How Health Tech Companies Actually Monetize Their Data

Every health tech company I talk to believes its data is valuable. Most of them are right. Almost none of them are making money from it.

The gap isn't the data. It's that data monetization conversations stall in a predictable way: five stakeholders, five favorite use cases, no owner, and no agreement on what happens first. Engineering hears "sell our data" and worries about consent. BD hears it and starts calling pharma. The CEO hears it and asks for a number no one can produce. Twelve months later, the data is still sitting there.

Here's the framework I use to get teams unstuck.

Start with who actually pays

"Selling data" isn't one market. It's at least four, and they buy different things:

Pharma HEOR teams buy longitudinal, de-identified real-world data to support outcomes research and market access. They care about depth, validated instruments, and linked data types.
Payer analytics groups buy signals that predict cost: utilization, risk, engagement. They care about your ability to tie interventions to avoided spend.
CROs and trial sponsors don't buy data at all in the classic sense. They pay for patients. Specifically, pre-screened, engaged patients who match inclusion criteria for trials they're struggling to fill.
Other health tech companies buy enrichment: data that makes their own product smarter.

If you can't name which of these you're building for, you don't have a data strategy yet. You have an asset and a hope.

Depth beats breadth

Teams consistently undervalue what they have because they compare themselves to claims aggregators with tens of millions of lives. Wrong comparison. The aggregators have breadth; they don't have you.

A recurring, validated, patient-reported signal on an engaged population, linked to claims, clinical outcomes, and increasingly device data, is something the big datasets cannot replicate. Buyers know this. A modest population with monthly validated instruments and multi-year history is a differentiated asset. Ten million unengaged lives with a single snapshot each is a commodity.

Sequence one effort to serve two goals

The most common strategic mistake is treating near-term revenue (usually clinical trials support) and long-term data sales as competing bets for limited resources. They're not, if you sequence them correctly.

Organizing your data around real trial inclusion and exclusion criteria is exactly the same work that, over time, produces a clean, structured, saleable real-world data asset. Trials revenue arrives in quarters; the RWD asset matures over years. One effort, sequenced correctly, serves both. I've written more about the trials side in Clinical Trials Are the Fastest Path to Data Revenue and the long-term side in What Buyers Actually Pay for De-Identified Real-World Data.

Do the unglamorous readiness work

Before any buyer conversation, you need honest answers to four questions:

What do we actually hold? Source, volume, history depth, update cadence. Per asset, in one table.
What are we allowed to do with it? Consent language, data use agreements, and restrictions, mapped per asset. This is where deals die late if you skip it early.
What would a buyer screen for? Structure your data against real criteria (trial eligibility fields, HEOR endpoints), not your internal schema.
Who owns the decision? One accountable owner. Committees don't monetize data.

Get everyone in one room

The failure mode is asynchronous: competing ideas circulating in docs and hallway conversations for months. The fix is synchronous. Run a facilitated working session where the inventory is on the wall, the buyer segments are explicit, and the group leaves with one agreed priority and a sequenced roadmap.

That's the format I run as a Data Strategy & Monetization engagement: a full-day session plus a written strategy summary your team can execute against. But whether you run it yourself or bring someone in, get the decision made in a room, not a thread.

Your data is probably worth more than you think. It's just worth nothing until someone decides what happens first.