What is the difference between a static and a dynamic context layer?

A traditional, static context layer describes the data: its definitions, schemas, and origins. It remains unchanged until someone updates it. A dynamic context layer (such as Huwise’s) continuously captures usage signals from a data marketplace—covering how data is actually searched for, viewed, and used. Built at the heart of the data marketplace, the dynamic context layer is enriched with every interaction and becomes increasingly relevant over time.

Why aren’t BI tools enough to capture complete context?

BI tools capture part of the demand for data by tracking analytics and reporting activity. But they do so only across their own data and for an expert audience. They cannot see the organization’s entire data estate, nor the needs of business teams that do not use BI tools.

Why aren’t data catalog tools enough to capture the complete context?

Data catalogs document the data being shared through metadata, and they do this well. However, as they were originally designed for data experts they are rarely used by business teams and do not monitor actual data consumption. As a result, they generally miss the usage dimension of context.

What components make up Huwise’s living context layer?

It brings together the two dimensions required to provide complete context. On the supply side, it draws on knowledge about the data: the assets, schemas, definitions, and lineage of each data product. On the demand side, it captures usage signals: searches, views, exports, access requests, drop-off points, and user journeys specific to each user profile. Governance rules applied at the point of use are also recorded.

What is the tangible impact of Huwise’s context layer for users?

Business teams and AI agents can find the data products they need faster, when they need them. Discovery is guided by usage: the most useful data is shown first, based on each user’s profile. AI agents, meanwhile, no longer simply know what the data is. They understand how the organization actually uses it, making their responses more reliable.

What are the benefits of Huwise’s living context layer?

Its main benefit is to create a self-reinforcing virtuous cycle. The more usage signals the marketplace captures, the more relevant its context becomes. A more relevant marketplace is adopted more widely across the organization, and that adoption in turn generates more signals. And because governance is embedded in the context, this large-scale adoption happens securely: everyone, whether human or AI agent, sees only what their permissions allow.

Is Huwise’s context layer interoperable with other tools?

Yes. It is built on industry standards, which we continually monitor as they are evolving rapidly. This ensures that context information remains usable beyond our platform. This guides our collaboration with Atlan, which sees Huwise as an essential partner for capturing usage intelligence .

Living context layer

Our data product marketplace solution captures every usage interaction to continuously build dynamic context, improving your AI agent performance and providing your business teams with the data products that best meet their needs.

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Everyone talks about context, but no one enriches it with [ usage signals ]

The richest context layer comes from the people who actually use the data. A complete context layer brings together two dimensions: the data that is shared and documented, and the way that data is searched for and used. As they are designed for data teams, traditional data management tools often just cover the first dimension. Some document metadata without engaging business users or observing actual consumption; others are limited to a narrow scope or a small group of experts. As a result, their context layer remains limited and never incorporates insights based on observing how data products are actually used across the organization.

Only a data marketplace transforms context into value

A showcase and single source of truth for the organization’s data, the data marketplace is where business teams and AI agents come to find and consume data. It is the meeting point between the two sides of the data and use context equation: supply and demand. Every data product is available on the marketplace with its meaning, origin, and rules, while every search, question, or access request highlights users’ tangible data needs. Powered by this information, context becomes dynamic and increasingly relevant across the data product lifecycle.

A dynamic context layer to power relevant and reliable AI agents

Every search, export, access request, or correction enriches the context, including unsuccessful searches. Your data marketplace therefore does more than simply know your data: it understands how your organization actually uses it. The result is a virtuous cycle: the more usage grows, the more context is refined, and the more reliable and effective your AI agents become.

AI agents designed to accelerate the [ success ] of your data products

The Huwise platform includes integrated AI agents that turn usage signals into tangible actions, enabling you to create the data products that matter faster, and to focus your time where it will have the greatest impact.

Improve existing data products

The Marketplace Curator agent continuously analyzes usage patterns (clicks, consumption, drop-offs, etc.) and recommends tangible actions to drive greater data product adoption.

Identify the next high-impact data products

The Opportunity Spotter agent identifies gaps between current data assets and user needs, then recommends which new data products to create.

Accelerate data product creation

The Data Product Designer agent supports you end-to-end, from identifying and formalizing the need for a product through to its design and packaging.

The [ Huwise ] difference

Unique expertise

5 years of experience and the lessons learned across more than 3,000 data marketplaces have built a platform designed to measure and analyze usage, and turn it into actionable insights to drive adoption.

Usage-driven discovery

searches, views, and drop-off points highlight which data is most helpful for each user.

Built-in governance

corporate access rules are enforced on every request, including those made by AI agents. Everyone sees only what their permissions allow, while each customer’s context remains strictly isolated.

Interoperable context

built on industry standards, enabling it to be usable beyond our platform, as demonstrated by our partnership with Atlan.

Another question?

A traditional, static context layer describes the data: its definitions, schemas, and origins. It remains unchanged until someone updates it. A dynamic context layer (such as Huwise’s) continuously captures usage signals from a data marketplace—covering how data is actually searched for, viewed, and used. Built at the heart of the data marketplace, the dynamic context layer is enriched with every interaction and becomes increasingly relevant over time.
BI tools capture part of the demand for data by tracking analytics and reporting activity. But they do so only across their own data and for an expert audience. They cannot see the organization’s entire data estate, nor the needs of business teams that do not use BI tools.
Data catalogs document the data being shared through metadata, and they do this well. However, as they were originally designed for data experts they are rarely used by business teams and do not monitor actual data consumption. As a result, they generally miss the usage dimension of context.
It brings together the two dimensions required to provide complete context. On the supply side, it draws on knowledge about the data: the assets, schemas, definitions, and lineage of each data product. On the demand side, it captures usage signals: searches, views, exports, access requests, drop-off points, and user journeys specific to each user profile. Governance rules applied at the point of use are also recorded.
Business teams and AI agents can find the data products they need faster, when they need them. Discovery is guided by usage: the most useful data is shown first, based on each user’s profile. AI agents, meanwhile, no longer simply know what the data is. They understand how the organization actually uses it, making their responses more reliable.
Its main benefit is to create a self-reinforcing virtuous cycle. The more usage signals the marketplace captures, the more relevant its context becomes. A more relevant marketplace is adopted more widely across the organization, and that adoption in turn generates more signals. And because governance is embedded in the context, this large-scale adoption happens securely: everyone, whether human or AI agent, sees only what their permissions allow.
Yes. It is built on industry standards, which we continually monitor as they are evolving rapidly. This ensures that context information remains usable beyond our platform. This guides our collaboration with Atlan, which sees Huwise as an essential partner for capturing usage intelligence.