For much of the 60 years or so that organizations have been managing data in electronic form, there has been an overpowering desire to subdue it through centralized planning and architectural initiatives.
These initiatives have had a variety of names over the years, including the most familiar: “information architecture,” “information engineering,” and “master data management.” Underpinning them has been a set of key attributes and beliefs:
Data needs to be centrally controlled.
Modeling is an approach to controlling data.
Abstraction is a key to successful modeling.
An organization’s information should all be defined in a common fashion.
Priority is on efficiency in information storage (a given data element should only be stored once).
Politics, ego, and other common human behaviors are irrelevant to data management (or at least not something that organizations should attempt to manage).
Each of these statements has at least a grain of truth in it, but taken together and to their full extent, I have come to believe that they simply don’t work as the foundation for data management. I rarely find business users who believe they work either, and this dissatisfaction has been brewing for a long time. For example, in the 1990s I interviewed a marketing manager at Xerox Corporation who had also spent some time in IT at the same company. He explained that the company had “tried information architecture” ...