CHAPTER An Overview of business Intelligence, Analytics and data science Learning objectives for Chapter 1 Understand today' s turbulent business environment and describe how organizations vive and even excel in such an environment(solving problems and exploiting opportunities) Understand the need for computerized support of managerial decision making Recognize the evolution of such computerized support to the current state analytics/data science Describe the business intelligence(BI)methodology and concepts Understand the various types of analytics, and see selected applications Understand the analytics ecosystem to identify various key players and career opportunitie CHAPTER OVERVIEW The business environment(climate)is constantly changing, and it is becoming force them to respond quickly to changing conditions and to be innovative in the war o more and more complex Organizations, both private and public, are under pressures they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex Making such decisions may require considerable amounts of relevant data, information, Copyright C2018 Pearson Education, Inc
1 Copyright © 2018Pearson Education, Inc. An Overview of Business Intelligence, Analytics, and Data Science Learning Objectives for Chapter 1 ▪ Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities) ▪ Understand the need for computerized support of managerial decision making ▪ Recognize the evolution of such computerized support to the current state— analytics/data science ▪ Describe the business intelligence (BI) methodology and concepts ▪ Understand the various types of analytics, and see selected applications ▪ Understand the analytics ecosystem to identify various key players and career opportunities CHAPTER OVERVIEW The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, CHAPTER 1 1
and knowledge. Processing these, in the framework of the needed decisions, must be done quickly, frequently in real time, and usually requires some computerized support This book is about using business analytics as computerized support for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support, as well as on the commercial tools and techniques that are available. This book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEe approach to introducing these topics: Exposure, Experience, and Explore. The book primarily provides exposure to various analytics techniques and their applications. The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also introduce specific software tools that can be used for developing such applications. The book is not limited to any one software tool, so the students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata University Network(TUN) and other sites that include team-oriented exercises where appropriate overview of the book. The chapter has the following sectiorge analytics as well as an This introductory chapter provides an introduction to CHAPTER OUTLINE 1. 1 Opening Vignette: Sports Analytics--An Exciting Frontier for Learning and Understanding Applications of Analytics 1.2 Changing Business Environments and Evolving Needs for Decision Support and analyti 1.3 Evolution of Computerized Decision Support to Analytics/Data Science 1. 4 A Framework for Business intelligence 1. 5 analytics Overview 1.6 Analytics Examples in Selected Domains 1.7 A Brief Introduction to Big Data Analytics 1.8 An Overview of the Analytics Ecosystem 1.9 Plan of the Book Copyright C2018 Pearson Education, Inc
2 Copyright © 2018Pearson Education, Inc. and knowledge. Processing these, in the framework of the needed decisions, must be done quickly, frequently in real time, and usually requires some computerized support. This book is about using business analytics as computerized support for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support, as well as on the commercial tools and techniques that are available. This book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE approach to introducing these topics: Exposure, Experience, and Explore. The book primarily provides exposure to various analytics techniques and their applications. The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what is being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also introduce specific software tools that can be used for developing such applications. The book is not limited to any one software tool, so the students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata University Network (TUN) and other sites that include team-oriented exercises where appropriate. This introductory chapter provides an introduction to analytics as well as an overview of the book. The chapter has the following sections: CHAPTER OUTLINE 1.1 Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics 1.3 Evolution of Computerized Decision Support to Analytics/Data Science 1.4 A Framework for Business Intelligence 1.5 Analytics Overview 1.6 Analytics Examples in Selected Domains 1.7 A Brief Introduction to Big Data Analytics 1.8 An Overview of the Analytics Ecosystem 1.9 Plan of the Book
