SQL Data Analyst Pro

SQL Data Analyst Pro: practical plan

Definition checklist

Before querying, define “net revenue”: gross item sales minus discounts, refunds, cancellations and taxes? Confirm currency, order status, timezone, recognition date and treatment of shipping. Align with finance reporting.

Example query pattern

SELECT
  DATE_TRUNC('month', order_created_at) AS month,
  SUM(item_subtotal - COALESCE(discount_amount, 0)
      - COALESCE(refund_amount, 0)) AS net_item_revenue
FROM orders
WHERE status IN ('paid', 'partially_refunded')
GROUP BY 1
ORDER BY 1;

This is illustrative; actual field names, status logic and refund timing must be checked. Avoid counting canceled orders or double-counting line-level refunds.

Validation

Reconcile totals to an authoritative finance report for a closed month. Check nulls, duplicate order IDs, timezone boundaries and late-arriving refunds.

Deliverable

Report the metric definition, query, data window, exclusions, reconciliation difference and known limitations. Use a parameterized query and read-only access. Never infer causality from a trend alone.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.