Future Trends, Privacy CHAPTER and managerial 8 Considerations in analytics Learning objectives for Chapter 8 Explore some of the emerging technologies that may impact analytics, business intelligence(BI), and decision support Describe the emerging Internet of Things (loT) phenomenon, potential applications, and the loT ecosystem Describe the current and future use of cloud computing in business analytics Describe how geospatial and location-based analytics are assisting organizations Describe the organizational impacts of analytics applications List and describe the major ethical and legal issues of analytics implementation Identify key characteristics of a successful data science professional CHAPTER OVERVIEW This chapter introduces several emerging technologies that are likely to have major impacts on the development and use of business intelligence(BI)applications In a dynamic area such as analytics, the terms also evolve and overlap. As noted earlier, we can refer to these technologies as Bl, analytics, data science, machine learning, artificial intelligence(Al), cognitive computing, Big Data, or by several other labels. Our goal is not to focus on subtle d ifferences among each, but to look at the collection as one big constellation. We focus on some trends that have already been realized and others that are about to impact analytics further. Using a crystal ball is always a risky proposition, but this chapter provides an analysis of some growing areas. We introduce and explain some emerging technologies and explore their current applications. We then discuss the organizational, personal, legal, ethical, and societal impacts of analytical support systems and issues that should be of importance to managers and professionals analytics. This chapter contains the following sections Copyright C2018 Pearson Education, Inc
1 Copyright © 2018Pearson Education, Inc. Future Trends, Privacy and Managerial Considerations in Analytics Learning Objectives for Chapter 8 ▪ Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support ▪ Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem ▪ Describe the current and future use of cloud computing in business analytics ▪ Describe how geospatial and location-based analytics are assisting organizations ▪ Describe the organizational impacts of analytics applications ▪ List and describe the major ethical and legal issues of analytics implementation ▪ Identify key characteristics of a successful data science professional CHAPTER OVERVIEW This chapter introduces several emerging technologies that are likely to have major impacts on the development and use of business intelligence (BI) applications. In a dynamic area such as analytics, the terms also evolve and overlap. As noted earlier, we can refer to these technologies as BI, analytics, data science, machine learning, artificial intelligence (AI), cognitive computing, Big Data, or by several other labels. Our goal is not to focus on subtle differences among each, but to look at the collection as one big constellation. We focus on some trends that have already been realized and others that are about to impact analytics further. Using a crystal ball is always a risky proposition, but this chapter provides an analysis of some growing areas. We introduce and explain some emerging technologies and explore their current applications. We then discuss the organizational, personal, legal, ethical, and societal impacts of analytical support systems and issues that should be of importance to managers and professionals in analytics. This chapter contains the following sections: CHAPTER 8
CHAPTER OUTLINE 8. 1 Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train 8.2 Internet of Things 8.3 Cloud Computing and Business Analytics 8.4 Location-Based Analytics for Organizations 8.5 Issues of Legality, Privacy, and Ethics 8.6 Impacts of Analytics in Organizations: An Overview 8.7 Data Scientist as a profession Copyright C2018 Pearson Education, Inc
2 Copyright © 2018Pearson Education, Inc. CHAPTER OUTLINE 8.1 Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures 8.2 Internet of Things 8.3 Cloud Computing and Business Analytics 8.4 Location-Based Analytics for Organizations 8.5 Issues of Legality, Privacy, and Ethics 8.6 Impacts of Analytics in Organizations: An Overview 8.7 Data Scientist as a Profession
ANSWERS TO END OF SECTION REVIEW QUEST|oNs°···· Section 8. 1 Review Questions 1. In industrial equipment such as trains, what parameters might one measure on a regular basis to estimate the equipment's current performance and future repair needs? There are many parameters that could be evaluated to help estimate current ormance and repair needs. Some of these parameters could include time in use, ther, ad verse impacts, and so on How would weather data be useful in analyzing a train s equipment status Weather data could indicate if the components have been exposed to water, or if the components have been exposed to excesses and heat or cold Estimate how much data you might collect in one month using, say, 1,000 sensors on a train. Each sensor might yield 1 KB data per second 1,000 sensors at 1KB of data per second(43, 200 K/month)is a total of 43. 