BUS101_Ch11_Lecture_4in1

BUS101_Ch11_Lecture_4in1 - Objectives Management...

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Management Information Systems, Sixth Edition Chapter 11: Business Intelligence and Knowledge Management Objectives • Explain the concepts of data mining and online analytical processing • Explain the notion of business intelligence and its benefits to organizations • Identify needs for knowledge storage and management in organizations • Explain the challenges in knowledge management and its benefits to organizations • Identify possible ethical and societal issues arising from the increasing globalization of information technology Data Mining and Online Analysis • Data warehouse: a large database containing historical transactions and other data • Data warehouses are useless without software tools to process the data into meaningful information • Business intelligence (BI): information gleaned with information analysis tools – Also called business analytics Data Mining • Data mining: the process of selecting, exploring, and modeling large amounts of data – Used to discover relationships that can support decision making • Data-mining tools may use complex statistical analysis applications • Data-mining queries are more complex than traditional queries
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Data Mining (continued) • Data mining has four main objectives: – Sequence or path analysis: finding patterns where one event leads to another – Classification: finding whether certain facts fall into predefined groups – Clustering: finding groups of related facts not previously known – Forecasting: discovering patterns that can lead to reasonable predictions Data Mining (continued) • Data mining techniques are applied to various fields, including marketing, fraud detection, and targeted marketing to individuals • Helps businesses in predicting customer behavior • Assists in building customer loyalty programs Data Mining (continued) • Many industries utilize loyalty programs – Examples include frequent-flier programs and consumer clubs – These programs amass huge amounts of data about customers • UPS has a Customer Intelligence Group – Analyzes customer behavior – Predicts customer defections so that a salesperson can intervene to resolve problems Data Mining (continued) • Identifying profitable customer groups – Financial institutions dismiss high-risk customers – Companies attempt to define narrow groups of potentially profitable customers • Utilizing loyalty programs – Amass huge amounts of data about customers – Help companies perform yield management and price-discrimination – Example: Harrah’s charges higher per-night rates to low-volume gamblers
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Data Mining (continued) • Inferring demographics – Predict what customers are likely to purchase in the future – Amazon.com • Determines a customer’s age range based on his or her purchase history • Attempts to determine customer’s gender • Advertises for appropriate age groups based on the inferred customer demographics • Anticipates holidays
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BUS101_Ch11_Lecture_4in1 - Objectives Management...

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