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)
3. Define oltP OLTP(online transaction processing) is a type of computer processing where the computer responds immediately to user requests. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the eceipt of inventory or a customer order 4. Define OLAP OLAP(online analytical processing)is processing for end-user ad hoc reports, queries, and analysis. Separating the oltP from analysis and decision support provided by OlaP enables the benefits of Bi that were described earlier and provides for competitive intelligence and ad vantage as described next 5. List some of the implementation topics addressed by Gartner's report Gartner's framework decomposes planning and execution into business, organization, functionality, and infrastructure components. At the business and organizational levels, strategic and operational objectives must be defined while considering the available organizational skills to achieve those objectives. Issues of organizational culture surround ing bI initiatives and build ing enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices must be considered by upper management-with plans in place to prepare the organization for change 6. List some other success factors of Bl If the company's strategy is properly aligned with the reasons for DW and BI initiatives, and if the company's IS organization is or can be made capable of playing its role in such a project, and if the requisite user community is in place and has the proper motivation, it is wise to start BI and establish a BI Competency Center (BICC)within the company. The center could serve some or all of the following functions The center can demonstrate how bi is clearly linked to strategy and execution of strategy A center can serve to encourage interaction between the potential business user communities and the is organization The center can serve as a repository and disseminator of best bi practices between and among the different lines of business Standards of excellence in BI practices can be ad vocated and encouraged throughout the company 6 Copyright C2018 Pearson Education, Inc
6 Copyright © 2018Pearson Education, Inc. 3. Define OLTP. OLTP (online transaction processing) is a type of computer processing where the computer responds immediately to user requests. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the receipt of inventory or a customer order. 4. Define OLAP. OLAP (online analytical processing) is processing for end-user ad hoc reports, queries, and analysis. Separating the OLTP from analysis and decision support provided by OLAP enables the benefits of BI that were described earlier and provides for competitive intelligence and advantage as described next. 5. List some of the implementation topics addressed by Gartner’s report. Gartner’s framework decomposes planning and execution into business, organization, functionality, and infrastructure components. At the business and organizational levels, strategic and operational objectives must be defined while considering the available organizational skills to achieve those objectives. Issues of organizational culture surrounding BI initiatives and building enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices must be considered by upper management—with plans in place to prepare the organization for change. 6. List some other success factors of BI. If the company’s strategy is properly aligned with the reasons for DW and BI initiatives, and if the company’s IS organization is or can be made capable of playing its role in such a project, and if the requisite user community is in place and has the proper motivation, it is wise to start BI and establish a BI Competency Center (BICC) within the company. The center could serve some or all of the following functions. • The center can demonstrate how BI is clearly linked to strategy and execution of strategy. • A center can serve to encourage interaction between the potential business user communities and the IS organization. • The center can serve as a repository and disseminator of best BI practices between and among the different lines of business. • Standards of excellence in BI practices can be advocated and encouraged throughout the company
The IS organization can learn a great deal through interaction with the user communities, such as knowledge about the variety of types of analytical tools that are needed The business user community and Is organization can better understand why the data warehouse platform must be flexible enough to provide for changing business requirements It can help important stakeholders like high-level executives see how BI play an important role Another important success factor of Bi is its ability to facilitate a real-time, on- agile environment Section 1.5 Review Questions 1. Define analytics The term replaces terminology referring to individual components of a decision support system with one broad word referring to business intelligence. More precisely, analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data language: " looking at all the data to understand what is happening, what wiy ptive Students may also refer to the eight levels of analytics and this simpler descri happen, and how to make the best of it What is descriptive analytics? What are the various tools that are employed in descriptive analytics? Descriptive analytics refers to knowing what is happening in the organization and understand ing some underlying trends and causes of such occurrences. Tools used in descriptive analytics include data warehouses and visualization applications 3. How is descriptive analytics different from trad itional reporting? Descriptive analytics gathers more data, often automatically It makes results available in real time and allows reports to be customized What is a Dw? How can data warehousing technology help to enable analytics? A data warehouse, introduced in Section 1.7, is the component of a BI system that contains the source data. As described in this section, developing a data warehouse usually includes development of the data infrastructure for descriptive analytics--that is, consolidation of data sources and making relevant data available in a form that enables appropriate reporting and analysis. a data warehouse serves as the basis for developing appropriate reports, queries, alerts and trends 5. What is predictive analytics? How can organizations employ predictive analytics? Copyright C2018 Pearson Education, Inc
