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.
At the heart of the data product lies a profound evolution in the way enterprises approach data.
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.
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:
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:
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:
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.
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?
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.
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.
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.
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.