Data product strategy and vendor evaluation
Choosing the right healthcare data foundation
A strategic evaluation of healthcare data partners across coverage, quality, freshness, usability, integration effort, and cost. The goal was not to find the largest dataset. The goal was to identify the data foundation that best supported the platform's product roadmap and commercial use cases.
Vendor economics and results are presented as approximate ranges. Customer and contract details have been excluded.
- Role
- Senior Data Solution Strategist — data strategy, sourcing, and evaluation
- Timeline
- 2021 to 2022
- Team
- Data engineering, procurement, and commercial stakeholders
The decision
The data partnership we had was becoming a product constraint.
The existing data vendor portfolio had grown over time, creating an opportunity to reassess overlap, coverage, product fit, and long term economics.
The platform's existing data partnership raised growing concerns across cost, operational reliability, and product fit. Renewal economics were moving in the wrong direction, refreshes were not dependable enough to build product features on, and the data increasingly limited what the roadmap could responsibly promise.
The question was never whether the incumbent data was usable. It was whether this foundation could sustain the platform's roadmap and commercial use cases, or whether a change was warranted.
Evaluation criteria
Every partner was compared against the same eleven questions.
01Patient and HCP coverage
Breadth of the populations the platform's workflows depend on.
02Claims completeness
Depth and consistency of the claims records behind cohort work.
03Clinical and demographic enrichment
Attributes available beyond the raw claims layer.
04Match rates and overlap
How much of each source was usable, and how much duplicated what we already had.
05Data freshness
Lag between real-world activity and availability in the platform.
06Refresh reliability
Whether scheduled refreshes could be depended on by product features.
07Integration effort
Engineering cost to onboard, map, and maintain each source.
08Documentation and usability
Whether teams could self-serve answers about the data without an analyst.
09Permitted use cases
Usage rights measured against commercial and internal workflows.
10Commercial terms
Cost structure, renewal exposure, and flexibility over time.
11Platform scalability
Whether the source could grow with the roadmap, not just the current scope.
Analysis approach
SQL profiling against real workflows, not vendor slide decks.
Coverage, duplication, usable records, provider reach, and compatibility with existing workflows were profiled directly with SQL based analysis. Overlap analysis showed how much of each candidate source genuinely added to what the platform already held.
The profiling used the platform's existing data assets and licensed evaluation samples. It did not involve, and does not claim, any proprietary identity graph.
What the analysis answered
- — How much of each source was actually usable
- — Where the sources overlapped and duplicated
- — Which workflows each source could support as-is
- — What each option would cost to integrate and maintain
Options and tradeoffs
Three options, compared on more than price.
01
Initial enterprise dataset
The incumbent foundation. Broad and familiar, but costly, with refresh reliability and product fit concerns that were getting harder to design around.
02
Alternative vendor considered
Attractive on cost and headline coverage, but with thinner enrichment and an unclear fit with the platform's existing workflows.
03
Final partner selected
Stronger platform fit, broader enrichment, and sustainable economics. The integration path was longer, and that tradeoff was accepted deliberately.
Vendor names and confidential terms are excluded. The tradeoffs are the part worth showing.
Product recommendation
One recommendation, balanced across seven factors.
The recommendation was a product decision before it was a data decision. Each option was weighed against what it meant for the people using the platform, what the roadmap could promise, and what the arrangement would cost over time — not only what it cost at renewal.
User workflow requirements
Data quality
Product roadmap needs
Commercial opportunity
Integration effort
Privacy and usage constraints
Long-term cost
Outcome
Lower data cost, broader usable coverage, and a more sustainable platform foundation.
- —Reduced dependence on a high cost data arrangement
- —Improved flexibility across platform modules
- —Expanded usable data coverage
- —A more sustainable foundation for commercial delivery
The selected data foundation reduced dependence on a high cost data arrangement, improved flexibility across platform modules, expanded usable data coverage, and created a more sustainable foundation for commercial delivery.
Financial figures from this decision are deliberately held as approximate ranges pending validation. What held up immediately was the structural outcome: a foundation sized to the product roadmap rather than to a contract renewal cycle.
What this demonstrates
The judgment behind the data decision.
Healthcare data strategy
Structured vendor evaluation
Build versus buy judgment
SQL informed decision support
Executive recommendation
Commercial and product tradeoffs