Consumer & CPGGlobal4-8 weeks
AI-Powered Data Quality for Trusted Reporting
8 weeks
Tech Lead + 2 AI Engineers
1The Challenge
Manual data quality processes causing delayed and unreliable financial reporting across 40+ markets. The global data team spent 60% of their time firefighting DQ issues rather than delivering insights.
2Our Solution
Deployed AI agents to automate DQ rule generation and anomaly detection, integrated with existing Snowflake warehouse and dbt transformation layer.
Approach
- Profiled existing data assets to identify critical DQ dimensions
- Used LLMs to generate business-context-aware DQ rules
- Built automated anomaly detection with configurable thresholds
- Established ownership model with clear escalation paths
3Results & Impact
40% reduction in data defects
60% faster issue resolution
£2M+ in avoided reporting errors
Team shifted from reactive to proactive
Key Takeaways
- AI accelerates rule generation but human review is essential
- Start with high-value, high-visibility data domains
- Ownership model is as important as technical implementation
Technology Stack
SnowflakePythondbtCustom AI Agents
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