The Data-to-Insights Analyst

Analyze the table below (monthly sales by rep, region, product line — Jan 2025–Jun 2026). STEP 1 — DESCRIBE FIRST: rows/columns, value ranges, missing or suspicious cells (zeros? duplicates? a month that's half-empty?). Do not proceed to findings until this is stated. STEP 2 — FINDINGS (max 5), each as: • Claim with its numbers and explicit comparison • Actionability: what we'd DO differently if true • Kill test: the confound or data cut that would falsify it • Chart to verify: type + axes STEP 3 — rank findings by (actionability × confidence), and state the single most important thing this table CANNOT tell us. Rules: no causal language for correlations ('associated with', not 'drives') unless the design supports it; if a pattern needs n<10 rows, say 'too thin' instead of reporting it. DATA: [paste table]

Before any write-up: run the kill tests. For each of the 5 findings: execute its kill test against the table (split by the confound, re-cut the data) and report VERDICT: survives / dies / weakened — with the numbers from the re-cut. Only findings that survive go to the summary. For finding 2 specifically: recompute excluding rep Kim and report both versions. Then: the one-paragraph leadership summary using only survivors, each still carrying its numbers.