Data Modeling Methods

Data modeling methods require considerable time, organizational commitment, and very knowledgeable managers. In order to deal with these concerns, certain guidelines must be followed such as objective, scope, outcome, and timing.

The objective basically defines the modeling effort. The model must be justified by some clear overriding need, such as coordination of operational data processing, flexibility to access data, or effectiveness of data systems. If the goal is not clear, the chance of failure is greater.

The scope deals with the coverage of the data model. For the data model to be effective the coverage must be carefully considered. For the most part the broader the scope, the higher the chances for failure. Scope choices include corporate-wide, division, areas with particular high-impact needs, and a particularly important or willing business function (Brown, 2009).

The outcome is based on the organizational commitment. The outcome deals with identification of common data capture systems to be shared by several departments, managerial and strategic databases and access services to support the information needs of these levels of management (Brown, 2009). The more uncertain the outcome, the lower the chances for success.

The timing of a data modeling system is also important. Only a few organizations can put all systems development on hold while a complete data model is developed. It is possible to do only a high level data model and then fill in details as major systems projects are undertaken.

A business manager often simply wants access to needed data and is not interested in waiting for an entire data model to be built. Unless an overall data management approach is taken, however, the inconsistencies and excessive costs of poorly managed data will consume the integrity and viability of the data resource (Brown, 2009).

Bibliography

Brown, C. (2009). Managing Information Technology. Upper Saddle River: Pearson Prentice...