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IntermediateSITUATIONAL
Suppose after deploying your Petrol Pump Fuel Transaction Analysis reports, the payment_mode breakdown shows an unexpected spike in a particular mode. What steps would you take using your database and reporting tools to investigate whether this is a data quality issue, fraud, or a real business change?
data analyst
General

Sample Answer

When I saw a 25% jump in mobile-wallet payments on the petrol-report I owned, my first step was to quantify scope: I compared the last 7 and 30 days to the prior period and saw the spike equaled roughly $120K extra daily. I ran SQL checks on raw transactions for duplicates, missing IDs, and unusually high volumes per pump or card—finding a cluster of identical transaction_ids from a single terminal. I then cross-joined terminal logs and POS firmware versions to rule out a reporting bug; that pointed to a misconfigured gateway retry causing duplicate authorizations. I alerted ops and fraud, rolled back the aggregated dashboard for two hours, and worked with the payments team to patch the gateway. Within 48 hours we removed the duplicates and the spike normalized, saving an estimated $350K in potential reconciliation costs.

Keywords

Quantify the anomaly with time-window comparisons and dollar impactQuery raw transactional data for duplicates, nulls, and clustering by terminal or merchantCorrelate with system logs and involve ops/fraud quicklyMitigate reporting exposure (temporary rollback) and track resolution metrics
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