For decades, data has been treated as a digital byproduct. Logs, clicks, transactions, sensor feeds, vast amounts of it generated daily, stored in bulk, and archived with little strategic intent. Companies were content to let it pile up, assuming that somewhere, someone would make sense of it.

That time has passed.

A quiet but consequential shift is underway: data is no longer exhaust. It is infrastructure engineered, governed, and increasingly treated as a first-class product.

This can be seen with the new EU Data Act coming into force this September. The most significant overhaul to data regulations since the GDPR, the legislation marks an important shift where policy considers not only the data rights of individuals, but clearly recognizes the fact that data products and services now drive a standalone economy. This presents both compliance challenges but also significant new growth opportunities across both consumer and industry data markets.

And for intelligent enterprises, this shift is not just semantic; it is existential. The ability to transform operational byproducts into scalable, reusable data assets is fast becoming a defining feature of digital leadership.

The end of accidental data

The era of data abundance was not, in itself, a strategic advantage. Most enterprises today sit on petabytes of information that remain structurally idle, fragmented across silos, locked in proprietary systems, or unfit for real-time use. Worse still, much of it is considered only after the facts and used for hindsight, not foresight.

By contrast, data products are designed with intent. They are curated, versioned, and made discoverable across teams. They have owners, lifecycles, and service levels. Whether it’s a real-time API for fraud detection or a machine learning model trained on purchase behavior, data products are architected to be consumed by humans, machines, or both.

This evolution from data hoarding to data productization is not merely technical. It represents a shift in how value is created and compounded in the enterprise.

Software is the container. Data is the fuel.

The historical division between software and data is eroding. Increasingly, the most impactful digital products are hybrids: their utility is shaped not just by functionality, but by the quality and intelligence of the data that powers them.

Consider Netflix, where the recommendation engine is not a feature, but the product. Or modern CRMs, which no longer merely log activity, but dynamically score leads and trigger next-best actions based on models that are fed by artificiaul intelligence (AI). Even in industrial contexts, manufacturers are turning telemetry from field-deployed equipment into predictive maintenance systems, creating new services and revenue streams from data once deemed disposable.

Companies that treat data as a product see 20% to 30% improvements in customer satisfaction, and 15% faster time-to-market, data from McKinsey says. The message is clear: intelligence is not layered on top but is built in, and data is its foundation.

The new data supply chain

To enable this transformation, enterprises are beginning to adopt a new kind of supply chain, one optimized not for goods, but for information. This includes:

• Data pipelines engineered for reuse, not one-off reporting
• APIs that package intelligence, not just records
• Clear ownership and governance to ensure quality, traceability, and trust
• Product-thinking mindsets applied to datasets as rigorously as to software applications

Companies such as Airbnb and Uber have embraced this approach, creating internal data marketplaces where product teams can discover, request, and apply curated data assets accelerating development cycles and standardizing intelligence across the business.

A shift in talent and culture

Just having the right tools isn’t enough. To truly make use of data products, companies need people with a different mindset and skillset; someone who understands data, knows how to build useful tools from it, and can think like a product manager.

For example:

• A data engineer who not only builds pipelines but also thinks about how marketing teams will use that data to run campaigns.
• A product manager who understands data well enough to help design a customer insights dashboard that sales teams actually want to use.
• A data scientist who collaborates with software developers to turn a churn prediction model into a live feature inside a mobile app.

It also requires a cultural shift. Traditionally, IT was seen as a back-office support function. But today, data needs to be everyone’s business.

For instance:

• A supply chain team using real-time data products to adjust delivery routes during monsoons.
• A finance team making daily decisions based on dynamic forecasting tools, not just quarterly reports.
• A store manager using a data dashboard to reorder fast-moving items before they run out.

This is not just about new roles. It’s about making data a central part of how every team operates and decides.

LinkedIn predicts that by 2030, half of all technical roles will require hybrid skill sets fluent in both AI literacy and domain understanding. Organizations that fail to cultivate this talent risk not just technical debt, but relevance debt.

Why this matters now

AI’s rapid integration into enterprise workflows has forced a reckoning. Models are only as good as the data they are trained on. Insights are only as valuable as the systems that deliver them in context. And decision-making is only as intelligent as the information it is fed.

With PwC forecasting $15.7 trillion in AI-driven economic growth by 2030, the bottleneck is no longer compute power or model sophistication. It is data readiness.

In this context, turning data exhaust into data products is not simply good hygiene; it is a growth strategy.

Building an intelligent core

Enterprises that continue to treat data as an afterthought will find themselves drowning in noise. Those that engineer it with purpose shaping it into modular, accessible, and intelligent products will lead in speed, insight, and impact.

The industrial economy had oil. The digital economy had software. The intelligent economy has data products crafted not by chance, but by design.