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:

  1. Discovery of Data Environment – Gather, compile and analyze information about the current situation and data environment.
  2. 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.
  3. Audit Data Quality – Identify data quality defects within the data environment.
  4. Assess Business Impact – Determine and prioritize the impact of data quality defects on the business.
  5. Identify Root Causes – Identify the true causes of the data quality defects and develop specific recommendations for addressing the defects.
  6. Develop Improvement Plan – Develop and execute improvement recommendations.
  7. Prevent Future Data Defects – Implement solutions that address the root causes of the data quality defects.
  8. Correct Current Data Defects – Implement steps to correct the data quality defects.
  9. Implement Controls – Monitor and verify the improvements that were implemented.
  10. Communicate Actions and Results – Continuously document and communicate the results of data quality issues identified and improvements made during each process step.