A data analyst is reviewing a quarterly sales dashboard and notices that the total revenue for the 'East' region is 50% lower than the previous quarter, which is highly unusual. Stakeholders have confirmed this does not align with their operational activities. The dashboard's data is sourced from a production sales database. The analyst suspects an issue with the data being pulled for the report. What is the most effective initial step to validate the integrity of the source data?
Review the dashboard's interactive filters to ensure the analyst has not accidentally excluded a key product category.
Submit the dashboard's underlying code and calculations for a peer review to identify logical errors.
Run a separate, simple query directly against the production sales database to sum the revenue for the 'East' region and compare it to the dashboard's figure.
Check the report server's monitoring alerts to determine if a slow refresh rate is causing stale data to be displayed.
The correct answer is to execute a separate, simple query directly against the source database to calculate the total revenue for the specified region and period. This is a core technique of source validation. It involves comparing the data displayed in the report or dashboard directly against the original data source to confirm accuracy and identify discrepancies. By running a simplified, targeted query on the production database, the analyst can quickly determine if the issue lies with the source data itself (e.g., missing records, incorrect entries) or if the problem is in the dashboard's more complex query, filters, or calculation logic.
Reviewing the dashboard's internal filter logic or submitting the report's code for peer review are subsequent steps, best taken after confirming the source data is correct. Checking for slow refresh rates is a performance troubleshooting technique and is irrelevant to solving a data accuracy problem.
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