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Lecture Business intelligence - Chapter 5: Technologies enabling insights and decisions

This chapter presents the following content: Data mining; business analytics; goal, design, techniques & implementation of data mining; decision trees or rule induction, as a knowledge-modeling tool; predictive techniques; real time decision support. | Chapter 5:Technologies Enabling Insights and Decisions Introduction Data mining Business analytics Goal, design, techniques & implementation of data mining Decision trees or rule induction, as a knowledge-modeling tool Predictive techniques Real time decision support Technologies to Create Insights: Using Data Mining to Create New Explicit Knowledge Business analytics Knowledge discovery in databases (KDD), or more commonly, data mining (DM) Analytics comes with hypothesis testing Data mining is more the act of discovery that lacks a hypothesis DM applications have made noteworthy contributions to scientific discovery, for example, in breast cancer diagnosis Technologies to Create Insights: Using Data Mining to Create New Explicit Knowledge Some of the factors driving BI are: Exploding data volumes Increasing decision complexity Need for quick reflexes Technological progress BI Intelligence in Practice: Data Mining Applications for e-Commerce One successful e-tailer implementation is | Chapter 5:Technologies Enabling Insights and Decisions Introduction Data mining Business analytics Goal, design, techniques & implementation of data mining Decision trees or rule induction, as a knowledge-modeling tool Predictive techniques Real time decision support Technologies to Create Insights: Using Data Mining to Create New Explicit Knowledge Business analytics Knowledge discovery in databases (KDD), or more commonly, data mining (DM) Analytics comes with hypothesis testing Data mining is more the act of discovery that lacks a hypothesis DM applications have made noteworthy contributions to scientific discovery, for example, in breast cancer diagnosis Technologies to Create Insights: Using Data Mining to Create New Explicit Knowledge Some of the factors driving BI are: Exploding data volumes Increasing decision complexity Need for quick reflexes Technological progress BI Intelligence in Practice: Data Mining Applications for e-Commerce One successful e-tailer implementation is the case of eBags, a web based storefront of handbags, suitcases, wallets, and other similar products. Web pages garnering maximum purchases Utilization of customer information for customizing and maximizing profits Proflowers, an online resource that describes itself as ‘connecting consumers with fresh-from-the-field flowers’ Timely delivery of the product to maximizing customer satisfaction Improved the management Other Successful DM Applications Banking Target Marketing Insurance Telecommunications Operations Management Retail Sales Forecasting Systems Diagnosis The Business Analytics Process Business analytics Two significant ways to get new insights from the existing information: Discovery by using existing information Discovery by finding useful patterns in observations Cross-Industry Standard Process for Data Mining (CRISP-DM) CRISP-DM Process Methodology Steps for the Data Preparation Selection Construction and transformation of variables Data Integration Formatting The .