Spreadsheets aren't the problem. Undefined concepts are. A column labeled “active users” that nobody can fully specify will produce different numbers every time someone rebuilds the report, and the team will slowly stop trusting whichever dashboard showed up last.
Define the unit of analysis first
Before you compute anything, decide what each row represents. Is it a patient, an encounter, a day, a claim? That question sounds trivial; it's the source of most of the disagreement I've seen in operational reports.
Write the definition next to the metric. “Active clients (active = at least one documented contact in the trailing 30 days).” If two analysts would disagree about whether a given case counts, the definition is too loose.
Build a minimal check layer
Every analysis-ready table should ship with a small set of diagnostics: row counts against a known expected range, null rates on identifying fields, duplicate-key checks, and at least one sanity metric a subject-matter expert would recognize. These run in seconds and catch the embarrassing mistakes before they ship.
Reporting primitives — one clean count, one clean average, one clean rate — compose well. The goal at this stage isn't insight; it's a table you can stop arguing about.