Data Quality & Governance

Data quality and governance encompass the practices, processes, and technologies that ensure your data is accurate, consistent, complete, and trustworthy. In an era where decisions are increasingly data-driven, poor data quality can lead to flawed insights and costly mistakes.

Data quality involves profiling data to understand its characteristics, validating data against business rules, monitoring for anomalies, and alerting when issues arise. It's about building confidence that the data being used is correct and reliable.

Data governance extends beyond quality to include data ownership, access controls, privacy compliance, and lineage tracking. It answers questions like: Where did this data come from? Who can access it? How has it changed over time? These capabilities are essential for regulatory compliance and operational trust.

Manual data quality checks don't scale. As data volumes grow and pipelines multiply, you need automated systems that validate data continuously and alert on issues in real-time. We build quality frameworks that operate at the speed of your data.

We implement data quality as code using tools like Great Expectations, integrating validation directly into your data pipelines. This means quality checks run automatically on every data load, catching issues before they propagate to downstream systems.

For governance, we implement metadata management platforms like DataHub or Apache Atlas that provide comprehensive cataloging, lineage visualization, and access controls. This gives you visibility into your entire data ecosystem and the controls needed for compliance.