it well: “You’re usually starting with thousands—if not millions—of tables. How do you move from that cluttered state into something curated and consumable? That’s the power of data products.”

Why we built the Open Data Product Specification (ODPS)

The challenge wasn’t just conceptual—it was practical. If enterprises are to scale data products, they need a consistent way to define, manage, and publish them. Existing standards like DCAT are dataset-centric and lack the critical business context that makes a data product work.

So, we took a different approach. We didn’t begin with metadata tables. We began with the marketplace and the customer experience. ODPS was built from the outside in, with the consumer’s needs as our north star.

ODPS defines seven core elements essential to making data products usable, valuable, and safe to consume:

• Service quality

• SLA and data quality guarantees

• Pricing and licensing

• Provider identity

• Technical access details

This structure allows for:

• Machine readability (YAML-based, CI/CD friendly)

• Vendor neutrality (no vendor lock-in)

• Extensibility (adaptable to industry-specific needs)

• Lifecycle management (with built-in status and versioning)

By standardizing how we define and communicate data products, ODPS bridges the gap between business needs and technical execution. It creates a common language across roles, tools, and systems—making data products more accessible, trustworthy, and scalable.

Making data products enterprise-ready

For data products to be useful at scale, they must be complete; not just technically sound, but contextually rich and business-ready. That means:

• Clear business purpose: What problem does it solve? Who benefits?

• Documented quality standards: ODPS supports eight key dimensions like accuracy, completeness, and timeliness.

• Defined access patterns: Whether via SQL, APIs, or Model Context Protocols (MCP) for AI agents.

• Governance frameworks: Every product must specify licensing, usage constraints, and compliance information from the start.

Without these components, a data product is just another dataset in disguise. But when these elements are thoughtfully integrated, enterprises unlock the true potential of data as a product—enabling safe, repeatable, and impactful consumption at scale.

How enterprises are implementing ODPS

Adopting data products is not just a theoretical exercise. Many organizations are already operationalizing these ideas through modern platforms and marketplaces.

Jake demonstrated this in the webinar: Alation’s marketplace is intentionally distinct from the catalog. It doesn’t just show everything—it only surfaces curated, standards-compliant data products. They must be explicitly published. Someone must have thought: This is ready. This has value.

The technology supports streamlined discovery and usage in several ways:

• AI-powered discovery: Search by intent, not metadata.

• Automated product suggestions: AI agents propose products based on usage signals.

• Standardized publishing workflows: No shortcuts. Every product must meet predefined criteria to be visible.

By pairing a robust specification with purpose-built infrastructure, organizations can create a clear and scalable path from raw data to ready-to-use products. The result? A more efficient, governed, and valuable data ecosystem—one where users find what they need, trust what they consume, and extract real business impact.

Operationalizing data products

The journey to data products is not just conceptual—it’s operational. Many organizations begin with a technical mindset, cataloging data as an end in itself. But true transformation happens when we reframe that data not as a final output, but as a means to a business end. This shift—from data management to data product management—is central to unlocking business value.

David Chao, Chief Marketing Officer at Alation, captured this evolution during the webinar:

“There is a very tangible shift when organizations go on that journey from moving to only thinking about data in a catalog to thinking about the data in a catalog as the platform for data products… to start building these data products in consultation and in partnership with internal business partners in service and in action against specific data problems and business problems.”

As this visual journey illustrates, organizations move from simply cataloging datasets to progressively building data products with richer business context. Internal data products are the first step—defined by use cases, customer orientation, and agreements. From there, organizations expand their scope, introducing licensing and pricing strategies for external data products designed for partners and customers.

Finally, we reach the frontier: data products for AI. In this phase, AI agents become consumers. Because data products are structured, context-rich, and standardized, they are ideally suited for automated consumption in agentic workflows or LLM training pipelines.

This operational journey reflects the maturity of an enterprise’s data strategy. It’s no longer just about managing information—it’s about productizing it, scaling it, and delivering it with intentionality to both human and machine consumers.

Best practices for real impact

Drawing on my experience developing ODPS and Alation’s deep engagement with enterprise data teams, we’ve identified what separates successful data product transformations from those that stall. These best practices are grounded in real-world implementations and industry feedback:

• Lead with business outcomes. Don’t focus on technical metrics like “tables cataloged.” Focus on decisions improved, time saved, or revenue generated.

• Empower data product managers. These aren’t data engineers. They are cross-functional thinkers who bridge business and tech.

• Start with the problem, not the data. Solve a real issue. Then work backward to build the product.

• Governance must be embedded from day one. Especially for products shared externally. “If you’re crossing the company border,” I often say, “you need a license—even if the consumer doesn’t ask.”

The future: AI-first, product-oriented

One of the most exciting developments is the growing intersection between data products and AI agents. Because data products are so well-structured and purposeful, they’re ideal for consumption by AI systems. It’s a natural fit. AI agents don’t want raw data—they want something they can act on. And that’s exactly what a data product is.

We’re also seeing broader adoption of ODPS across public and private sectors—from smart cities to e-commerce, from government data exchanges to AI-first startups. The ecosystem is growing rapidly.

The path is clear: Organizations that embrace the product mindset—and implement with open, flexible standards—will be the ones to lead in this new data economy.

Let’s stop treating data like static inventory. Let’s start treating it like a product—built for users, measured by outcomes, and governed by design.

Learn more about Alation’s data product offering:

• Explore the Data Products Marketplace

• Read the press releases: Alation Agentic Platform, Data Products Marketplace

• Explore our professional services offering, the Data Products Playbook