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