Companies today recognize that their competitiveness depends on their ability to analyze and leverage data. The challenge, however, is that data is often fragmented and scattered across silos and disconnected systems.
In this context, it becomes essential not only to define a transformation strategy but also to decide how data will be managed within the organization, so it can be quickly and efficiently accessed by all stakeholders.
In this article, we explore one of the most promising evolutions in this direction: the approach of what we call a data product.
What Is a Data Product: The Foundation of a Data-Driven Company
At the heart of the data product lies a profound evolution in the way enterprises approach data.
The Starting Point: The Data Project
For years, using data to support business decisions has been the result of ad hoc projects, often initiated by individual business units.
Data teams were tasked with collecting, integrating, and analyzing data for a specific purpose, whether a report or an interactive dashboard. But these efforts lacked a systemic vision. Only the team that launched the project could benefit from the resulting insights; for other divisions, accessing or integrating that same data meant starting from scratch with a new ad hoc project. The result: wasted time, duplicated efforts, and missed opportunities.
Data Product: The Modern Way of “Seeing” Data
The data product model was created to overcome these project-based limitations. In this approach, data is no longer a raw ingredient to be processed case by case but a ready-to-use digital product, designed to be easily found, understood, integrated, and reused, even by other teams or external partners.
A data product is therefore a dataset or an information asset designed with the same rigor and methodologies used in software development. It has a lifecycle, stages (from design to continuous improvement), and follows pipelines and centrally defined processes. It is a powerful tool to overcome the information fragmentation typical of large organizations and to connect data sources in order to generate valuable insights.
A data product is characterized by:
- Clear purpose and business orientation
Every data product is created to address a concrete need. Its purpose is explicit and measurable.
- Composability
A data product is designed to be composable, meaning it can be combined into more complex informational products. This capability, crucial for moving beyond project-based logic, enables the rapid creation of new solutions, larger products, or sub-products by leveraging existing data products, much like modular software.
- Defined ownership
Every data product has an owner responsible for its quality, maintenance, and availability. This ensures reliability and continuous updates.
- Integrated governance and discoverability
Since a data product potentially serves the entire organization, it must comply with corporate policies on quality, access, and security. Discoverability (the ability to easily locate it) is ensured through inclusion in a corporate data catalog. This allows teams to know what already exists, avoiding duplication, wasted time, and multiple redundant copies of the same data.
- Accessible, up-to-date documentation
A data product must be documented: description, structure, data schema, update logic, and possible use cases. This enables other teams to use it independently.
- Versioning, updates, and reusability
Data products are not static. Users must know which version they are working with, what changes have been made, and how frequently the data is updated.
Practical Examples of Data Products
Data products can be applied across any industry and business function, from manufacturing to marketing, from logistics to HR. Wherever there is data relevant to the business, a product can be created to be used, reused, and in some cases even monetized. Examples include:
- A catalog of customer profiles for marketing automation campaigns, integrated via API with third-party tools.
- A view of logistics performance across the entire supply chain, accessible and shareable among departments or partners.
- Mobility data (already aggregated and analyzed) collected by telco operators and sold to retailers or brands to analyze foot traffic in specific urban areas—supporting decisions on new store openings or product assortments
Data product or Data as-a-product?
The two expressions—data product and data as a product—are often used interchangeably, and understandably so: both refer to a new way of interpreting and managing enterprise data. However, there is a subtle but useful distinction:
- Data as a product is the approach, the mindset. It means applying product thinking to the world of data: user experience, quality, reusability, clear accountability, and versioning. It represents a cultural shift even before a technical one.
- The data product is the tangible outcome of this approach. It is the final object that embodies those principles: an information asset designed as a true digital product—governed, documented, maintained, and ready to be used by teams, professionals, or systems.
Data Product and Digital Twin: A Strategic Alliance
One of the most ambitious goals of digital transformation initiatives is the creation of a digital twin of the organization: a digital ecosystem that faithfully mirrors its structure and processes, continuously fueled by data from every operational area.
Naturally, the larger and more complex the company, the more this vision collides with a reality of heterogeneous systems, inconsistent data, and fragmented management approaches. And yet, it is precisely in such contexts that transformation delivers the greatest value. If the organization can access all its data, it can finally apply predictive algorithms across processes, uncover hidden inefficiencies, simulate scenarios, and make truly data-driven decisions.
The data as a product approach helps companies build their digital twin. Standardized, interoperable, and well-governed data products act as modular building blocks, which can be combined in different configurations to quickly develop new analyses, dashboards, and applications tailored to business needs.
How to Develop a Product Mindset
Building data products requires a cultural shift, because it means not only recognizing the value of data but also treating it with the same care and accountability as consumer-facing products. Where to begin?
Data as a Shared Responsibility
Every data product must have an owner, but everyone must feel accountable for its correct use. This requires clear policies as well as a mindset shift: data is not just a file or a piece of information—it is an asset on which the company’s competitiveness is built.
From design to delivery
A data product usually stems from a specific need, but it must be designed for reusability and scalability. Time should be invested in the design phase: defining objectives, target users, metrics, interfaces, APIs, and update processes.
Equipping Yourself with the Right Technology
Cloud platforms, APIs, catalogs, data platforms, pipelines, and versioning tools are the invisible engine of the product mindset. Without a solid, composable technological foundation and a modern data architecture, the risk is to fall back into isolated, project-based initiatives.
Creating Small, Measurable Wins
Instead of aiming for the ultimate data platform from the start, it’s advisable to begin with limited use cases that have a tangible impact: a self-service report, a dynamic dashboard, a shared customer view. Each small success strengthens the data culture—and this is where every journey should begin.
Adopting a data as a product approach is a strategic step toward building a more connected, intelligent, and future-ready company. If you’d like to learn more about how to apply this model within your organization, identify the first use cases, or evaluate the most suitable technology solutions, the Kirey team is here to help.
Contact us for a personalized consultation: together, we’ll identify where to start and how to create real value from your data.
