Business Intelligence: A Managerial Perspective on Analytics(3rd Edition) INTELLIGENCE A Managerial Perspective on Analytics Chapter 4: EFRAUTI RRAN Data Mining
Chapter 4: Data Mining Business Intelligence: A Managerial Perspective on Analytics (3rd Edition)
Learning Objectives Define data mining as an enabling technology for business intelligence Understand the objectives and benefits of business analytics and data mining Recognize the wide range of applications of data mining Learn the standardized data mining processes CRISP-DM SEMMA KDD Continued.) Copynight@ 2014 Pearson Education, Inc Slide 4-2
Copyright © 2014 Pearson Education, Inc. Slide 4- 2 Learning Objectives ▪ Define data mining as an enabling technology for business intelligence ▪ Understand the objectives and benefits of business analytics and data mining ▪ Recognize the wide range of applications of data mining ▪ Learn the standardized data mining processes ▪ CRISP-DM ▪ SEMMA ▪ KDD (Continued…)
Learning Objectives Understand the steps involved in data preprocessing for data mining Learn different methods and algorithms of data mIning Build awareness of the existing data mining software tools Commercial versus free/open source Understand the pitfalls and myths of data mining Copynight@ 2014 Pearson Education, Inc Slide 4-3
Copyright © 2014 Pearson Education, Inc. Slide 4- 3 Learning Objectives ▪ Understand the steps involved in data preprocessing for data mining ▪ Learn different methods and algorithms of data mining ▪ Build awareness of the existing data mining software tools ▪ Commercial versus free/open source ▪ Understand the pitfalls and myths of data mining
Opening Vignette Cabela's reels in more Customers with Advanced Analytics and Data Mining Decision situation Problem Proposed solution Results Answer discuss the case questions Copynight@ 2014 Pearson Education, Inc Slide 4-4
Copyright © 2014 Pearson Education, Inc. Slide 4- 4 Opening Vignette… Cabela’s Reels in More Customers with Advanced Analytics and Data Mining ▪ Decision situation ▪ Problem ▪ Proposed solution ▪ Results ▪ Answer & discuss the case questions
Questions for the Opening Vignette 1. Why should retailers, especially omni-channel retailers pay extra attention to advanced analytics and data mining? 2. What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges? 3. What are the sources of data that retailers such as Cabela's use for their data mining projects? 4. What does it mean to have a single view of the customer"? How can it be accomplished? 5. What type of analytics help did Cabela's get from their efforts? Can you think of any other potential benefits of analytics for large-scale retailers like Cabela's? Copynight@ 2014 Pearson Education, Inc Slide 4-5
Copyright © 2014 Pearson Education, Inc. Slide 4- 5 Questions for the Opening Vignette 1. Why should retailers, especially omni-channel retailers, pay extra attention to advanced analytics and data mining? 2. What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges? 3. What are the sources of data that retailers such as Cabela’s use for their data mining projects? 4. What does it mean to have a “single view of the customer”? How can it be accomplished? 5. What type of analytics help did Cabela’s get from their efforts? Can you think of any other potential benefits of analytics for large-scale retailers like Cabela’s?