7 Copyright © 2018Pearson Education, Inc. • The IS organization can learn a great deal through interaction with the user communities, such as knowledge about the variety of types of analytical tools that are needed. • The business user community and IS organization can better understand why the data warehouse platform must be flexible enough to provide for changing business requirements. • It can help important stakeholders like high-level executives see how BI can play an important role. Another important success factor of BI is its ability to facilitate a real-time, ondemand agile environment. Section 1.5 Review Questions 1. Define analytics. The term replaces terminology referring to individual components of a decision support system with one broad word referring to business intelligence. More precisely, analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data. Students may also refer to the eight levels of analytics and this simpler descriptive language: “looking at all the data to understand what is happening, what will happen, and how to make the best of it.” 2. What is descriptive analytics? What are the various tools that are employed in descriptive analytics? Descriptive analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences. Tools used in descriptive analytics include data warehouses and visualization applications. 3. How is descriptive analytics different from traditional reporting? Descriptive analytics gathers more data, often automatically. It makes results available in real time and allows reports to be customized. 4. What is a DW? How can data warehousing technology help to enable analytics? A data warehouse, introduced in Section 1.7, is the component of a BI system that contains the source data. As described in this section, developing a data warehouse usually includes development of the data infrastructure for descriptive analytics—that is, consolidation of data sources and making relevant data available in a form that enables appropriate reporting and analysis. A data warehouse serves as the basis for developing appropriate reports, queries, alerts, and trends. 5. What is predictive analytics? How can organizations employ predictive analytics?
Pred ictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a teams success 6. What is prescriptive analytics? What kind of problems can be solved by prescriptive analytics? Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches 7. Define modeling from the analytics perspective As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world These models can be used in predictive and prescriptive analytics to develop forecasts. recommendations and decisions 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before apply ing prescriptive analytics? As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business oblem. This requires analysis of historical data, or descriptive analytics Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model How can analytics aid in objective decision making? As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes. The result is that future decisions are no better than past decisions Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered Section 1.6 Review Questions 1. Why would a health insurance company invest in analytics beyond fraud detection? Why is it in their best interest to predict the likelihood of falls by An insurance company would potentially want to evaluate analytics to both quantify the risk of a potential incident category (like falls)and to help identif subgroups of the population that are at-risk for this type of injury. With this type Copyright C2018 Pearson Education, Inc
8 Copyright © 2018Pearson Education, Inc. Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion, and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a team’s success. 6. What is prescriptive analytics? What kind of problems can be solved by prescriptive analytics? Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches. 7. Define modeling from the analytics perspective. As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world. These models can be used in predictive and prescriptive analytics to develop forecasts, recommendations, and decisions. 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before applying prescriptive analytics? As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business problem. This requires analysis of historical data, or descriptive analytics. Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model. 9. How can analytics aid in objective decision making? As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes. The result is that future decisions are no better than past decisions. Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered. Section 1.6 Review Questions 1. Why would a health insurance company invest in analytics beyond fraud detection? Why is it in their best interest to predict the likelihood of falls by patients? An insurance company would potentially want to evaluate analytics to both quantify the risk of a potential incident category (like falls) and to help identify subgroups of the population that are at-risk for this type of injury. With this type
