Customer Data Integration: Reaching a Single Version of the Truth

CHAPTER 4

“Our Data Sucks!”: The (Not So Little) Secret about Bad Data

The term bad data has lots of implications. It can mean inaccurate data, like the wrong account number. It can mean data that’s simply missing in a record, was never entered, or is unknown. It can represent data that was made up or derived, by either a system or a human being. It can imply data that’s inconsistent across systems. It can even mean meaningless data that serves no valid business purpose.

Since the goal of customer data integration (CDI) is to generate authoritative customer data, the implications of bad data can be staggering. From regulatory compliance to reconciliation of company hierarchies to “person of interest” recognition, the business need for CDI implies the need for accurate data. Fixing bad data is not just a technological challenge. It requires rigor around requirements, clear organizational responsibilities, and a cultural awareness that continuous improvement begets data refinement over time.

When data quality hits the business’s radar, it’s usually at the business’s expense. The customer notices a billing error on an invoice—for the third month in a row. Or a finance executive knows that the top-line financial reports are wrong but can’t put his finger on why. Or the regulatory agencies come calling, and there’s contradictory data. The business implications of lost customers, miscommunications to prospects and to Wall Street, and inability to comply with new regulatory legislation, ...