From the Data Value Chain to the Decision Value Chain.

I really enjoyed Adil Ahmed's recent CDO Magazine article, “If Data Is Like Oil, Why Aren't CDOs Managing It Like Oil and Gas Companies?” It is a strong and useful piece because it gives business leaders a simple way to understand the data value chain. Adil argues that, like oil, data does not create value through extraction alone. It needs to be discovered, refined, governed, distributed and consumed before it becomes useful to the business.

That framing matters because many organizations still struggle to explain data strategy in language the C-suite can immediately understand. Adil's upstream, midstream and downstream analogy helps move the conversation away from technical language such as data lakes, medallion layers and governance frameworks, and toward something much more practical: how data flows through the organization to create business value.

Adil has articulated how the data value chain should evolve. At Data Tiles, we believe the next evolution is connecting that value chain directly to the decisions the business is trying to make.

The data value chain remains fundamental. Organizations still need trusted data, governance, quality and ownership. But we believe they should no longer begin by asking, “What data products should we build?” Instead, they should begin by asking, “What are the most important decisions our business makes, and what trusted information is required to make those decisions with confidence?”

That shift changes everything.

The people closest to the decision understand the business context, what the data means and ultimately what good looks like. They understand the outcome they are trying to achieve, the policies that apply and when trusted information is needed. They are therefore best placed to create the trusted, fit-for-purpose data products required to support those decisions.

This does not mean compromising governance. Quite the opposite.

Trusted data products should be created with active governance, lineage, security, policy and enterprise standards built in from the beginning. Governance should be part of the data product itself, not something applied afterwards. The business should never have to choose between speed and trust. Those closest to the decision should be able to create trusted data products while the platform applies governance, lineage, security and enterprise policy by design. The role of the data team evolves from building every individual data product to defining the enterprise standards, reusable assets and governance framework that every trusted data product inherits automatically.

This becomes even more important as organizations move from AI pilots to AI agents. Gartner has highlighted that a lack of semantics, business context and trusted information can make AI agents inaccurate, inefficient and significantly more expensive to operate. Gartner has also warned that agentic AI initiatives will struggle when organizations attempt to scale without the right governance, trust and business foundations in place. McKinsey reaches a similar conclusion, arguing that organizations preparing for agentic AI need to focus not only on the models themselves, but on the workflows, governance and trusted information that enable AI to deliver measurable business outcomes.

We see this as confirmation that AI is not simply a technology challenge.

It is a business challenge.

AI agents should not be left to consume raw enterprise data, interpret conflicting business definitions or determine context for themselves. They should consume the same trusted, governed, fit-for-purpose data products that business users rely upon, complete with business context, lineage, ownership and policy. When people and AI operate from the same trusted foundation, organizations gain something far more valuable than faster access to information. They gain confidence that every recommendation, every automation and every decision is based on information the business trusts.

This is what we mean by becoming decision driven.

It is not about creating more data products. It is about creating the right data products, designed around the decisions that matter most to the business and delivering trusted information at the point of decision. That is where data products create value, not because they exist, but because they enable better decisions.

As Cameron Price, CEO and Founder of Data Tiles, often says: