Management Information Systems
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When analysts speak of companies or organizations, they are actually referring to open systems composed of subsystems, each of which must function efficiently and effectively if the organization is to survive. Often, these subsystems are viewed independent from the larger whole, but in fact, they are interrelated and interdependent. This interrelationship has become increasingly clear as information technology has emerged that is able to bring together various pieces of data generated in different and disparate subsystems but used by entirely different subsystems. Managing the information technology in this environment is a challenging and even daunting task, but bringing together key pieces of data and transforming them into valuable information can be a critical success factor for many companies. This research considers the various subsystems at a typical company and how three different applications of information technology link those subsystems together through management information systems.Fundamental to the understanding of organizations is the concept of the organization as system. In this context, a system is composed of interrelated and interacting components that modify their environment. In other words, the organization-as-system takes inputs from the environment, modifies them, and returns the resulting output to the environment (Daft, 1998). Open systems must have external interaction
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data that can be either lightly or heavily summarized, and the data warehouse is excellent at storing and transforming historical data. Typically, data in a data warehouse is structured for query and analysis purposes (Smith, 2002).
Traditionally, DSS provides strong data transformation capabilities and strong analysis, but on a limited basis (Shafiei & Sundaram, 2004). For example, decision support systems might be implemented in individual functional areas of an organization, such as the manufacturing group, but isolated from other parts of the organization. Thus, a decision support system might take into account the various factors that influence production capacity and analyze the most efficient quantities for production. The manufacturing decision makers might well act on this information. However, if the company is unable to sell the units that are produced, the company is actually worse off than if production had remained at previous levels. This illustrates one of the drawbacks to DSS and highlights the need for systems that can pull information from across the organization (Brady, Monk & Wagner, 2001).
EXPERT SYSTEMS/ARTIFICIAL INTELLIGENCE
Artificial intelligence came to be largely synonymous with "expert system
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Some common words found in the essay are:
SYSTEMS Executive, Guimaraes O'Neal, Weather Channel, SUBSYSTEMS Fundamental, North Carolina, CBT CD ROMs, INTRANETS Intranets, Thomas Kleiner, SYSTEMS INTRODUCTION, Arif Badiru, information systems, decision support, expert systems, executive information, executive information systems, decision support systems, support systems, decision maker, neural networks, information technology, decision makers, natural language, management information systems, natural language processing, decision support system,
Approximate Word count = 3942
Approximate Pages = 16 (250 words per page)
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