Over the years, we have all seen data rationalization and data quality improvement projects either fail outright or take significantly longer than planned. Similarly, I have seen many projects designed to integrate customer and vendor databases across functions or divisions, implementations of master data management (MDM)/customer data integration (CDI) solutions and even simple efforts to clean and maintain a single division’s customer data frequently fail to deliver the data required by end users because of data quality problems. Perhaps the most dramatic failures I have witnessed were initiatives to identify the company’s 25 largest customers for the CEO. (I have watched this action film three times with different companies in the lead role. They were spectacles because of the high visibility of the project.)

There are lots of reasons why these projects take so long to deliver results, but there is a common thread in all the examples I have witnessed. The underlying problem has been attacking the problem from the wrong end of the transformation chain. What do I mean by transformation chain? All systems that transform raw data into information that can be used for analysis or insight go through a set of common steps. (In this column, I’ll use information about customers and vendors, a.k.a. business entities, for illustrative purposes.) The steps are:

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access