What Is a Data Marketplace?

A data marketplace is a governed storefront where data producers publish reusable, high-quality data products that data consumers can easily find, trust, and use to drive business impact.

Unlike raw data catalogs that inventory thousands of assets, a marketplace curates and packages the right data into business-ready products. Each product comes with clear documentation, ownership, refresh schedules, and governance guardrails, ensuring that consumers can confidently access data that is accurate, compliant, and fit for purpose. This packaging shifts the enterprise data model from simply collecting and storing information to delivering solutions that directly answer business questions.

The result is a more scalable and outcome-driven approach to data management. Data marketplaces connect supply and demand: they enable data providers to publish products once and reuse them many times across functions, while giving analysts, business leaders, and even AI applications real-time access to the data they need. By embedding governance and privacy controls, marketplaces also ensure that self-service doesn’t mean uncontrolled chaos, but rather a consistent, compliant, and optimized way of turning raw data into measurable business results.

Organizations have no shortage of data. As of 2024, the global volume of data created, consumed, and stored is estimated at 149 zettabytes, and it is projected to rise to 394 zettabytes by 2028 (Forbes).  Despite this surge, most companies still struggle to convert data into business value. Forrester found that 60–73% of enterprise data goes unused for analytics.

Why? Traditional data programs tend to stall before value is realized. Enterprises may appoint a Chief Data Officer, invest in literacy programs, or attempt to collect requirements directly from business users. Yet too often, these approaches fail to bridge the gap between data availability and data outcomes.

This is where the concept of a data marketplace comes in—a governed storefront for data products that makes data discovery intuitive, data use safe, and business value repeatable.

Key takeaways

• A data marketplace is a governed storefront of reusable, business-purpose data products that make it easy for organizations to find, trust, and use data.

• Traditional approaches to improving data value often fail; marketplaces succeed by treating data as a product rather than raw inventory.

• Internal marketplaces streamline access, embed governance, and improve collaboration across the enterprise.

• With marketplaces, organizations can achieve 90% faster use-case implementation and ~30% lower total cost of ownership (TCO) while improving trust and compliance.

• In 2026, data marketplaces are essential for AI readiness, serving as the knowledge layer that fuels copilots, agents, and machine learning applications.

What are data products?

Data products are the foundation of any marketplace. They are curated, reusable data assets designed to deliver value for a defined business use case.

The essential trait of data products

While the specific traits of a data product will vary depending on the organization, industry, and use case—whether in healthcare, e-commerce, or financial services—they all share one unifying characteristic: data products are designed to solve a specific business problem and address specific business questions.

This problem-first orientation distinguishes data products from raw datasets. For example, an e-commerce company might build a data product to analyze abandoned shopping carts, while a healthcare provider may develop one to track patient readmission rates.

In the context of modern data architectures like data mesh, data products are treated as decentralized assets owned by different teams, each responsible for the quality, accessibility, and governance of their respective data. This approach not only improves the scalability of data-driven operations but also fosters a more agile and self-service data culture within organizations.

Characteristics of a data product

• Discoverable: Listed in a searchable, governed marketplace.

• Trustworthy: Documented with metadata, lineage, validation, and stewardship.

• Interoperable: Integrated via APIs into apps, data platforms, and analytics functions.

• Self-describing: Includes context about creation, purpose, and limitations.

• Monetizable: In external data exchanges, products can be packaged and sold, enabling new revenue streams.

By framing data as a product, organizations move from activity to outcomes. Data ceases to be an abstract asset and becomes a practical, governed tool for decision-making, AI, and even monetization of third-party data.

Why traditional data program approaches fail (and what to do instead)

Many enterprises attempt to unlock the value of data through familiar strategies. While well-intentioned, these approaches often stop short of creating sustainable, organization-wide impact.

Failure point: CDOs focused on defensive priorities

Appointing a CDO signals commitment, but too often the role skews defensive—focused on cleaning pipelines, securing data privacy, and modernizing warehouses. While critical, these tasks rarely optimize business outcomes quickly enough. With more than half of CDOs serving less than three years, and roughly a quarter lasting less than two years per MIT Sloan Management Review, ROI often fails to materialize when expectations are set for longer-term transformation.

