Data Analysis
Anomaly DiagnosisAE Report Data Mismatch Investigation
Two AE reports don't match? Compare layer by layer across the definition layer, time layer, and filter dimensions to pinpoint the source of the definition difference and give a fix.
Segment breakdown
by groupAE Report Data Mismatch Investigation is a data-quality capability in ThinkingAI's industry Skill library. When two AE reports show a difference, it helps data analysts investigate by comparing layer by layer across model type, metric definition, time range, and filter dimensions, pinpoint the root cause of the mismatch, and output an actionable fix. This is ThinkingAI's decade of industry experience, packaged into ready-to-use Skills.
Live walkthrough
order_create, finance uses pay_success: difference oneReconciled. The data isn't wrong; both reports are each correct on their own: they simply aren't measuring the same metric. The 100 ($10K) gap is fully explained by three metric-definition differences, so there's no need to doubt data quality and no need to check the tracking.
1. Metric definitions, compared item by item
| Comparison dimension | Operations report | Finance report | Verdict |
|---|---|---|---|
| Analysis model | Event analysis | Event analysis | Same |
| Metric event | order_create (order created) | pay_success (payment success) | Difference one |
| Time definition | Calendar day | Transaction day | Difference two |
| Refund handling | Includes refunded orders | Excludes refunded orders | Difference three |
| Dedup logic | Dedup by order number | Dedup by order number | Same |
2. Gap breakdown: the full 100 ($10K) closes
Recomputing on a common baseline of "payment success, calendar day, includes refunds" gives a baseline value of 3,132 ($10K). On top of that, the operations definition overcounts 68 ($10K) of orders created but not paid (present in order_create, absent in pay_success); below that, the finance definition excludes 21 ($10K) of refunds and, due to a transaction-day cutoff misalignment, undercounts 11 ($10K), so 68 + 21 + 11 = 100 ($10K), the gap closes completely, with no unexplained residual.
On your data
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The problem
Two reports not matching is one of a data team's biggest headaches. About 40% of monthly reviews surface a metric disagreement, and each manual investigation averages 2 to 3 hours. Traditional methods only catch surface-level filter differences, while the more hidden problem is at the model level: event analysis and retention analysis use different ID systems for the same set of users, causing inherent data skew.
What it does
When to use it
The operations and product dashboards show different data for the same metric
The user count in a retention report does not match an event report
Two teams' monthly reports disagree and need investigation
A newly configured report's definition is inconsistent with a historical report
Systematically investigating definition inconsistencies in a data-governance project
In the field
FAQ
How long does the investigation take?
A simple definition difference is usually located in 5 to 10 minutes; complex cases involving a model difference take 15 to 30 minutes to confirm layer by layer.
Does it only apply to AE reports?
The current version is deeply adapted to AE report logic, but the five-dimension model-definition-time-filter-grouping method applies to all analytics platforms.
Does the data get fixed automatically after the investigation?
It does not change the configuration automatically. The Skill outputs a root-cause report and a fix, and the team adjusts the report configuration accordingly.
Related Skills
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