As the success of process automation and artificial intelligence depends so heavily on data quality and availability, the need for information governance becomes ever more pronounced. Productization clarifies data ownership, form, history, role and governance methods. In this article, we explain what data productization means in practice.

What is a Modern Data Product?

A modern data product is a thoughtfully selected, governed and value‑driven unit that serves its user , whether it is a human, system or algorithm. It may take the form of a shared foundational data product for the organization, or a highly refined specialized product, such as a dataset that describes the purchasing behaviour of a customer segment for market‑analysis purposes.

Viewing data as a product applies a boundary: tied to a specific purpose. A multi‑functional system, on the other hand, contains data for many products and therefore cannot itself be developed as a single product. Data becomes information only when it has a title and in this case, the data product is given a name.

Without productization, data easily remains fragmented, hard to find and unreliable. AI and agents demand sharper quality from data products. The dynamic nature of AI requires more from data: consistent quality and clear descriptions. A data‑product creator cannot guess what insight an agent might draw from a piece of data or its combination. The requirement for recency is also emphasized, as the data may slip into any usage scenario where human cognitive checks are often light.

A good data product has a documented, clear purpose, a coherent structure and reliable, business‑useful content that is easily understood. It has an appointed owner who is responsible for the product’s content, lifecycle and ensuring the data is available in the right form at the right time. A well‑constructed data product is easy to find, understand and use without deep technical expertise. It offers clear interfaces, defined usage patterns and transparent service‑level commitments.

Data Products Build Upon Other Data Work

The product mindset resembles the Data Mesh paradigm, where the purpose of data is emphasized, and responsibility for capturing and using the correct data moves from a central IT team to the business where data originates. The goal is to build data as one would build any product: owned, continuously maintained, clearly described and value‑creating. Like any other product, a data product has versions, a roadmap and feedback channels, and the value it produces is measured.

Ideally, data products are developed when the business’s most important data has already been unified in the course of core data work, also known as Master Data Management (MDM). A distribution platform is needed for the sharing of data products. Often a specialized data platform is the recommended solution. Once products are ready, they naturally become shareable: a once‑refined dataset is logical to offer as a foundational building block for applications, reports and AI usage.

An organization’s capability for data‑driven leadership does not emerge in perfect order; it must be planned as evolving from the start. The product mindset helps improve data usability even when the environment is still maturing.

The Criteria for a Data Product Evolve with Digitalization

Over the past decade, it has been easy to call any standardized raw dataset a data product. Meanwhile, usage scenarios have shifted from reporting toward process automation and AI agents, which demand much more from products.

Data products are not built only for humans, but also for agents, automations and orchestration layers. This imposes a clear requirement: the data product must be described and must not rely on hidden expertise. The conceptual model (ontologies, taxonomies) is often managed in a separate layer so that multiple data products can reference a shared vocabulary and stay consistent.

A modern data product must be contextual, semantically enriched and business‑linked. As analysts such as Gartner and DAMA emphasize: a data product does not need to function as a full process tool, but it must provide enough business context and semantic references to operate independently and seamlessly across different users and agents. Without this semantic connectivity the risk is “data silo 2.0” where each data product describes itself differently and agents cannot link them automatically.

Culture Behind the Data Product

Implementing data products requires a change in mindset and culture. It is essential to move away from centrally controlled IT and to learn building data quality in a business‑centric, agile way. This requires that people know how to talk about data usage and quality and, therefore a shared language around information governance is needed.

Data also needs clear owners. Domain thinking helps here, for example, customer‑data ownership lies with the business unit that knows the customers best. In this way the data is developed driven by user need. Sustainability and long‑term value are ensured by a disciplined data‑governance model that complements the business‑driven side, with practices setting minimum quality requirements, clear responsibility models and continuity.

Without clear responsibilities and structures, “nobody’s land” data easily emerges — data with no owner, purpose or quality guarantee. That data cannot be used in business and AI has no hook into it.

Change execution also involves a lot of communication and collaboration across organizational stakeholders so that data products are widely adopted and maintained as new requirements emerge. In this coordination the data‑owner plays a central role. Of course the data owner doesn’t need to handle all change‑management tasks alone; often a more central “data office” supports the change and enables safe, efficient, business‑supporting use of data products.

Data Product Quality is a Major Part of AI Readiness

AI solutions are not isolated projects but merge into intelligent business. A modern data product forms the foundation.

Data can only feed into AI if it is findable, understandable and reliable. That’s why a modern data product offers the structure that ensures these prerequisites. At the heart of corporate data lies a comprehensive data catalogue that documents what data exists, where it lives and how it can be used. In addition one must have clear ownership to guarantee data quality, continuity and improvement. Finally distribution capability ensures that data flows seamlessly between systems, agents and business units.

We are on the path toward a world where agent‑based AI, whether custom or product‑based. can navigate the business jungle via data catalogues and process documents, for example, identifying improvement opportunities, aiding audit or recognizing cross‑unit collaboration potential. These broad scenarios require data warehouses to grow into genuine knowledge networks which is a decades‑long journey.

A well‑constructed data product is, however, the first step on this path. Already at this stage good data enables AI and intelligent business, where AI is not a standalone solution but is seamlessly embedded in everyday processes. In the next stages, connections between data products strengthen so that the reach of AI expands from individual problems to whole processes and business domains. That is exactly why data products are a crucial first step on the journey toward enterprise‑scale AI.