Caroline Carruthers, co-author of The CDO’s Playbook, emphasizes that CDOs should build trust by focusing on people first:

“Don’t go to stakeholders and talk to them about data. Take them for coffee and cake. Ask what keeps them up at night. Start small—solve a cross-organizational problem that impacts everyone, and you’ll earn credibility to drive transformation.”

Actionable tip: Empower CDOs to prove early wins by packaging the right data into products that address urgent business needs.

Failure point: disconnected literacy programs

Training initiatives often remain abstract, leaving employees unable to access or apply data in their everyday work. Knowledge without utility erodes quickly.

Data literacy expert Jennifer Belissent argues that literacy must be contextual:

“The HR team doesn’t need the same training as supply chain managers. Tailor literacy to the job and make it practical so data can be used day-to-day.”

Actionable tip: Build literacy into the marketplace. Make products intuitive, documented, and supported with contextual guides and applications.

Failure point: incomplete requirement gathering

Polling business users for needs leads to reactive development. Users struggle to articulate unspoken needs, resulting in misaligned products.

Jeff Cruz, data product manager at the NBA, has found that working across teams means helping people extract value they didn’t know how to ask for. A product model—with owners, APIs, and validation rules—ensures alignment and scalability.

Actionable tip: Use a data product operating model. Define owners, refresh cadences, APIs for access, and validation rules. This ensures each product is purposeful, scalable, and aligned with business outcomes.

Internal data marketplace: Key features and benefits

An internal marketplace does more than centralize access—it creates a scalable operating model where data products can be discovered, trusted, and consumed across the enterprise. The marketplace functions as a governed storefront where data teams publish reusable, high-quality products that others can confidently consume.

Centralized data access

A single marketplace connects diverse data platforms—such as Snowflake, Databricks, and other clouds—into one entry point. Business users can access data without toggling across silos, accelerating procurement and reducing friction.

Data as a product

Every dataset is packaged as a product with ownership, documentation, validation, and SLAs. Producers own data and treat it as a deliverable, while consumers know they are accessing the right data.

This model also supports AI: copilots and agents can rely on high-quality, contextualized products to reason effectively and generate trustworthy outputs.

Self-service capabilities

Business teams independently search, filter by types of data, and even use chat interfaces or APIs to consume products. This reduces dependence on IT while giving users confidence that the data is accurate and governed.

Enhanced data governance

Access controls, lineage, and embedded policies ensure responsible use. Governance becomes an enabler, not a bottleneck, making data privacy and compliance seamless.

Improved data quality

Centralization allows for standardization. Products undergo validation and are continuously refreshed, enabling real-time analytics and reliable functions for decision-making.

Increased collaboration

Departments across e-commerce, supply chain, and healthcare can reuse the same trusted data products, driving consistent KPIs and enabling cross-functional innovation.

Cost efficiency

By reducing redundant pipelines and avoiding duplication, enterprises see up to 30% TCO reduction. Products scale once, rather than being rebuilt repeatedly.

The marketplace model transforms the way organizations manage and own data: not as isolated silos, but as reusable products that scale across business lines.

Governance guardrails for safe data sharing

A marketplace only succeeds if it balances agility with governance. In global operations, marketplaces must respect data sovereignty while adhering to regulations (GDPR, HIPAA, CCPA). Guardrails ensure that data sharing is safe, compliant, and trusted.

• Apply governance policies at scale using metadata: Policies are enforced automatically across products, ensuring consistency.

• Track usage with audit logs and lineage: Complete visibility into who accessed data, when, and how.

• Prevent data drift with versioning and deprecation controls: Outdated or invalidated products are flagged, ensuring real-time accuracy for apps and analytics.

These governance mechanisms make it possible to safely scale marketplaces across industries with stringent compliance requirements, from healthcare to financial services.

AI governance best practices for data marketplaces

As portals to enterprise data designed to be foundational for AI models, data marketplaces must support AI governance by surfacing the granular metadata details that inform responsible AI development. Copilots, agents, and other AI applications depend on structured, transparent, and unbiased data products. Without robust governance, AI initiatives risk bias, inaccuracy, or regulatory failures.

Transparency: Detailed metadata—covering origin, transformations, lineage, and usage policies—ensures AI developers understand how data was created and can trace its journey end-to-end.

Bias mitigation: Curated, representative products reduce risks of skewed outputs in sensitive contexts such as healthcare, financial services, or retail targeting. By standardizing validation and stewardship, marketplaces help identify and correct imbalances before they impact models.

