A meeting that should drive a decision turns into an investigation and momentum evaporates.
Somewhere a metric drifted — in a SQL query, a Python notebook, or a Slack thread that never got documented. In regulated settings like RWE and HEOR, that drift can hold up a paper submission or stall a study for months.
Here are three questions that we've learned to ask when trust is breaking in analytics:
- Can you trace a dashboard number back to the exact pipeline step that created it?
- Can anyone see a plain-English definition of the SQL that produces your key metrics?
- When definitions change, is there a clear history of what changed, when, and why?
If you answer "no" to one (or all) of these questions, you're not alone.
This isn't a tooling problem. It's a context problem.
Most teams were never set up to capture context at all.
Every time a definition holds, trust builds — and that trust is what keeps studies moving forward.
Ask yourself and your team:
When a metric changes, how easy is it to trace what changed, when, and why?
- Always easy — we have clear version control + definitions
- Sometimes possible — depends on the metric
- Rarely — we piece it together from docs/Slack
- Almost impossible — no real history
Related article: context infrastructure is the modern analytics stack addition to keep definitions aligned for humans and AI