The Importance Of Business Intelligence

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Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business…show more content…
Business intelligence (BI) could be the competitive advantage for organizations to increase profitability. South Africa is faced with an unemployment rate of over 40% and it is not desirable that costs are contained by reducing staff. It is clear that innovative ideas should be looked at to ensure that organizations continue to make profits. Information management The interrelationships with suppliers, customers, distributors, and other business that are needed to design, build and sell a product make up the network of business entities, relationships, and processes that is called a supply chain. Business Intelligence (Castellanosm and Dayal, 2008) involves the integration of core information with meaningful business information to detect significant events, discover new business scenarios and predict business situations. It includes the ability to monitor business trends, to evolve and adapt quickly as situations change and to make intelligent business decisions on uncertain judgments and contradictory information. The paper tries to identify the role of business intelligence in supply chain…show more content…
These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. The amount of data is so great that manual analysis of the data is difficult, if not impossible. The need to handle such large volumes of data led to the development of knowledge-based expert systems. These automated systems performed important functions such as identifying fraudulent phone calls and identifying network faults. The problem with this approach is that it is time-consuming to obtain the knowledge from human experts (the “knowledge acquisition bottleneck”) and, in many cases, the experts do not have

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