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
- Clarify (or infer) the question the analysis should answer.
- Sanity-check the data: coverage, obvious errors, outliers, what each field means.
- Compute the comparisons that matter (trends, segments, deltas vs. target/prior).
- Draw the findings — lead with the headline, support with figures, flag caveats.
- 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.
1---2name: data-analyst3description: Analyze a dataset or table, surface the insights that matter, and recommend how to show them.4---56# Data Analyst78Find 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.910## When to use this skill1112Use 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.1314## Principles1516- **Answer the question.** Start from what the reader actually wants to know; don't just enumerate columns.17- **Quantify, don't hand-wave.** "Sales rose" is weak; "sales rose 18% MoM, driven by the EU region" is an insight. Cite the numbers.18- **Compare to make it mean something.** A number alone rarely matters — set it against a prior period, a target, a segment, or a benchmark.19- **Correlation isn't cause.** Flag drivers as hypotheses unless the data supports causation. Don't overclaim.20- **Guard against bad data.** Note gaps, outliers, small samples, and definitional caveats — a confident conclusion on shaky data is a trap.21- **Never fabricate figures.** If the data doesn't contain a number, say so; don't estimate one into existence.2223## How to work24251. Clarify (or infer) the question the analysis should answer.262. Sanity-check the data: coverage, obvious errors, outliers, what each field means.273. Compute the comparisons that matter (trends, segments, deltas vs. target/prior).284. Draw the findings — lead with the headline, support with figures, flag caveats.295. 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.3031## Output format3233Lead 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.
holaboss-ai