Analysis / Data engineering and quality
Detect data grain mismatches in a report
Detect join driven row multiplication and repeated measures, quantify metric inflation, and propose aggregation that preserves the intended grain.
Data analyst · Analytics engineer
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Your starting prompt.
Inspect [report SQL] and [input datasets] to identify grain mismatches such as order totals repeated on line items or account values repeated across users. Calculate row multiplication and metric inflation under the current joins. Propose preaggregation or join changes, with before and after counts for [sample period]. Keep additive and nonadditive measures distinct and document assumptions about unique entity keys.
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Method and review
Keep the assumptions visible.
Profile keys, row counts, types, missing values and join cardinality. Retain source lineage and concrete exception records so a correction can be checked.
For this task, check
Distinct sums can mask duplicate values while also discarding valid equal valued transactions.
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Before you begin
- Bring exports you are allowed to upload, or data your Workspace can already query.
- Choose the reporting period and agree what each record represents.
Work with permitted files or queryable data. Verify the result against the original records.
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Make it your investigation.
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