Standardization of data and data quality measurement
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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Siloed risk management in outdated legacy systems.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.
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