Practical Software Measurement

Taking Responsibility for Quality Data

Thomas C. Redman recently wrote about data quality on the Harvard Business Review blog.  In his post, he creates a vignette of an executive who finds an error in data provided by the "Widgets Department" for an important meeting. The executive corrects the error, the meeting is a huge success, and the story ends there. Redman argues that someone should have gone back to the Widgets Department to report the error, not to complain that the error could have ruined the presentation, but rather that it could ruin the next person's presentation.

The hardest part about database validation is not reviewing every individual project, but rather, determining if the information on each tab is correct. Sometimes, it's easy to tell that the organization name is spelled incorrectly, other times, it's difficult to discern if a labor rate is incorrect. Having a well-documented database is important, not just for your own use, but for whatever you plan on using it for next.  For example, if you plan on making custom trend lines, but you recorded that it took you 31 man months instead of 3.1 man months, that would have a disastrous effect on your trends! It's obvious that the error would need to be recorded, but it's also important to report the error to whoever prepared the data so that they can check the rest of the projects in the database for the same error. 

Redman suggests creating an office culture which promotes the following three points:

  1. Data creators create data correctly, the first time, with full understanding of what that means to customers, those who use data they create.
  2. Data customers must communicate their data requirements to sources of data, and they provide feedback when data are wrong.
  3. Virtually everyone recognizes they are at once data creators and data customers.

Redman acknowledges that following these steps would increase time spent documenting and editing data, but the reward is quality data that can be trusted. 

Although points one and two are important, point three is especially critical: we are all data creators and data customers. Because we are all creators and consumers, we are all capable of creating quality data and we are all burned by mistakes in data. As mentioned in my previous blog post on data collection, you can use the Validation feature in SLIM-DataManager to point out potential mistakes in your historical database and help promote quality data in your organization.

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