What is a Data Product and Why Does Your AI Strategy Depend on It?

Your organization has spent years investing in technologies to collect, integrate, govern, and analyze data. So why does it feel like the people and systems that need it most can’t trust it?

With AI now moving from experimentation to production at many organizations, solving that issue is becoming even more urgent. AI models, copilots, and agents depend on data.

If that data is incomplete, inconsistent, poorly documented, or difficult to access, AI systems can produce inaccurate answers, unreliable recommendations, and costly mistakes. That’s why the conversation around data products has gone from “nice to have” to “we need to figure this out now.”

According to recent research from BARC and Actian, data product adoption increased from 48% in 2024 to 69% in 2026. That’s not a trend. It’s a signal that’s hard to ignore.

Qu'est-ce qu'un produit de données ?

A data product is a curated, governed, and reusable data asset. It’s designed to deliver value to a specific group of consumers, whether that’s a business analyst, an application, or an AI agent pulling answers every day at 2 AM.

“Data products are assets that help organizations take control of their data and generate business value,” according to Deloitte.  “They are shareable and can help unlock the potential of data in a way that benefits both internal and external customers.”

Unlike a traditional dataset, a data product has:

• A clearly defined purpose.
• Documented business meaning.
• Known ownership.
• Quality standards.
• Service expectations.
• Governance controls.
• Ongoing lifecycle management.

Here’s a way to think about it: raw data is like ingredients. You can technically make something from it, but you have to figure out what goes with what, what’s expired, and what the recipe even is.

A data product is like the prepared meal. It’s ready to consume: packaged with the context, quality, and governance needed to actually make it useful. So instead of handing your AI systems a pile of ingredients and hoping for the best, you’re giving them a trusted, documented, and ready-to-use asset.

A real example is a Customer 360 data product that combines customer records from CRM, billing, support, and marketing systems into a single trusted view of each customer. Business users can then analyze it, applications can consume it, and AI systems can use it without having to reconcile conflicting records from multiple sources.

Why AI Changes the Data Conversation

Human analysts could often spot questionable data. They’d notice when a number looked off, dig into it, and flag the issue before it became a business decision. AI systems don’t work that way.

They generate content, answer questions, make recommendations, and increasingly take action based on the data they receive, even when that data is not fit for purpose.

That’s the uncomfortable truth behind why 60% of organizations in the BARC and Actian research cited “trustworthy data for AI use cases” as a primary driver for implementing data products. Ensuring trustworthy data is a way to implement damage control before something goes sideways.

The good news? Enterprises have gotten this concept right and are seeing real results. GEMA, for example, deployed the Actian Data Intelligence Platform and built more than 400 certified data products, with 11 AI models running in production, achieving 140% ROI and over €1M per year in cost savings.

Why ‘We Have Data’ isn’t the Same as ‘We Have Data Products’

Most organizations often have thousands of tables, reports, dashboards, and datasets. That doesn’t necessarily mean they have data products, and the gap matters.

Traditional data assets often suffer from common challenges:

• Unclear ownership.
• Inconsistent definitions.
• Unknown lineage.
• Duplicate versions.
• Limited documentation.
• Manual governance.

Data products flip the question. Instead of asking, “How do we store this data?” you start asking, “How will consumers use this data, and what do they need in order to trust it?”

The shift alone changes how data teams operate. At Groupe BPCE, the approach led to over 80,000 business terms defined and more than 1,000 unique data explorers a week, turning a traditional siloed data function into something the whole organization can access and act on.

What Makes a Good Data Product?

Not all data products are created equal. The ones that hold up under pressure, including AI workloads, tend to share a few characteristics:

• Business context. Consumers understand what the data represents and how it should be used.
• Ownership. Someone is accountable for the quality, usability, and ongoing evolution of the data product.
• Discoverability. Users can easily find and access the data product without relying on IT or manual requests.
• Trust. Quality standards, lineage, and governance controls help consumers understand whether the data is fit for the intended purpose.
• Reusability. The same data product can support multiple business use cases instead of requiring each team or user to recreate products using the same data.
• Observability. Teams can monitor quality, detect issues, and maintain confidence as data changes over time.

Enter Data Contracts: The Other Half of the Equation

As organizations scale data products across teams and business domains, another critical factor emerges: a clear agreement between producers and consumers on what they’re actually committing to.

That’s what a data contract is. It defines the structure, ownership, quality requirements, and operational expectations of a data product. When a downstream application, dashboard, or AI agent depends on that product, a contract is what gives them confidence that the data will still be what they expect next week.

The BARC and Actian research found that data contract adoption is closely following data product adoption, with many organizations viewing contracts as a critical component of trust, governance, and quality management. Together, data products and data contracts create the foundation for scalable AI.

The Numbers Make a Pretty Strong Argument

If you’re still weighing whether this is worth the investment, consider this: 85% of organizations with company-wide data products have three or more AI projects in production. That’s not a coincidence.

Data product strategies correlate strongly with agentic AI systems operating in production environments. This is the kind of autonomous AI that can drive business outcomes, not just answer questions in a chat box.

The organizations moving fastest on AI aren’t collecting more data. They’re making the data they already have understandable, reliable, and ready to use.

The era of AI does not tolerate messy data foundations. The good news is no one needs to start over. They just need to be deliberate about how data gets packaged, governed, and delivered.

See how to create trusted, contract-governed data products on a platform that makes it easy to discover, trust, and activate data.

À propos de l'auteur

À propos de Actian Corporation

Actian permet aux entreprises de gérer et de gouverner leurs données en toute confiance, quelle que soit leur ampleur. Les organisations font confiance aux solutions gestion des données d'intelligence des données d'Actian pour rationaliser leurs environnements de données complexes et accélérer la mise à disposition de données prêtes pour l'IA. Conçues pour être flexibles, les solutions Actian s'intègrent de manière transparente et fonctionnent de manière fiable dans les environnements sur site, dans le cloud et hybrides. Pour en savoir plus sur Actian, la division données et IA de HCL Software, rendez-vous sur actian.com.