Transition from data to 'data products': Adopting a product-thinking approach when it comes to enterprise data

A data product is an active, value-generating asset.

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Would you feel comfortable driving a car that did not go through proper product-related quality checks, safety features and usability?

For years, organisations have invested heavily in collecting and storing data. Yet many still struggle to translate that data into meaningful, actionable insight. An effective transition from traditional data management to “data products” requires organisations to adopt a product-thinking mindset, where data is no longer viewed as a passive by-product of operations but as a valuable asset designed, developed and delivered with end-users in mind.

By treating data as a product, businesses can ensure higher quality, greater accessibility and stronger governance, enabling teams to generate more reliable and actionable insights. Curated and well structured data products allow companies to better cater for business needs, behaviours and preferences. Ultimately, shifting towards data products strengthens decision-making across the organisation, improves agility and unlocks new opportunities for innovation and competitive advantage. The challenge is not a lack of data, but rather how it is managed, delivered and consumed. To unlock its full value, the shift from simply managing data to building data products means treating data as a managed, consumable asset designed with the end-users in mind.

This transition marks a fundamental change in mindset, where organisations adopt product-thinking principles, designing data intentionally to serve specific business needs. The result is improved decision-making, better strategic insights and greater operational efficiency.

A data product is a re-usable, self-contained solution that brings together data, metadata, business definitions and templates to address a range of business needs. It may include elements such as datasets, dashboards, reports, machine learning (ML) models, preconfigured queries or data pipelines.

Unlike raw datasets sitting in a data lake, a data product is purpose built. It is structured, documented and delivered in a way that enables users to derive value quickly and confidently. It is designed from the end-user's point of view, ensuring that stakeholders get the best possible experience when interacting with it.

In essence, a data product moves data from being a passive resource to an active, value-generating asset.

A key principle in this transition is to treat data as a managed, consumable asset. Just as physical products require design, quality control and life cycle management, data products must be deliberately developed, maintained and improved over time.

Managed data means it is governed, quality controlled and continuously monitored. It is not left to degrade or become outdated. Standards for accuracy, completeness, timeliness and consistency are clearly defined. This ensures that decision-makers can rely on the insights generated.

Furthermore, a data product must be secure. Access controls, privacy measures and compliance requirements are built into the product from the outset.

One of the most critical elements of a successful data product strategy is clear ownership. A data product is developed and maintained by a single team that understands the business domain, the business processes and the data. This ensures that technical expertise is combined with deep contextual understanding.

Each data product has a dedicated contact person (or product owner). This person is ultimately responsible for the data product and its quality. This clear accountability eliminates ambiguity about who maintains the product, who handles issues and who prioritises enhancements.

With defined ownership, data products evolve in alignment with business needs rather than becoming neglected technical artefacts.

For data to drive value, it must be discoverable and understandable. A data product is catalogued and described in business friendly language. Metadata, definitions and semantic layers ensure that users know what the data represents, how it was calculated and how it should be used.

Discoverability reduces duplication and inefficiency. Instead of teams recreating similar datasets or reports, they can easily find and re-use existing data products.

Understandability builds trust. When users can clearly interpret metrics and definitions, they are more confident in their decisions. This transparency is particularly critical for advanced analytics and ML models, where explainability strengthens adoption.

Data products are interoperable, meaning they integrate seamlessly across systems and platforms. Standardised formats, APIs and integration patterns allow different teams and technologies to connect to the product without friction.

They are also shareable. Designed for cross-functional use, a single data product can serve multiple teams simultaneously. This promotes consistency in reporting and eliminates conflicting versions of the truth.

Most importantly, they are re-usable. A well-designed data product can support multiple use cases without needing to be rebuilt.

When organisations adopt a product-thinking approach, the impact is significant. Because data products are aligned to specific domains and use cases, they enable richer, more deeper insights.

Improved decision-making follows naturally. Leaders are no longer debating whose numbers are correct; instead, they focus on strategy and execution. With secure, high quality and re-usable data products, organisations gain agility and responsiveness in a rapidly changing environment.

The shift from raw data to data products is more than a technical upgrade, it is a cultural transformation. It requires organisations to embed product management principles into data teams, establish clear ownership and prioritise the consumer experience.

By making data discoverable, understandable, shareable, secure and re-usable, businesses elevate it from a by-product of operations to a strategic asset.

In doing so, they position themselves to extract lasting value from their data, turning information into insight and insight into competitive advantage.