BARC Spotlight: A Data Marketplace Is What Your Agents Need

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A Data Marketplace Is<br>What Your Agents Need

An independent BARC report on why agentic AI raises the stakes for trustworthy, governed, and discoverable enterprise data — and how a data marketplace built on data products and data contracts answers it.

This BARC Spotlight report was written by<br>Florian Bigelmaier,<br>Analyst for Data & Analytics at BARC, the leading independent analyst firm for data & analytics, AI, and corporate performance management.<br>Entropy Data sponsored this report and contributed input and feedback, but the analysis and opinions are BARC's own.<br>The full text is reproduced below, unchanged.

Published June 2026.

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73%<br>say relevant data is not easy to find

75%<br>confirm their data and analyses lack reliability and interpretability

49%<br>name building a data product organization an active challenge

Source: BARC Survey &ldquo;Data Mesh and Data Fabric&rdquo; 2024 (n=197 / n=121).

Trust, Access, and Accountability: How Data Marketplaces Enable AI Agents

Making data trustworthy and discoverable has been a persistent, unsolved challenge in enterprise data analytics over years. According to the BARC study Data Mesh and Data Fabric – From Theory to Application1, 73% of respondents report that relevant data is not easy to find, and 75% confirm that their data and analyses lack reliability and interpretability.

These figures predate the agentic AI wave. As AI agents begin automating critical business processes, the stakes rise sharply: the infrastructure gaps that slow data consumers2 down can cause agents to act on incomplete or untrustworthy data. A data marketplace is part of the answer: it makes data discoverable, trustworthy, and accessible in a way that works for both humans and AI agents.

Humans and Agents Require the Same Four Conditions From Enterprise Data

Before we come to the proposed solution, let us dive one level deeper into the diagnosis. BARC's research consistently points to four foundational data challenges facing organizations today:

Data consumers cannot find the right data (discoverability)

They do not understand what data means because it is poorly explained (context and semantics)

They cannot access data quickly enough due to complex and manual data access workflows (data access governance)

They cannot assess whether the data they are looking into is trustworthy (quality signals)

We argue that AI agents face the same four barriers, just with less tolerance for failure. Let me give you two examples:

Situation<br>Human Behavior<br>Agent Behavior

Data Quality does not fit purpose<br>Can pause, investigate, or ask a colleague<br>May not recognize the quality gap at all. Outcomes range from proactively seeking better data to aborting the task, or silently proceeding with unsuitable data and hallucinating results.

Lack of access rights for the intended purpose<br>May request, wait, ask the owner via a phone call<br>Either stalls or, if metadata is missing or unclear, draws on a data asset it has physical access to – but does not have the right to use it for the new, deviating purpose.

Unfortunately, these are not edge cases, but rather the predictable consequences of deploying agents on top of data infrastructure that was not designed with agentic AI in mind.

The implication is straightforward : data must be discoverable, contextualized, and accessible in a way that works for AI agents. Then it will work for humans, too, because they are more adaptable.

Arbitrary Data Assets Will Not Do

Addressing these four barriers is not only a matter of the data platform or user interface design. It starts with how data is packaged for consumption. Let us use an analogy: A bulk active pharmaceutical ingredient, such as ASA, is not a product. Aspirin3 is, because it is the dosed, labeled version of it, packaged with contraindications, from a responsible manufacturer, and coming with a usage insert. The analogy is simple: if you want to offer something on a marketplace, make a product out of it that is easy to evaluate, consume, and trust.

Figure 1: One need, two consumers © BARC 2026

Data Product Definition – Deep Dive for the Curious

A data product is a shoppable, reusable, active, and standardized data asset designed to deliver measurable value by applying product thinking principles. It includes one or more artifacts enriched with metadata such as...