Data Analyst Agent

Problem

Data teams spend 60% of their time on recurring reports and ad-hoc queries rather than strategic analysis. Manual dashboard compilation introduces delays and human error, while stakeholders wait days for insights that should be available in minutes.

Solution

Build an agent that connects to your data warehouse, translates natural language questions into SQL, runs anomaly detection on key metrics, and generates formatted executive summaries on a schedule.

Implementation Steps

1. Map data sources and schema

   Document your database schema, key tables, and relationships. Create a metadata file the LLM can reference for accurate SQL generation.

   Tip: Include column descriptions and sample values in your schema doc — this dramatically improves SQL generation accuracy.

2. Build natural language to SQL pipeline

   Create a chain that converts business questions into validated SQL queries with guardrails against destructive operations.

   Tip: Define a measurable success metric and review weekly to improve quality and cost.

   # Text-to-SQL with safety guardrails
   SAFE_PREFIXES = ['SELECT', 'WITH']
   def validate_query(sql: str) -> bool:
       return sql.strip().upper().startswith(tuple(SAFE_PREFIXES))

3. Define metric library and KPIs

   Map business KPIs to query templates with units, expected ranges, and comparison periods for automated reporting.

4. Add anomaly detection

   Flag metrics that deviate beyond configurable thresholds from historical baselines. Route alerts to stakeholders via Slack or email.

5. Generate formatted summaries

   Produce concise daily or weekly analysis cards with charts, trends, and plain-language interpretations for executive audiences.

Recommended combos

FAQs