Enterprise data modeling (EDM) got a bad reputation in the late 1980’s, and for good reason. It took too long to model the entire enterprise, and the model had little or no effect on how systems were built. It was perceived as a wasted effort, and the practice was abandoned by most companies. However, in the late 1990’s, it became clear that we had thrown the baby (sound data management principles) out with the bathwater (tedious and ineffective approaches). As a result, new disciplines started to emerge, such as metadata management, data governance, data stewardship, master data management, enterprise information management, information center of excellence, and so on.

Data Governance is the practice of managing information to identify and improve its business value. Data governance provides a practical methodology for data with business priorities. Although meta data is not new, its importance to effective data governance has recently been receiving attention as a critical element for maintaining the value of the organization’s data. Metadata provides the means for identifying and classifying data within subject areas and enabling users and technologists to manage the context as well as the content in information systems.

In my last article, I began a 3 part series designed to outline criteria for staffing a data warehouse program within a corporation. Part I focused on breaking down the various roles, thoughts on how to organize those resources, and considerations for handling support and development. In part II, we will look deeper into how to build and retain a strong organization. This is a significant challenge that corporations face, and more often than not, struggle with.