of information, the company can ad dress clients who might be at-risk, and attempt to intervene with less expensive preventative measures What other applications simila ediction of falls Student responses will vary, but could include a number of other medical d itions or types of ac 3. How would you convince a new health insurance customer to adopt healthier ple 3)? Student responses will vary, but may focus on improved customer education that is targeted at specific risk factors as well as financial or benefit inducements tied to positive changes in lifestyle 4. Identify at least three other opportunities for applying analytics in the retail valt chain beyond those covered in this section Many potential opportunities exist, and student responses will vary based on their experences 5. Which retail stores that you know of employ some of the analytics applications identified in this section? Student responses will vary based on the retail establishments they are familiar with and the applications used at the time Section 1.7 Review Questions 1. What is Big Data analytics? Typically, the data is arriving in many ditferent forms. be they structllGo unit The term Big Data refers to data that cannot be stored in a single storage unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data What are the sources of Big data? Major sources include clickstreams from Web sites, postings on social med ia, and data from traffic sensors and the weather 3. What are the characteristics of Big Data? Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading which operates in the order of microseconds; and data about consumer opinions measured from postings in social med ia What processing technique is applied to process Big Data? One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming Copyright C2018 Pearson Education, Inc
9 Copyright © 2018Pearson Education, Inc. of information, the company can address clients who might be at-risk, and attempt to intervene with less expensive preventative measures. 2. What other applications similar to prediction of falls can you envision? Student responses will vary, but could include a number of other medical conditions or types of accidents. 3. How would you convince a new health insurance customer to adopt healthier lifestyles (Humana Example 3)? Student responses will vary, but may focus on improved customer education that is targeted at specific risk factors as well as financial or benefit inducements tied to positive changes in lifestyle. 4. Identify at least three other opportunities for applying analytics in the retail value chain beyond those covered in this section. Many potential opportunities exist, and student responses will vary based on their experiences. 5. Which retail stores that you know of employ some of the analytics applications identified in this section? Student responses will vary based on the retail establishments they are familiar with and the applications used at the time. Section 1.7 Review Questions 1. What is Big Data analytics? The term Big Data refers to data that cannot be stored in a single storage unit. Typically, the data is arriving in many different forms, be they structured, unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data. 2. What are the sources of Big Data? Major sources include clickstreams from Web sites, postings on social media, and data from traffic, sensors, and the weather. 3. What are the characteristics of Big Data? Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading, which operates in the order of microseconds; and data about consumer opinions measured from postings in social media. 4. What processing technique is applied to process Big Data? One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming paradigm
Section 1. 8 Review Questions List the 11 categories of players in the analytics ecosystem These categories include Data generation Infrastructure Providers Data management Infrastructure Providers Data Warehouse Providers Mid d leware Providers Data Service Providers Analytics Focused Software Developers Application Developers: Industry Specific or General Analytics Industry Analysts and Influencers Academic Institutions and Certification Agencies Regulators and Policy Maker Analytics User Organizations 2. Give examples of companies in each of the 11 types of players Examples of companies by area include Data Generation Infrastructure Providers(Sports Sensors, Zepp, Shockbox Advantech B+B Smart Worx, Garmin, and Sensys Network, Intel, Microsoft Copyright C2018 Pearson Education, Inc
10 Copyright © 2018Pearson Education, Inc. Section 1.8 Review Questions 1. List the 11 categories of players in the analytics ecosystem. These categories include: • Data Generation Infrastructure Providers • Data Management Infrastructure Providers • Data Warehouse Providers • Middleware Providers • Data Service Providers • Analytics Focused Software Developers • Application Developers: Industry Specific or General • Analytics Industry Analysts and Influencers • Academic Institutions and Certification Agencies • Regulators and Policy Makers • Analytics User Organizations 2. Give examples of companies in each of the 11 types of players. Examples of companies by area include: • Data Generation Infrastructure Providers (Sports Sensors, Zepp, Shockbox, Advantech B+B SmartWorx, Garmin, and Sensys Network, Intel, Microsoft
