In a previous essay, I argued that when AI makes everything technically possible, the most important job in data becomes deciding what to prioritize. But even the right priorities won't land if you describe them in language the business doesn't use.
In nearly twenty years working with data, I have yet to meet a business stakeholder who woke up wanting a data product. They want to know why customer churn is accelerating, whether a pricing change will hold margin, or which inventory decisions drive basket size.
"Data as a product" brought real discipline to how data teams work: ownership, SLAs, documentation, quality. That shift was necessary and overdue. But for the boardroom? The right vocabulary for the wrong conversation.
The underlying problem is that data has two sides, and we keep collapsing them into one. There is data the thing: the actual bits and bytes that need to be stored, processed, and managed. And there is data the concept: the strategic resource that funds decisions and drives outcomes. The first is technical. The second is business. And almost everything we've built to bridge them, including the "data as a product" framing, still lives on the technical side.
No business leader thinks about investment budget in terms of banknotes, bank accounts, or treasury operations. Budget is a concept. It represents the capacity to fund a project that delivers an outcome. The entire discipline of finance is organized around that abstraction: ROI, return on capital, cost of investment. Not "money as a product." And yet in data management, even when the intent is to bring business and technology together, the conversation quickly pulls toward data assets, data elements, lineage, catalogs. These are real and necessary. They are also firmly on the technical side.
A business leader asked to engage with data at that level faces the equivalent of a CFO being asked to care about individual banknotes.
The abstraction layer is missing.
When you ask practitioners which business outcomes depend most on data, strategy and planning tops the list. Not reporting. Not dashboards. Strategy. The business already sees data as a strategic resource. The data profession just hasn't built a language that meets them there, let alone one that helps them act on it.
The shift we need is from describing what data is to articulating what data does. Not "here is a well-governed, documented data product with an SLA." But: "this is the data capability, the investment, and the organizational capacity required to deliver on our top three strategic priorities".
Don't get me wrong. Ownership, quality, governance: those foundations remain essential. But the framing needs to evolve. Data as a product was the right conversation for the data team. Data as a value driver is the right conversation for the business.
I've seen this shift happen. A leadership change brought someone with a purely business background into a data science team. Almost overnight, the conversation moved from infrastructure problems to customer outcomes. No new platform. No restructuring. Just a shift in vocabulary.
That's the real job of the data leader now. Build the abstraction layer this profession has been missing. Not by pushing value language onto the boardroom, but by creating the conditions where the organization naturally adopts data as a second language when defining and executing strategy.
Simple to say. Hard to do. But when that happens, data won't be a separate conversation. It'll be the language the strategy is written in.