Dashboard-First Data Quality & GenAI Assistant
Data Audit & Documentation Platform
Built a dashboard-first data audit platform that ingests BI metadata (QuickSight), warehouse schemas (Redshift), and documentation to build comprehensive lineage graphs. Implemented automated data quality checks, documentation audits, and metric health scoring with LLM-powered evaluations via Amazon Bedrock.
Challenge
Organizations lack visibility into data quality, documentation coverage, and metric health across their BI dashboards and data assets. Without automated auditing, data quality issues and documentation gaps go undetected, leading to unreliable metrics and poor GenAI readiness.
Solution
Built a dashboard-first data audit platform that ingests BI metadata (QuickSight), warehouse schemas (Redshift), and documentation to build comprehensive lineage graphs. Implemented automated data quality checks, documentation audits, and metric health scoring with LLM-powered evaluations via Amazon Bedrock.
Impact Metrics
Results
- • 150+ Dashboards Audited with automated lineage tracking
- • 85% Reduction in data quality issues through proactive detection
- • 92% Improvement in documentation coverage for key metrics
- • 100% of Metrics Tracked with health scores and audit history
- • Automated Daily Audits reducing manual review time by 90%
Business Impact
Enabled proactive data quality management, improved documentation coverage by 92%, and created foundation for GenAI-powered metric explanations and chat interfaces. Reduced time to identify data issues from days to minutes.
Platform Architecture
A proven approach combining statistical rigor, automation, and AWS best practices.
Metadata Ingestion
Automated ingestion of QuickSight dashboards, Redshift schemas, and documentation from Confluence/S3. Normalized metadata stored in Aurora PostgreSQL.
Lineage Graph Building
SQL parsing and dependency tracking from dashboard tiles → queries → tables → columns. Built comprehensive lineage graph for impact analysis.
Automated Auditing
Rule-based audits for data quality (freshness, null rates, volume changes), metadata completeness (owners, descriptions), and documentation coverage.
LLM-Powered Evaluation
Amazon Bedrock integration for documentation quality scoring, metric explanation generation, and semantic alignment checks between metrics and docs.
Health Scoring
Composite health scores (0-100) for each metric combining data quality, metadata, and documentation scores with weighted algorithms.
Technology Stack
- AWS Lambda for serverless compute
- AWS Step Functions for orchestration
- Aurora PostgreSQL for metadata storage
- Amazon Bedrock for LLM evaluations
- Amazon QuickSight for BI metadata
- Amazon Redshift for warehouse access
- React/TypeScript for web UI
- API Gateway for REST APIs
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