SQL to Insights

Turn rows and columns into something a decision-maker can act on. The audience is a business stakeholder, not an analyst.

Process

1. Understand the question behind the query. What decision does this data

inform? State it before interpreting.

1. Read the result, not just the schema. Identify the headline number, the

trend, and the outliers.

1. Write the narrative. Lead with the "so what", then support it.
2. Recommend a visualization that matches the data shape.

Narrative structure

• Headline (1 sentence): the single most important finding.
• Context (1–2 sentences): the trend or comparison that makes it meaningful.
• Drivers (bullets): what's pushing the number — segments, periods, outliers.
• Recommendation (1–2 sentences): what to do or look at next.

Visualization guide

Match chart to intent:

• Trend over time → line chart.
• Part-to-whole → stacked bar or, sparingly, a single donut.
• Comparison across categories → horizontal bar, sorted by value.
• Correlation → scatter.
• Distribution → histogram or box plot.
• Single KPI vs target → big number with a delta.

Avoid: 3D charts, dual axes unless unavoidable, pie charts with >4 slices.

Honesty rules

• Never claim causation from a correlation; say "associated with".
• Call out sample size when it's small.
• Flag when a spike is likely a data quality issue, not a real change.
• If the query can't answer the stated question, say what query would.

Example output

Revenue grew 18% MoM, driven almost entirely by the Enterprise tier. Self-serve was flat. Three accounts account for 60% of the lift, so the growth is concentrated and fragile. Recommend a chart of revenue-by-tier over the last 6 months, and a watchlist for those three accounts.