Data Quality Improvement Methodology
Our Data Quality Process Improvement methodology is illustrated below and provides client’s with an end-to-end, robust service solution for their current and future data quality needs:
The Data Quality Improvement Process Steps are defined as follows:
- Discovery of Data Environment – Gather, compile and analyze information about the current situation and data environment.
- Assess Data Quality Process Maturity – Obtain consensus, define and document the current and desired maturity levels by dimension with associated Action Plan that will be the foundation for all work done throughout the project.
- Audit Data Quality – Identify data quality defects within the data environment.
- Assess Business Impact – Determine and prioritize the impact of data quality defects on the business.
- Identify Root Causes – Identify the true causes of the data quality defects and develop specific recommendations for addressing the defects.
- Develop Improvement Plan – Develop and execute improvement recommendations.
- Prevent Future Data Defects – Implement solutions that address the root causes of the data quality defects.
- Correct Current Data Defects – Implement steps to correct the data quality defects.
- Implement Controls – Monitor and verify the improvements that were implemented.
- Communicate Actions and Results – Continuously document and communicate the results of data quality issues identified and improvements made during each process step.