Data Quality in 5 Steps: Apply Lean principles to meet the many challenges of customer MDM initiatives

I needed to develop a process to deliver continued improvement. The customer data began to come into our central repository, which is named Customer Data on Demand or CD2. Dun & Bradstreet assisted in data cleansing and enrichment with family alignment and SICs. D&B matches each customer record to a DUNS number from their database and assigns a confidence code. A tolerance level was set at a confidence code of eight out of 10. Lower confidence codes were put into the low confidence bucket, and zero confidence records were categorized as a nonmatch. Low confidence and nonmatched records needed to be sent back to the business group to determine if a DUNS number match could be found with improved data. Three important metrics were identified: A philosophy of getting it right the first time drives cost containment and points to the need for mechanized standards. Rework wastes the cost of employee time and the lost opportunity to work on other projects that add to growth. Using a data quality tool, data can be correct at the beginning. We chose Informatica Data Quality, which has functionality to partner with D&B to cleanse and enrich with the DUNS number as the data is created. There will always be a cleansing effort due to acquisitions, legacy system issues, etc. But after the baseline is created, tools can keep this at bay. Using a data quality tool to load data from a legacy system into the new SAP instance resulted in a 99 percent productivity improvement, allowing the team to cleanse and fix in four days what would have taken four months. We began with a 64 percent revenue alignment to DUNS number and ended with a 90 percent alignment. We now had control over our data. A true cultural shift had taken place. We had clear data ownership at the point of creation. Teams understood the power of the DUNS number, our metric for quality, which enabled family alignment, revenue recovery and credit risk management, always essential, but especially in this volatile time. We engaged visual controls, measuring completeness by identifying scrap and OTTR with scorecarding posted on the site. Information sharing provided through a centralized forum for customer data management in the centralized repository and best practice sharing through the governance council trumped the old way of process failure and decision-making by conjecture.