Despite the very impressive advances in AI in the last three years, there are recalcitrant problems that still haunt and hamper its effective application. I’m referring to data quality issues, faulty data governance, correctly prioritized data operations and a subjective way to measure impact on business results.

But with the adoption of a product-led data strategy, these universal problems may finally be solved, at least within the scope covered by data products.

The Product Approach To Data

A data product is a reusable, governed and measurable package of business insights. It is designed to solve a specific business problem and deliver tangible value. Instead of ad hoc data requests, teams create standardized data products that can be used and reused across the business.
Think of it like this: a centralized data team might create a «customer 360» data product. This product contains everything a sales team needs to understand their customers. This can include past purchases to support tickets to website visits. It is a single source of truth that is reliable, governed and consistently updated. That product can then be used by the marketing team to personalize campaigns, the product team to guide feature development or the executive team to measure growth.

Now let’s see how packaging data services and business insights into consumable data products address governance, operations and business impact.

From Governance As A Goal To Governance As A Feature 

Old-school data governance focused on rigid rules and compliance often slows down innovation. The data product approach flips this on its head. It embeds governance directly into the product lifecycle, making data findable, trustworthy, and usable from the start.

This new model ensures every data product meets quality standards and is easy to find in a central catalog. This helps teams find the data they need with confidence, accelerating the opportunity to use it to build AI solutions that are reliable and responsible.

From Technical Silos To Unified Operations

Data science and AI projects often suffer from fragmented workflows. Data teams handle the ingestion and cleaning, while machine learning teams build the models, and ops teams manage the deployment. This creates friction, slows down progress, and leads to inconsistent results.

A data product framework unifies these operations under a single umbrella. It brings together Data Ops, MLOps, and Agent Ops into a single lifecycle. This ensures that every stage, from data source to AI model deployment, is managed with a consistent framework and a clear focus on the final business outcome.

From Vague Promises To Measurable Outcomes

For years, the value of data projects was difficult to quantify. A new data lake might be «great for the future,» but it has no clear, objective measurement of its success today.

The data product framework changes this by building measurement directly into the process. Each product is designed with a clear business goal in mind, and its impact is measured against key performance indicators. This approach helps you prove the value of your data investments and build a culture that is focused on tangible results.

In summary, the adoption of data products has three important effects on a modern data management strategy:

• Multiplication effect: Data products, once created, can be easily used by other areas for compatible purposes
• Focus effect: Data products streamline the data governance and data operation frameworks, enabling an intense focus on things that are actually being used and that provide value to the business

Objectivity effect: Data products usage (or abandonment) is easy to track, and this can be correlated with OKRs achievement in order to measure their business impact.

So if the usage of data products brings so many benefits, why aren’t they more widely used? This is probably due to the fact that data products imply the adoption of a decentralized data architecture, which is a big paradigm shift from the traditional and widely adopted centralized architectures.

Nonetheless, I believe a hybrid architecture may be an ideal and viable solution in most cases, but that’s a topic for another time 😉