Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE Chapter 1 ANALYTICS AND DATA SCIENCE An overview of business A Managerial Intelligence, Analytics, and Data science Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7
Learning Objectives 1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state--analytics/data science 1.3 Describe the business intelligence(Bl)methodology and concepts 1. 4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives 1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state—analytics/data science 1.3 Describe the business intelligence (BI) methodology and concepts 1.4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities
Opening vignette (I of 5) Sports Analytics-An Exciting Frontier for Learning and Understanding Applications of Analytics Sports analytics is becoming a specialty within analytics Sports is a big business Generating $145B in revenues annually Additional $100B in legal and $300B in illegal gambling Analytic in sports popularized by the Moneyball book by Michael Lewis in 2003 About oakland a's And the follow-on movie in 201 1 Nowadays, analytics is used in many facets of sports Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (1 of 5) Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics • Sports analytics is becoming a specialty within analytics • Sports is a big business – Generating $145B in revenues annually – Additional $100B in legal and $300B in illegal gambling • Analytic in sports popularized by the Moneyball book by Michael Lewis in 2003 – About Oakland A’s – And the follow-on movie in 2011 • Nowadays, analytics is used in many facets of sports
Opening vignette (2 of 5) Example 1: The Business Office Figure 1.1 Season Ticket Renewals--Survey Scores Tier Highly Likely Likely May be Probably Not Certainly Not 92 88 75 6 45 12345 88 81 70 65 38 80 76 68 55 36 77 72 65 45 25 75 70 60 35 25 Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (2 of 5) Example 1: The Business Office • Figure 1.1 Season Ticket Renewals—Survey Scores Tier Highly Likely Likely Maybe Probably Not Certainly Not 1 92 88 75 69 45 2 88 81 70 65 38 3 80 76 68 55 36 4 77 72 65 45 25 5 75 70 60 35 25
Opening vignette (3 of 5) Example 2: The Coach Figure 1.4 Heat Map Zone Analysis for Passing Plays c Complete: 35 Complete日 81.48b Expose: 4 Explose 5 Explosive: 2 Line of Scrimmage Complete: 25 Complete: 12 Complete: 14 Complete: B Complete: 25 Total: 24 71.4 5714 56. B1 Explosive:0 Explosive:o Explosive:0 Explosve:1 Complete: 13 Complete: 15 55.55b Explosive: 2 Exptoswo: 9 Explosve: B Comptete 7 7699 3H B 33.339 Explosive: 7 Defense Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (3 of 5) Example 2: The Coach • Figure 1.4 Heat Map Zone Analysis for Passing Plays
Opening Vignette (4 of5 Example 3: The Trainer Figure 1. 7 Single Leg Squat Hold Test- Core Body Strength Test (Source: Wilkerson and Gupta Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (4 of 5) Example 3: The Trainer • Figure 1.7 Single Leg Squat Hold Test – Core Body Strength Test (Source: Wilkerson and Gupta)
Opening Vignette (5 of5 Discussion Questions 1. What are three factors that might be part of a PM for season ticket renewals? 2. What are two techniques that football teams can use to do opponent analysis? 3. How can wearables improve player health and safety? What kinds of new analytics can trainers use? 4. What other analytics applications can you envision in sports? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (5 of 5) Discussion Questions 1. What are three factors that might be part of a PM for season ticket renewals? 2. What are two techniques that football teams can use to do opponent analysis? 3. How can wearables improve player health and safety? What kinds of new analytics can trainers use? 4. What other analytics applications can you envision in sports?
Changing Business Environments and Evolving Needs for Decision Support and Analytics Increased hardware software and network capabilities Group communication and collaboration Improved data management Managing giant data warehouses and Big Data Analytical support Overcoming cognitive limits in processing and storing information Knowledge management Anywhere, anytime support Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Changing Business Environments and Evolving Needs for Decision Support and Analytics • Increased hardware, software, and network capabilities • Group communication and collaboration • Improved data management • Managing giant data warehouses and Big Data • Analytical support • Overcoming cognitive limits in processing and storing information • Knowledge management • Anywhere, anytime support
Evolution of Computerized Decision Support to Analytics/Data Science Figure 1. 8 Evolution of Decision Support, Business Intelligence, and analytics 分 1970s 1980s 1990s 2000s 2010s Decision Support Systems Enterprise/Executive Is Business Intelligence Analytics Big Data… Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Evolution of Computerized Decision Support to Analytics/Data Science • Figure 1.8 Evolution of Decision Support, Business Intelligence, and Analytics
A Framework for Business Intelligence (1 of 3) DSS→E|S→Bl Definition of Business Intelligence Broad Definition] an umbrella term that combines architectures, tools, databases, analytical tools applications, and methodologies [Narrow Definition] Descriptive analytics tools and techniques (i.e, reporting tools) A Brief History of B|-1970s→1980s→1990 The origins and Drivers of Bl ( See Figure 1.9) The architecture of Bl (See Figure 1.10) Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved A Framework for Business Intelligence (1 of 3) • DSS → EIS → BI • Definition of Business Intelligence – [Broad Definition] An umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies – [Narrow Definition] Descriptive analytics tools and techniques (i.e., reporting tools) • A Brief History of BI – 1970s → 1980s → 1990s … • The Origins and Drivers of BI (See Figure 1.9) • The Architecture of BI (See Figure 1.10)
