Dashboard for Mean Time Between Failure
Mean time between failures, or MTBF, is a metric that tells you the average time between software failures. The purpose of this metric is to show you how stable and reliable a product is, and give a piece of insight into performance. If the failures are frequent, it can highlight a need for change. You might notice that although problems that’re coming up aren’t difficult to fix, they’re happening frequently enough to identify a problem. This could be a team efficiency issue, a testing issue, or something else.
How to Measure Mean Time Between Failure
Learn about Mean Time Between Failure, including how to measure it, and leverage it in dashboards and visualizations with Metabase.
What is Mean Time Between Failure?
Mean time between failures, or MTBF, is a metric that tells you the average time between software failures. The purpose of this metric is to show you how stable and reliable a product is, and give a piece of insight into performance. If the failures are frequent, it can highlight a need for change. You might notice that although problems that’re coming up aren’t difficult to fix, they’re happening frequently enough to identify a problem. This could be a team efficiency issue, a testing issue, or something else.
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How to calculate Mean Time Between Failure
To calculate a product’s MTBF, you’ll need to divide the total number of operational hours by the number of failures. For example, if your product has been up and running for a total of 1,000 hours and there were 12 failures, your MTBF would be approximately 84 hours. That means every 84 hours your product is operational an error is more likely to occur.
Other KPIs to measure related to Mean Time Between Failure
- Online Application Performance
- Time to Value
- Build Time
- Recovery Point Objective
- Downtime
- Bug Count
- Automated Test Pass Percentage
- Infrastructure Service Value Scores
- Storage Costs
Why build a dashboard for Mean Time Between Failure?
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