Business Intelligence and Information Systems for Decision Making
1. Briefly summarize what Business Intelligence is.
Management of great amounts of data is a complex process which requires a comprehensive approach and efficient system of information gathering, transmittal, storing and processing. According to Kroenke, Business Intelligence is “an information system that provides information for improving decision making process as a key business task” (211). It becomes the primary means of improving the companies’ efficiency regarding the delivering of information on a timely basis. Business Intelligence process is divided into two basic types of operations: data acquisition and data transmission. The process of data obtaining represents information inflow from multiple source systems into an integrated repository. After this data processing it becomes meaningful in terms of decision-making (211-212).
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2: How do BI Systems provide competitive advantages?
Business Intelligence Systems help to save time of data providers and consumers through more effective delivery of information. BI Systems provide competitive advantage only in the case when the top management of the company understands the importance of Bi Systems; sufficient resources are available including software and HR that will manage it. Kroenke points that such management covers many important issues, including coordination, unding, prioritization of projects, project management and data quality. Business Intelligence Systems include 4 categories: reporting systems, data mining systems, knowledge management systems, expert systems. Reporting systems provide creating multidimensional reports to users at the right time with the necessary report requirements. Data mining systems process information from a statistical view and help users to evaluate the existing correlations between report elements. Knowledge management systems provide exchange of knowledge to improve the quality of decision-making process. Expert systems encode data in a simple form (211).
3: What are the characteristics of Data Mining Systems?
Kroenke describes Data Mining Systems as a process of discovery useful and accessible interpretation of knowledge in the initial data needed for decision making in various spheres of human activity by using statistical methods. Data Mining is focused on a single data analysis; this process involves obtaining the data, interpretation, integration and visualization of results. The purpose is construction of prediction models. Market-basket analysis is an example of data mining system. Data Mining Systems use more complex and sophisticated statistical methods than simple grouping or averaging (211).
4: How are reports authored, managed, and delivered?
Acccording to Kroenke, report authoring defines and gets data from existing sources, structures report and formats it. Reports can be created using Microsoft Access or alternative developer tool like Visual Studio. Reports are managed in the form of determining what kind of reports are needed, what users obtain the data as well as time period. User accounts and user groups exist. Report administrator decides by which means the user will receive the report, in which format, the notion of urgency or delay of the reports. The report delivery provides security and establishes clear connection between the authorized users and appropriate reports. Delivery of the reports can be processed via email, SOA services, or other means (520-521).
5: What is OLAP?
Kroenke outlines that OnLine Analytical Processing (OLAP) is reporting technology that combines data, using filtering, sorting, grouping, and simple calculating to produce final information. Main OLAP characteristics include multidimensionality of the data model, availability, transparency, multi-user operations, storing final results separately from the original data, flexible report generation. The biggest advantage of OLAP is its ability to provide users with dynamic reports changing few indicators. Measures and dimensions are used in OLAP report creating. OLAP can process larger volume of data presented than Excel. The example can be OLAP cube (521-523).
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