1.10 Resources, Links, and the teradata university network Connection TEACHING TIPSIADDITIONAL INFORMATION The purpose of any introductory chapter is to motivate students to be interested in the remainder of the course(and book). The real-life cases, beginning with Magpie Sensing and continuing with the others, will show students that business intelligence is not just an academic subject; it is something real companies use that makes a noticeable difference to their bottom line. So, try to relate the subject matter to these cases. For example, consider the types of actions managers take to counter pressures, especially the list of organizational responses. The opening case about Magpie illustrates several of the options available to health care companies, such as innovation, partnerships with others in the cold chain, and the use of IT to improve data access. The other cases in the chapter offer other examples of managerial actions taken in response to pressure. By referring back to this list when discussing other cases, you demonstrate the unity of the analytics field All this should show students that a new professional who understands how information systems can support decision making, and can help his or her employer obtain those benefits, has a bright career path. Since students in this course are typically within a year of graduation. that will get their attention ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 1.1 Review Questions 1. What are three factors that might be part of a PM for season ticket renewals? The case provides several examples of data that may be used as a part of this analysis. Data factors may include survey responses, pricing models, and customer tweets 2. What are two techniques that football teams can use to do opponent analysis? annotated game film to produce an analysis evaluating whether to buia ach's In the example provided, opponent analytics was evaluated using the c cascaded decision tree model on play prediction, heat maps of passing offenses and time series analytics on explosive plays How can wearables improve player health and safety? What kinds of new nalytics can trainers use? The case provides several examples of how wearables can be used to improve player health. Wearables can help to identify levels and variation in core body Copyright C2018 Pearson Education, Inc
3 Copyright © 2018Pearson Education, Inc. 1.10 Resources, Links, and the Teradata University Network Connection TEACHING TIPS/ADDITIONAL INFORMATION The purpose of any introductory chapter is to motivate students to be interested in the remainder of the course (and book). The real-life cases, beginning with Magpie Sensing and continuing with the others, will show students that business intelligence is not just an academic subject; it is something real companies use that makes a noticeable difference to their bottom line. So, try to relate the subject matter to these cases. For example, consider the types of actions managers take to counter pressures, especially the list of organizational responses. The opening case about Magpie illustrates several of the options available to health care companies, such as innovation, partnerships with others in the cold chain, and the use of IT to improve data access. The other cases in the chapter offer other examples of managerial actions taken in response to pressure. By referring back to this list when discussing other cases, you demonstrate the unity of the analytics field. All this should show students that a new professional who understands how information systems can support decision making, and can help his or her employer obtain those benefits, has a bright career path. Since students in this course are typically within a year of graduation, that will get their attention! ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 1.1 Review Questions 1. What are three factors that might be part of a PM for season ticket renewals? The case provides several examples of data that may be used as a part of this analysis. Data factors may include survey responses, pricing models, and customer tweets. 2. What are two techniques that football teams can use to do opponent analysis? In the example provided, opponent analytics was evaluated using the coach’s annotated game film to produce an analysis evaluating whether to build a cascaded decision tree model on play prediction, heat maps of passing offenses, and time series analytics on explosive plays. 3. How can wearables improve player health and safety? What kinds of new analytics can trainers use? The case provides several examples of how wearables can be used to improve player health. Wearables can help to identify levels and variation in core body
strength, mobile devices worn during play can record data on hits to assist in concussion protocols, and sleeps sensors can identify how rested players are 4. What other analytics applications can you envision in sports? Student responses will vary, but many potential examples are possible. Some include tracking performance over time or location Section 1.2 Review Questions What are some of the key system-oriented trends that have fostered IS-supported decision making to a new Improvements and innovation in systems in many areas have facilitated the growth of decision-making systems. These areas include Group communication and collaboration software and systems Improved data management applications and techniques Data warehouses and Big Data for information collection Analytical support systems Growth in processing and storing formation storage capabilities Knowled ge management systems Support of all of these systems that is always available 2. List some capabilities of information systems that can facilitate managerial decision Information systems can aid decision making because they have the ability to perform functions that allow for better communication and information capture better storage and recall of data, and vastly improved analytical models that can be more voluminous or more preci 3. How can a computer help overcome the cognitive limits of humans? Computer-based systems are not limited in many of the ways people are, and this lack of limits allows unique abilities to evaluate data. Examples of abilities include being able to store huge amounts of data, being able to run extensive Copyright C2018 Pearson Education, Inc
