Enterprise computing

This concept refers to the organization of data processing and communications across an entire corporation or other organization. Historically, computing technology and infra-structure often developed at different rates in the various departments of a corporation. For example, by the 1970s, departments such as payroll and accounting were making heavy use of electronic data processing (EDP) using main-frame computers. The introduction of the desktop computer in the 1980s often resulted in operations such as marketing, corporate communications, and planning being conducted using a disparate assortment of software, databases, and document repositories. Even the growing use of networking often meant that an enterprise had several different net-works with at best rudimentary intercommunication.

The movement toward enterprise computing, while often functioning as a buzzword for the selling of new network-ing and knowledge management technology, conveys a real need both to manage and leverage the growing information resources used by a large-scale enterprise. The infrastruc-ture for enterprise computing is the network, which today is increasingly built using Internet protocol (see tcp/ip), although legacy networks must often still be supported. Enterprise-oriented software uses the client-server model, with an important decision being which operating systems to support (see client-server computing).

The need for flexibility in making data available across the organization is leading to a gradual shift from the older relational database (RDBMs) to object-oriented databases (OODBMs). One advantage of object-oriented databases is that it is more scalable (able to be expanded without run-ning into bottlenecks) and data can be distributed dynami-cally to take advantage of available computing resources. (An alternative is the central depository. See data ware-house.) The dynamic use of storage resources is also impor-tant (see disk array).

The payoffs for a well-integrated enterprise informa-tion system go beyond efficiency in resource utilization and information delivery. If, for example, the marketing depart-ment has full access to data about sales, the data can be     analyzed to identify key features of consumer behavior (see data mining).