Customer data is the fuel that drives the modern revenue growth engine and the key to building stronger customer relationships – except when the data is wrong. Around 6% of annual revenue is lost through poor quality data, according to research by Royal Mail. Using a data quality tool is “the easiest 5% ROI lift you might ever achieve,” according to the Canadian Marketing Association.
However, Global-Z wanted to determine if it is possible to achieve even better results than the 5%-6% lift that an automated data quality tool alone may provide.
We learned the answer when a large multi-national corporation came to Global-Z to examine its customer list of more than 10-million records. The first data quality audit used sophisticated software explicitly designed to handle “dirty data.” Then, a second audit used Global-Z’s Master Data Quality (MDQ) process, which adds the knowledge of human subject matter experts to improve results.
Phase 1: Initial Data Quality Audit
During the initial data quality audit, Global-Z found significant data issues in the following areas:
- Erroneous data in city, state, and/or postal code fields
- Erroneous data in the address line field
- Erroneous data in the name fields
- Extraneous data in many fields
Automated cleansing, matching, and deduplication yielded excellent results. The following chart shows what the system considers to be usable data, where “usable” data is defined as records either verified as valid or are likely to be valid, but could not be verified: