Act as a practical specialist helping me with data quality checklist. My context: [dataset; source; key fields; known issues; report using it].
1. Check row counts against the source.
2. Look for nulls, duplicates and odd values.
3. Validate totals against a known figure.
4. Document fixes and assumptions.
5. Add automated checks for the top issues.
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
#data & analytics#data quality
💡 Replace the [brackets] with your details — the more specific, the sharper the answer.
One metric that matters, and everything feeding it.
Act as a clear-headed analytics lead who insists decisions come first. The task is designing a KPI tree for my team or product. Context: [the north star or goal, the levers I believe drive it, team responsibilities, current metrics].
1. Break the north star into input metrics two levels deep.
2. Assign an owner and cadence to every input metric.
3. Flag metrics that are easy to game and add a guardrail metric.
4. Define what we stop measuring once this tree exists.
Make it specific to my context and immediately usable. Do not invent current prices, laws, permissions or other missing facts; label what needs checking.
Act as a clear-headed analytics lead who insists decisions come first. The task is writing a requirements brief for a new dashboard. Context: [decisions this dashboard must support, audience, data sources available, refresh needs].
1. List the five questions the dashboard must answer in order.
2. Sketch each panel: question, metric, dimension, time grain.
3. Define what "good" and "bad" look like so color means something.
4. Name what this replaces so we do not add dashboard number twelve.
Make it specific to my context and immediately usable. Do not invent current prices, laws, permissions or other missing facts; label what needs checking.
Act as a clear-headed analytics lead who insists decisions come first. The task is translating a business question into a data specification. Context: [the business question, tables or fields I know exist, time range, comparison needed].
1. Restate the question as a measurable definition with edge cases called out.
2. Specify filters, cohorts and the time window explicitly.
3. List the ambiguities the analyst will hit and my answer to each.
4. Write the sanity check that proves the result is right.
Make it specific to my context and immediately usable. Do not invent current prices, laws, permissions or other missing facts; label what needs checking.