Thomas Haslam

In highly regulated industries like financial services, maintaining data quality is an ongoing challenge. Reactive measures often fail to prevent regulatory penalties, causing inaccuracies in reporting and inefficiencies due to poor data visibility. Regulators closely examine the origins and accuracy of reporting calculations to ensure compliance. A robust system for data quality and lineage is crucial. Organizations are utilizing Databricks to proactively improve data quality through rules-based and AI/ML-driven methods. This fosters complete visibility across IT, data management, and business operations, facilitating rapid issue resolution and continuous data quality enhancement. The outcome is quicker, more accurate, transparent financial reporting. We will detail a framework for data observability and offer practical examples of implementing quality checks throughout the data lifecycle, specifically focusing on creating data pipelines for regulatory reporting,