AI Assistants for Analysts: Using LLMs to Speed Up SQL, EDA, and Reporting

Analysts spend 80% time on data plumbing. LLM‑powered assistants now generate SQL, prototype EDA, draft reports — 10x productivity if used correctly. Here’s the 2026 state‑of‑play.​

What AI assistants can do well

1. SQL generation

Ask: “Daily active users by country last 90 days”

Get: SELECT country, COUNT(DISTINCT user_id)…

1. EDA acceleration

• Suggest next plots (“correlation heatmap?”).
• Surface outliers/anomalies.
• Generate summary stats + insights.​

1. Reporting – Narrative summaries, anomaly explanations, slide outlines.

Leading tools and patterns

1. BI‑native:

• ThoughtSpot, Hex: NLQ → charts.
• Tableau Ask Data, Looker Copilot.

1. Code‑first:

• Cursor/GitHub Copilot: EDA notebooks.
• Continue.dev: LLM in your IDE.

1. Warehouse‑native:

• BigQuery Gemini, Snowflake Copilot.​

Production patterns

1. Human + AI workflow:

• NL query → SQL → human review/execute.
• Auto‑generated insights → analyst validates context.
• Prompt library for common patterns.

1. Prompting tips:

“Write SQL for [metric] by [dimension] for [time period]. Use CTEs. Add comments.”

Risks and guardrails

1. Pitfalls:

• Hallucinated SQL syntax.
• Wrong business logic/joins.
• No lineage or testing.​

1. Fixes:

• Ground in schema/docs (contextual RAG).
• Unit test generated SQL.
• Human approval loop.
• Feedback loops improve over time.

Try this: Use an LLM to write SQL for your most common report. Copy‑paste to validate. Tweak prompt based on misses.

Deepak Nagpal