This repository contains curated patterns and examples focused on building trust in analytics and decision-making systems through reliable data, validation controls, and quality frameworks.
- Data quality rules and validation logic
- Consistency and reconciliation of metrics
- Monitoring and control mechanisms
- Foundations for trusted decision support
Analytics outputs are only valuable when the underlying data can be trusted. Poor data quality increases risk, reduces confidence, and leads to incorrect decisions. This repository focuses on practical approaches to ensure reliability and transparency in analytics systems.
Each example is organized as a self-contained project under /projects.
Start with the README inside each project folder to understand the quality
controls, assumptions, and impact on decision-making.