Dashboard Design Brief
Write a complete brief for a data dashboard — what to show, how to organize it, and what decisions it should drive.
Generate a tailored data cleaning checklist for your dataset — every issue to check before you start analyzing.
Generate a data cleaning checklist for my dataset before I begin analysis. **What my dataset contains:** [Describe the data — what it represents, how many rows/columns, time period, how it was collected] **Data source:** [e.g. CRM export, database dump, survey platform, web scrape, manual entry, API pull] **Format:** [e.g. CSV, Excel, SQL table, JSON, Parquet] **Known issues I'm already aware of:** [Any data quality problems you've already spotted] **Analysis I'm planning:** [What you'll do with this data — affects what cleaning matters most] **Tool I'm using:** [e.g. Python/pandas, R, Excel, SQL, dbt] Generate a comprehensive data cleaning checklist organized by category: **Structure checks:** - Column names, data types, encoding issues **Completeness checks:** - Missing values, nulls, empty strings — and what to do with each **Validity checks:** - Out-of-range values, impossible dates, format inconsistencies **Consistency checks:** - Duplicates, conflicting values, join key integrity **Relevance checks:** - Columns to drop, rows to filter, time period to scope For each check: what to look for, how to detect it in my tool, and the recommended action (remove / impute / flag / investigate).
Systematically cleaning a dataset before analysis to catch quality issues that could corrupt results or mislead conclusions.
A categorized data cleaning checklist covering structure, completeness, validity, consistency, and relevance — with detection methods and recommended actions for each issue.
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Claude Sonnet 4
Write a complete brief for a data dashboard — what to show, how to organize it, and what decisions it should drive.
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