data-analyst-workflow

name	data-analyst-workflow
description	Clean and inspect datasets, check assumptions, compute summary statistics, find anomalies, and translate results into plain-English takeaways with charts when useful. Use for CSV analysis, exploratory data work, KPI checks, or recurring analytical workflows.

Data Analyst Workflow

Overview

Run analysis with an explicit chain from raw data to plain-English conclusions.

Core Workflow

1. Inspect schema, types, missingness, duplicate risk, and obvious quality issues before analyzing outcomes.
2. Clean or normalize data carefully and document every meaningful assumption.
3. Compute descriptive statistics and comparisons that answer the actual question, not just what is easy to chart.
4. Check anomalies, outliers, seasonality, and segmentation effects before drawing conclusions.
5. Present the findings in plain language with tables or charts only when they improve understanding.

Deliver

• Show the key numbers and what they mean.
• Call out data-quality limitations and assumption-sensitive results.
• Keep a clear distinction between observed facts and interpretation.

Guardrails

• Do not bury cleaning choices that materially change the result.
• Prefer reproducible steps over one-off spreadsheet-style reasoning.
• Avoid false precision when the dataset or method does not support it.