Imagine if you owned a restaurant, and you found out that about 10% of customer checks didn’t match up with orders placed through the kitchen. You’d quickly ask tough questions: Is someone stealing money?  Are customers being cheated? What’s causing the errors? After a quick assessment you would take quick action to correct the problem and make sure it never happens again.

Strangely, that kind of awareness of data quality doesn’t seem to scale up to large organizations.  When data management teams contact CapTech for help, they routinely recount challenges in funding data quality work. They ask for simple, direct examples showing tangible business benefit from improving data quality. 

Here are three of our favorites:

If business or IT users insist that their definition is good and everyone knows what they mean when in fact that is not the case, the strategies below may help.

1. Provide examples of unclear vs. clear definitions

Users who are intimately familiar with their business process and supporting systems may not understand the point of specifying exactly what they need. To them "the ID of the customer" is a perfectly acceptable definition of "Customer ID." Or, the IT representative may give a definition that works for them but no one else, such as "the primary key of the customer table". It will help both to see examples of what is needed in order to have a workable definition to support data warehouse population and use of the data.

How often do we find the currency and gold lying unprotected in the office cubes or corridors?  How often do we find piles of garbage in and around the office buildings? Even if we do find them occasionally, how often do we find the gist of it getting summarized, packaged and sent to most of the senior managers, along with many other goodies undetected?