Analysis, SQL, dashboards, metrics and data quality. 20 reviewed prompts for data and analytics.
Data work mixes analysis with explaining it. The prompts and skills here help you describe a dataset, draft SQL, check data quality, choose metrics and write up what a chart shows for people who are not analysts. Share the schema or a small sample instead of sensitive records, and verify results against your own numbers before they go into a report.
Explains what a SQL query does in plain language, including the business question, data used, filters, and risks, with a short summary for executives. Use it when you need to review or hand off a query without reading code.
Reviews how values are missing in your dataset, recommends a handling method per column with reasons, and gives checks to confirm nothing was distorted. Use before analysis or modelling.
Designs a practical data governance setup for an organisation: roles, decision rights, first policies, data quality rules, and a phased rollout. Use when data ownership is unclear or regulation and AI projects demand more control.
Analyzes a pasted dataset or data summary to surface patterns, outliers, likely drivers and data quality issues, then recommends specific charts and next actions. Use when you have a table and need to know what it says.
Turns a business question into a precise metric definition and a pipeline spec with sources, grain, transformations, tests and monitoring. Use before building a dashboard metric or a new data model.
Defines what a management dashboard should show: decisions it supports, metrics with definitions, data sources, layout and refresh rules. Use before building or commissioning a dashboard.
Traces where a table or column comes from and what depends on it across SQL scripts and stored procedures, and shows the impact of a change. Use before altering a table or when documenting a data flow.
Turns a table of weekly figures into a short slide outline with headline findings, chart suggestions and speaker notes. Use it for recurring status or performance updates.
Plans and interprets an analysis of product usage data to find why users stay or leave, ending in prioritized, testable recommendations. Use when engagement or retention is flat or falling.
Writes readable, correct SQL for your schema and dialect, or diagnoses a slow query from its plan and proposes specific fixes. Use it for reports, data pulls and query performance problems.
Turns a business question and a description of your data into a step-by-step analysis plan covering sources, cleaning, methods, checks and how to present findings. Use before starting an analysis.
Recommends a suitable statistical approach for a question and dataset, explains it in plain language, lists assumptions to check and how to read the result. Use before analyzing survey, test or experiment data.
