A Series A healthtech platform on HubSpot could not trust its own reporting. We rebuilt the data layer: agreed definitions, deduplicated records, and validation rules that hold. Reporting that used to take days now takes minutes, and leadership trusts the pipeline again.
Context
The company was a Series A healthtech platform selling into clinics and provider groups, running on HubSpot with a warehouse bolted on for reporting. Growth was healthy and headcount was climbing, which is exactly when data problems stop being an annoyance and start driving decisions.
The Problem
Nobody trusted the numbers. The same account showed up three different ways, lifecycle stages meant different things to different teams, and every board report was rebuilt by hand because no two dashboards agreed. Specifically:
- No shared definition of an account, a qualified lead, or an active opportunity.
- Thousands of duplicate and partial records accumulated over two years of fast growth.
- Reporting pulled from the warehouse, but the warehouse pulled from dirty source data, so the outputs were confidently wrong.
- Compliance-sensitive fields were inconsistently filled, a real risk in healthtech.
What We Did
We fixed the foundation before touching automation. In sequence:
- Defined the model. Ran working sessions with sales, marketing, and CS to agree on one definition each for account, lead, MQL, and opportunity, then wrote them down where everyone could see them.
- Cleaned and deduplicated. Merged duplicate accounts and contacts, standardized key fields, and archived dead records, with a documented, reversible process.
- Built validation that holds. Added required-field rules, formatting constraints, and ownership on the compliance-sensitive fields so the data stays clean going forward, not just once.
- Rebuilt reporting on the clean layer. Pointed dashboards at the corrected source of truth so every team reads the same numbers.
The Result
Reporting stopped being a fire drill. What used to take a person the better part of a week now runs in minutes off data everyone trusts. Leadership got its confidence in the pipeline back, the compliance-sensitive fields are reliably populated, and the clean foundation set the company up to automate safely instead of automating on top of a mess. This is the groundwork every GTM engineering project depends on.
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