MIS Exam 3 Study Guide - MIS Exam 3 Study Guide Chapter 9:...

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MIS Exam 3 Study Guide Chapter 9: Competitive Advantage with Information Systems for Decision Making Terms : Business intelligence (BI) System – provides information for improving decision making - Reporting system: integrate data from multiple sources and process that data by sorting, grouping, summing, averaging, and comparing; results are formatted into reports; improves decision making by providing the right information to the right user at the right time. - Data-mining systems: process data using sophisticated statistical techniques like regression analysis and decision tree analysis; look for patterns and relationships used to anticipate events or predict outcomes. Market-basket analysis: computes correlations of items on past orders to determine items that are frequently purchased together - Knowledge-Management (KM) system: create value from intellectual capital by collecting and sharing human knowledge of products, product uses, best practices, etc. with employees, managers, customers, suppliers, etc. - Expert system: encapsulate experts’ knowledge in the form of if/then rules; improves decision making by nonexperts Clickstream data – describes a customer’s clicking behavior - nothing wrong with sampling Content management system: track organizational documents, Web pages, graphics, etc. Do not directly support business operations; concerned with creation, management and delivery of documents that exist for the purpose of communicating knowledge; basic function are to manage and deliver documents Data mart: data collection created to address needs of a particular business function, problem or opportunity - smaller than data warehouse - users may not have data management expertise but are knowledgeable analysts for a given business function (ex: user of Store Sales data mart can analyze data in that data mart and make a market-basket analysis for sales training) Data mining (knowledge discovery in databases – KDD): application of statistical techniques to find patterns and relationships among data and to anticipate events and predict outcomes - Unsupervised: analysts don’t create a model or hypothesis before running the analysis; apply data-mining technique and observe results; hypotheses created after the analysis to explain the pattern found cluster analysis: identify groups of entities that have similar characteristics - supervised data mining: data miners develop a model prior to the analysis. Statistical techniques applied to data to estimate parameters of the model regression analysis: measures the impact of a set of variables on another variable neural networks: predict values and make classifications, such as “good prospect” or “poor prospect” customers; complicated set of nonlinear equations -Problems with data mining: dirty data; missing values or outside of range; time value may make no sense; you know the least when you start the study so when you gain knowledge, you add parameters and have to
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MIS Exam 3 Study Guide - MIS Exam 3 Study Guide Chapter 9:...

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