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A:
When trying to show that a Metabase update actually improved team productivity without presenting new hazards, you should keep an eye on a number of usage metrics, time-to-insight, and data reliability signals. Start with adoption KPIs, like the growth in the number of dashboards that are presently active, saved queries, or scheduled reports each week, as higher engagement usually means that the update made analytics easier or faster to use. Quantify the gains in query performance to show how the updated activities have accelerated. By examining mean time to decision-basically, the time it takes to receive a response to a query-you can determine whether productivity declined following the update.
Keep an eye on data refresh issues, query failure rates, and permission anomalies (such as authorization mismatches or unauthorized access attempts) from a risk perspective. Additionally, monitor the volume of bug tickets or rollbacks, as a reliable update should lower those. The best indication that the update increased productivity without increasing operational or data risk is if your analysts or business teams are spending more time acting on the data and less time waiting on it, and if your system logs indicate fewer crashes or errors.
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