Compare trials, suppliers, and operating performance with analyses that retain their source records, transformations, and calculation versions.
Select records, properties, units, and time ranges intentionally. Preserve the membership of a reviewed dataset so a changing dashboard does not silently change the basis of a decision.
Compare yields, cycle times, supplier outcomes, trial conditions, and quality trends. Retain transformations, unit conversions, and calculation versions alongside the output.
Show missing observations, sample counts, and differences in conditions. Distinguish a recorded fact, a derived measure, a statistical association, and an AI interpretation.
Use Python, R, or specialist analysis tools where the work calls for them. Link the input snapshot, script or method version, output, and review back to the operating records.
| Source run | Cycle time | In calculation |
|---|---|---|
| RUN-021Reviewed | 42 min | Included |
| RUN-022Reviewed | 45 min | Included |
| RUN-023Unreviewed | 60 min | Excluded · unreviewed |
| RUN-024Reviewed | 39 min | Included |
| RUN-025Unreviewed | Missing | Excluded · missing |
3 observations included · 2 excluded
Illustrative local data · missing values are excluded, never treated as zero. A smaller average alone does not establish a better process.
A changed average is an invitation to inspect the underlying records. Open the exact sources and assumptions before turning an apparent pattern into an operational change.
Choose a template, shape your workspace, and try the work for yourself. The sandbox is free forever.