Since data quality has such a strong tendency to go unnoticed, it is even more important to translate the consequences of poor-quality data to the one dimension each and every manager understands so well: dollars. This also gives a perspective on the kinds of investments that are appropriate to make in order to resolve such issues. Also, a mechanism for prioritizing improvement programs is desirable. You want to begin picking the low-hanging fruit first, but you certainly also want to know where the whoppers are! According to Gartner, Fortune 1000 enterprises may lose more money in operational inefficiency due to data quality issues than they spend on Data Warehouse and CRM initiatives.

4. Data quality issues typically arise when existing data are used in new ways

In my experience as a data miner, where I am very often looking for new ways of using existing data, this is where many problems originate. The data itself hasn't changed, but it are new uses for existing data that make problems apparent that were already there. So what constitutes "data quality" needs be considered in relation to its intended use. And change of usage then brings up new ways to evaluate the quality and hence may bring up concerns. The reason these problems didn't surface before is usually because the business adapted to the data, the way they are. People and processes avoided the consequences of inaccurate entries. Which incidentally, is also why legacy system migrations can be so painful.

5. Many CRM projects collapse under data quality issues

Gartner and Forrester have estimated that 60-70% of CRM implementations fail to deliver on expectations. That is not to say that these projects are all abandoned halfway; it's foremost that expectation aren't met. One of the biggest reasons for the 'technical' challenges in bring CRM projects to completion is that disparate data sources are getting merged to create a 360° customer view. Often, this is the first time that customer records of disparate systems are merged. There is typically tremendous "fallout", and records that do get merged contain many inconsistencies. This then often leads to disappointed end-users, and unmet expectations.

6. Data quality is a management issue, not a technology issue

The typical situation in the overwhelming majority of organizations I have visited is like this:

• there is low awareness of the embedded cost of their data quality issues
• management has no idea of the potential value in fixing data quality issues "upstream"
• those who have insight in data quality issues have little or no incentive in bringing these issues out