The Real Cost of Bad Data

Somewhere in a data warehouse, a customer record sits incomplete. A postcode field contains only the first half of its expected value. An email address lacks its domain. A timestamp references a date that never existed. These fragments of broken data might seem trivial in isolation, but multiply them across millions of records and the consequences become staggering. According to Gartner research, poor data quality costs organisations an average of $12.9 million annually, whilst MIT Sloan Management Review research with Cork University Business School found that companies lose 15 to 25 percent of revenue each year due to data quality failures. The challenge facing modern enterprises is not merely detecting these imperfections but deciding what to do about them. Should a machine learning algorithm guess at the missing values? Should a rule-based system fill gaps using statistical averages? Or should a human being review each problematic record individually? The answer, as it turns out, d