Managing Data as a Product, Not a Project

Since 2021, many organisations have embraced the data transformation path, starting new initiatives to serve business needs and support decision-making processes. However, many of these projects failed to deliver the desired results to end users' requests. Indeed, many projects contained for example dashboards that were never used, models that were never adopted and slow time-to-value.

In the ever-evolving world of data, rather than developing data initiatives using traditional approach with one-time delivery, organisations shall manage data as a product, not a project.

In this article, we’ll see what is a data product and how it answers business needs?

What Do We Mean by a Data Product?

First, let’s define simply what a data product is?

A data product is a governed and reliable data solution built for business teams to consume, and it evolves gradually over time. The conception is also user-centred, meaning that the development of a data product is launched after a business worker expresses a need for it.

Also, a data product follows these principles:

• Discoverable: data must be easily identifiable and catalogue within the organisation
• Accessible: users shall have access to data efficiently and without barriers by using user-friendly interfaces such as search engine and standardised protocols
• Reliable: data must be reliable and consistent, ensuring trusted use for decision-making and analysis
• Non-ambiguous: data must be clearly defined and precise, avoiding any confusion or misunderstanding
• Safe and governed: Data must be compliance with organisation’s data governance and rules, meaning enforced protection against non-authorised access.

At last, a data product has users and customers and designed to solve a specific business problem. Finally, any data product has clear ownership and lifecycle.

Rethinking Data Delivery: From Projects to Products

As mentioned above, delivering a data product to business teams differs significantly from delivering a classical data project through traditional approach. Instead of building the use-case and then have it deliver in a single release; a data product has iterative releases where each of them answer to a new business problematic.

For example, imagine your business partner is requesting a new dashboard based on KPIs from sales data. He comes to you and then building the product. Months later, he comes back with new needs to fulfil business goals. Then the dashboard evolves with new features such as new KPIs or updated data’s freshness.

Before any development work begins, you and the business teams must work together to determine the measurable outcomes that the data product will generate, and agree on a shared understanding of the business need. This will ensure that the solution fits properly.

In addition, the research phase of the solution is not technically focused. Rather than searching for the best possible algorithm, the key question is how the solution—during the data product developing phase—will serve the end user and how to measure its value.

These new ways of working bring significant changes to the organisation. They require strong, cross-team collaboration and transparency to ensure that data products continuously align with business needs, following agile principles.

Additionally, it enables the emergence of new roles inspired by Agile ways of working, such as the Data Product Manager. This role is responsible for closing the gap between data, technology, and business teams by acting as a single point of interface. In doing so, he ensures that data initiatives are aligned with business objectives, translated into clear technical requirements, and delivered in a way that maximises value and adoption.

Implementing a data product approach changes many habits in terms of funding. In a project-based funding model, budgets are allocated and approved upfront for a fixed scope, approved upfront, fixed timeline initiative. Once the project is delivered, funding typically stops. Any changes require new approval cycles and create challenges to adapt when business needs to evolve.

In a product-based funding model, funding is allocated to a long-lived product team, responsible for continuously delivering and improving a data product. Success is measured through usage, delivered value, and measurable business impact, which aligns interests with business outcomes instead of technical outputs.

Why Data as a Product Is a Strategic Mindset Change

In conclusion, adopting a data-as-a-product approach requires a profound cultural shift within the organisation, starting with a change in mindset. Data is no longer treated as a one-off deliverable, but as a long-term business asset that must be owned, valued, and continuously improved.

Firstly, the development of a data product is an ongoing process. As business needs evolve, so does the product. It is inherently user-centred, built in close collaboration with business teams, and shaped by continuous feedback to ensure it remains relevant and actionable.

Secondly, this approach demands stronger collaboration and transparency across teams. They must work together throughout the product lifecycle, sharing ownership and accountability for outcomes rather than simply delivering outputs.

Finally, success is measured not by delivery milestones, but by adoption and business impact. By embedding agility, clear ownership, and a focus on value creation, the data-as-a-product approach enables organisations to turn data into a sustainable competitive advantage, rather than a static technical capability.