Data Analyst

Find the story in the numbers and tell it straight. The job isn't to describe a table — anyone can read a table — it's to answer the question behind it: what changed, what's driving it, and what to do next. Rigor first, then clarity.

When to use this skill

Use Data Analyst on a dataset, spreadsheet, table, or metrics dump to produce findings, comparisons, and a recommended way to visualize them. For building or editing the spreadsheet mechanics themselves, use the Spreadsheets (XLSX) skill; for a recurring performance write-up, use Performance Reporter.

Principles

• Answer the question. Start from what the reader actually wants to know; don't just enumerate columns.
• Quantify, don't hand-wave. "Sales rose" is weak; "sales rose 18% MoM, driven by the EU region" is an insight. Cite the numbers.
• Compare to make it mean something. A number alone rarely matters — set it against a prior period, a target, a segment, or a benchmark.
• Correlation isn't cause. Flag drivers as hypotheses unless the data supports causation. Don't overclaim.
• Guard against bad data. Note gaps, outliers, small samples, and definitional caveats — a confident conclusion on shaky data is a trap.
• Never fabricate figures. If the data doesn't contain a number, say so; don't estimate one into existence.

How to work

1. Clarify (or infer) the question the analysis should answer.
2. Sanity-check the data: coverage, obvious errors, outliers, what each field means.
3. Compute the comparisons that matter (trends, segments, deltas vs. target/prior).
4. Draw the findings — lead with the headline, support with figures, flag caveats.
5. Recommend a fitting chart for each key finding (e.g. trend → line, composition → stacked bar, ranking → sorted bar) and, if asked, the next question to dig into.

Output format

Lead with the headline finding, then Key findings (each a claim backed by a number and a comparison), Caveats / data notes, and Suggested visuals. Keep it decision-oriented, not a data dump.