4 Copyright © 2018Pearson Education, Inc. strength, mobile devices worn during play can record data on hits to assist in concussion protocols, and sleeps sensors can identify how rested players are. 4. What other analytics applications can you envision in sports? Student responses will vary, but many potential examples are possible. Some include tracking performance over time or location. Section 1.2 Review Questions 1. What are some of the key system-oriented trends that have fostered IS-supported decision making to a new level? Improvements and innovation in systems in many areas have facilitated the growth of decision-making systems. These areas include: • Group communication and collaboration software and systems • Improved data management applications and techniques • Data warehouses and Big Data for information collection • Analytical support systems • Growth in processing and storing formation storage capabilities • Knowledge management systems • Support of all of these systems that is always available 2. List some capabilities of information systems that can facilitate managerial decision making. Information systems can aid decision making because they have the ability to perform functions that allow for better communication and information capture, better storage and recall of data, and vastly improved analytical models that can be more voluminous or more precise. 3. How can a computer help overcome the cognitive limits of humans? Computer-based systems are not limited in many of the ways people are, and this lack of limits allows unique abilities to evaluate data. Examples of abilities include being able to store huge amounts of data, being able to run extensive
numbers of scenarios and analyses, and the ability to spot trends in vast datasets Section 1.3 Review Questions 1. List three of the terms that have been predecessors of analytics Analytics has evolved from other systems over time including data support systems(DSS), operations research(OR)models, and expert systems(ES) 2. What was the primary difference between the systems called MIS, DSS, and Executive Support Systems? Many systems have been used in the past and present to provide analytic Management information systems(MIS) provided reports on various aspects of business functions using captured information while decision support systems (SS)added the ability to use data with models to address unstructured problems Executive support systems(ESS) added to these abilities by capturing understand ing from experts and integrating it into systems via if-then-else rules or Did dss evolve into bi or vice versa? DSS systems became more advanced in the 2000s with the addition of data warehousing capabilities and began to be referred to as Business Information(Bl) systems Section 1.4 Review Questions 1. Define Bl Business Intelligence(Bl) is an umbrella term that combines architectures, tools databases, analytical tools, applications, and methodologies. Its major objective is to enable interactive access(sometimes in real time)to data, enable manipulation of these data, and provide business managers and analysts the ability to conduct appropriate analysis List and describe the major components of BI BI systems have four major components the data warehouse(with its source data), business analytics(a collection of tools for manipulating, mining, and nalyzing the data in the data warehouse), business performance management(for monitoring and analyzing performance), and the user interface(e.g, a dashboard) Copyright C2018 Pearson Education, Inc
5 Copyright © 2018Pearson Education, Inc. numbers of scenarios and analyses, and the ability to spot trends in vast datasets or models. Section 1.3 Review Questions 1. List three of the terms that have been predecessors of analytics. Analytics has evolved from other systems over time including data support systems (DSS), operations research (OR) models, and expert systems (ES). 2. What was the primary difference between the systems called MIS, DSS, and Executive Support Systems? Many systems have been used in the past and present to provide analytics. Management information systems (MIS) provided reports on various aspects of business functions using captured information while decision support systems (DSS) added the ability to use data with models to address unstructured problems. Executive support systems (ESS) added to these abilities by capturing understanding from experts and integrating it into systems via if-then-else rules or heuristics. 3. Did DSS evolve into BI or vice versa? DSS systems became more advanced in the 2000s with the addition of data warehousing capabilities and began to be referred to as Business Information (BI) systems. Section 1.4 Review Questions 1. Define BI. Business Intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. Its major objective is to enable interactive access (sometimes in real time) to data, enable manipulation of these data, and provide business managers and analysts the ability to conduct appropriate analysis. 2. List and describe the major components of BI. BI systems have four major components: the data warehouse (with its source data), business analytics (a collection of tools for manipulating, mining, and analyzing the data in the data warehouse), business performance management (for monitoring and analyzing performance), and the user interface (e.g., a dashboard)