Automated compliance: Embedded governance rules enforce GDPR, CCPA, HIPAA, and other regulatory frameworks at scale, protecting organizations from compliance gaps while enabling rapid AI experimentation.

By embedding AI governance into the marketplace itself, enterprises can accelerate innovation while ensuring safety, fairness, and trustworthiness. Marketplaces thus become not only a distribution layer for data but also a foundation for building ethical, compliant, and high-performing AI systems.

Use your data catalog to enable self-serve data products

A catalog is the foundation of modern data management, but catalogs alone don’t deliver business outcomes. A marketplace builds on the catalog by shifting from inventory to utility.

• From cataloging to productizing: Catalogs document what exists; marketplaces create curated, business-ready packages.

• From searching to solving: Instead of spending weeks finding tables, business teams get immediate access to pre-built, validated solutions.

• From owning data to reusing products: Teams no longer protect data in silos but publish products that scale across functions.

With Alation, data teams can even build products using natural-language chat, assemble datasets with APIs, and publish them with governance hooks. Consumers, in turn, can chat with data products, use them in apps, or integrate them into AI workflows.

The outcome: organizations accelerate AI readiness, enable business self-service, and optimize the total cost of data ownership.

Value and outcomes of a data marketplace

The marketplace model translates directly into measurable outcomes.

• Speed: Up to 90% faster implementation of new use cases by reusing products.

• Cost: ~30% TCO reduction by focusing on valuable products instead of managing millions of raw assets.

• Innovation: Plug-and-play data exchange powers apps, dashboards, AI copilots, and real-time functions.

• Business impact: Faster decision-making, stronger procurement strategies, higher e-commerce conversion rates, reduced healthcare costs.

These outcomes prove that marketplaces are not just operational improvements—they are strategic levers for competitiveness.

The bottom line? Successful data leaders are moving from searching to solving—from cataloging to productizing—to drive business outcomes, not activity.”

Customer proof: Kenvue’s data product marketplace in action

Kenvue, maker of consumer brands like Neutrogena, Tylenol, and Aveeno, evolved from using technical metadata catalogs to building a business-focused data products marketplace. As Nathan Caplan, Lead Analyst for Data Catalog at Kenvue, explained:

“It wasn't until I transitioned over to Johnson and Johnson and using Alation that I found that we were able to finally document the why of data… well beyond just documenting your columns and your tables and your schemas, we were finally able to really explain why the data product exists and what additional information you might be wanting to find out from it.”

The shift helped bridge the divide between technical and business users:

“Tech speak does not equal business speak. You are going to run into issues consistently if your tech community is driving documentation … without input from business. The two have to go hand in hand together.”

To make this real, Kenvue introduced solution pages that centralize documentation, policies, and links to systems like Confluence and Jira. They defined business and technical product owners, documented governance rules like PII masking, and emphasized usability—aiming for “three, maybe four clicks” to find what users need. Starting with just four core products, Kenvue has since grown to more than 70.

Kenvue’s journey shows that marketplaces succeed when business and technical perspectives converge. By focusing on usability, ownership, and governance, organizations can scale from a handful of critical data products to dozens—delivering trust, adoption, and measurable business value.

Final thoughts

By 2026, data marketplaces are no longer experimental—they are essential. They are the connective layer between data providers and consumers, governance and agility, and today’s analytics and tomorrow’s AI. Enterprises that adopt marketplaces are not just managing data; they are using it to optimize outcomes, monetize external data, and scale responsibly. Whether in healthcare, e-commerce, or financial services, the marketplace model is the foundation for turning the right data into the right decisions—fast, safe, and at scale.

Still, not all marketplaces are equal. DIY builds are brittle, expensive, and non-scalable. Point solutions offer a storefront without a catalog backbone. Catalog-plus-marketplace peers often provide only partial functionality with weak consumption experiences. And while hyperscalers like Snowflake or Databricks deliver value inside their ecosystems, they risk creating lock-in and limiting multi-cloud flexibility.

Alation stands apart with a metadata-first, open design that avoids lock-in and connects seamlessly across platforms. By combining builder, chat, and governance, Alation delivers both supply-side ease and demand-side usability. The conclusion is clear: enterprises need a scalable, governed approach that works across all ecosystems—not siloed solutions.

Curious to learn how a data catalog can help you develop an internal data marketplace? Book a demo with us to learn more.