Turns SQL query results into a decision-ready business narrative - headline finding, drivers, recommendation - plus the right chart choice for the data shape. Use when someone asks "what does this data actually mean", "summarize these query results for the exec team", "what chart should I use for this", or pastes a result set and wants the so-what for a non-technical audience. Do NOT use to diagnose or speed up the query itself - use sql-query-optimizer instead; do NOT use to build a full multi-part narrative presentation around an analysis - use data-story instead; do NOT use for open-ended exploration of an unfamiliar dataset - use eda-playbook instead.
Click to play with sound.
---
name: SQL to Insights
description: Turns SQL query results into a decision-ready business narrative - headline finding, drivers, recommendation - plus the right chart choice for the data shape. Use when someone asks "what does this data actually mean", "summarize these query results for the exec team", "what chart should I use for this", or pastes a result set and wants the so-what for a non-technical audience. Do NOT use to diagnose or speed up the query itself - use sql-query-optimizer instead; do NOT use to build a full multi-part narrative presentation around an analysis - use data-story instead; do NOT use for open-ended exploration of an unfamiliar dataset - use eda-playbook instead.
---
# SQL to Insights
Turn rows and columns into something a decision-maker can act on. The costly failure mode is a "summary" that restates the numbers the stakeholder can already see, buries the one finding that matters under six that do not, or implies causation the data cannot support - all of which erode trust in every future readout.
## Operating procedure
Work the steps in order: the decision question must be stated before interpretation, and the honesty screens must run before the narrative is written, because a caveat discovered after the headline ships is a retraction.
### Step 1: Gather inputs
Collect these before interpreting anything. Label anything assumed as a guess.
1. The result set (and ideally the query), including row count and date range.
2. The audience: exec, functional lead, or analyst peer. Default: business stakeholder with no SQL.
3. The decision this data informs. If the requester cannot name one, propose the most likely one and label it a guess.
4. The comparison baseline: prior period, budget/target, cohort, or benchmark. A number with no baseline is not a finding. Default: prior period.
5. Known data caveats: incomplete current period, recent tracking changes, backfills.
### Step 2: State the question behind the query
… install to load the full skill