Standardization of data and data quality measurement
Objective
A large US-based bank wanted to standardize data processes, implementing and monitoring control check points to measure data quality for large scale migration of legacy risk management systems into a strategic system.
Challenges
- Lack of trust in data due to poor data quality and lack of data quality reporting.
- Siloed risk management in outdated legacy systems.
Approach
- Created an end-to-end control framework for data quality management.
- Established a scalable and standardized reporting framework and automated across 3 stages leveraging Python.
- Incorporated Machine Learning matching algorithms (fuzzy logic) using distance and ratio methodology to automate data quality checks.
Impact
- Increased data quality and built a reproducible framework for measuring and reporting DQ for future use.
- Overall automation resulted in a reduction of ~ 85% turn around time.
Questions