A data analyst is investigating a sales dashboard where the "Average Order Value" (AOV) metric appears significantly lower than historical averages. The analyst has already confirmed the source data is accurate and the report is refreshing correctly. The current AOV formula in the BI tool is SUM(Sales_Total) / COUNT(Order_ID). The analyst suspects the logic does not correctly account for returned orders, which are recorded as separate transactions with negative Sales_Total values. Which of the following is the most direct troubleshooting step to perform a calculation review?
Apply a global filter to the dashboard to exclude all transactions with negative 'Sales_Total' values.
Rerun the ETL job that loads data from the transactional database into the data warehouse to ensure data integrity.
Manually calculate the AOV for a small, targeted sample of orders that includes both sales and returns, and then compare the result against the value shown in the report.
Submit the report's source code and data model to another analyst for a peer review.
The correct answer is to manually calculate the metric for a small, representative data sample and compare it to the report's output. This is the most direct way to perform a calculation review ('Review > Calc'), as it isolates the formula and its logic to verify its correctness against a known outcome. Peer review is a valid technique but is less direct than validating the calculation oneself. Filtering the data to exclude returns would change the underlying data and prevent the analyst from fixing the actual calculation logic. Rerunning an ETL job is a source validation technique, but the scenario states the source data has already been confirmed as accurate.
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What impact do negative 'Sales_Total' values have on AOV?
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