2 GB across all sensors 4. How would you propose to store such data sets? This volume of data would need to be stored in a robust database system that would be able to analyze all of the individual readings Section 8.2 Review Questions 1. What are the major uses of IoT There are a wide variety of uses for the Internet of Things(loT). Examples car include monitoring the status of different devices, as well as communicating that status and other environmental information to other devices or to central systems What are the technology build ing blocks of IoT These major building blocks include hardware, connectivity, the software kend, and applicat 3. What iS RFID? RFID is a generic technology that refers to the use of radio-frequency waves to identify objects. Fundamentally, RFID is one example of a family of automatic Copyright C2018 Pearson Education, Inc
3 Copyright © 2018Pearson Education, Inc. ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 8.1 Review Questions 1. In industrial equipment such as trains, what parameters might one measure on a regular basis to estimate the equipment’s current performance and future repair needs? There are many parameters that could be evaluated to help estimate current performance and repair needs. Some of these parameters could include time in use, weather, adverse impacts, and so on. 2. How would weather data be useful in analyzing a train’s equipment status? Weather data could indicate if the components have been exposed to water, or if the components have been exposed to excesses and heat or cold. 3. Estimate how much data you might collect in one month using, say, 1,000 sensors on a train. Each sensor might yield 1 KB data per second. 1,000 sensors at 1KB of data per second (43,200 K/month) is a total of 43.2 GB across all sensors. 4. How would you propose to store such data sets? This volume of data would need to be stored in a robust database system that would be able to analyze all of the individual readings. Section 8.2 Review Questions 1. What are the major uses of IoT? There are a wide variety of uses for the Internet of Things (IoT). Examples can include monitoring the status of different devices, as well as communicating that status and other environmental information to other devices or to central systems. 2. What are the technology building blocks of IoT? These major building blocks include hardware, connectivity, the software backend, and applications. 3. What is RFID? RFID is a generic technology that refers to the use of radio-frequency waves to identify objects. Fundamentally, RFID is one example of a family of automatic
identification technologies, which also includes the ubiquitous barcodes and magnetic strips 4. Search online for applications of RFID in healthcare, entertainment, and sports Student searches will vary 5 Identify some key players in the loT ecosystem. Explore their offerings Major players in the Internet of things can be classified into building block suppliers, platforms and enablement, and applications across multiple verticals. A discussion of any of these areas will be highly variable based on the player and sub area selected and when the research is conducted 6. What are some of the major issues managers have to keep in mind in exploring When managers consider the loT there are several important concepts to take into account. The first is organizational alignment; how does this technology fit in with the companys current goals and resources? Second are interoperability challenges, will the company be able to use this ad vancement within their current infrastructure? The final issue is security; will information be able to be controlled in a manner that is required and consistent with company policy and existing law? Section 8.3 Review Questions 1. Define cloud computing How does it relate to PaaS, SaaS, and laaS? Cloud computing offers the possibility of using software, hard ware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc, cloud computing offers organizations the latest technologies without significant upfront investment In some ways, cloud computing is a new name for many previous related trends utility computing, application service provider grid computing, on-demand computing, oftware as a service(SaaS), and even older centralized computing with dumb terminals But the term cloud computing originates from a reference to the Internet as a"cloud"and presents an evolution of all previous shared /centralized computing trends 2. Give examples of companies offering cloud services Companies offering such services include 1010data, LogIXML, and Lucid Era. These companies offer feature extract, transform, and load capabilities as well as advanced ata analysis tools. Other companies, such as Lastra and rightscale, offer dashboard and data management tools that follow the saaS and DaaS models Copyright C2018 Pearson Education, Inc
4 Copyright © 2018Pearson Education, Inc. identification technologies, which also includes the ubiquitous barcodes and magnetic strips. 4. Search online for applications of RFID in healthcare, entertainment, and sports. Student searches will vary. 5. Identify some key players in the IoT ecosystem. Explore their offerings. Major players in the Internet of things can be classified into building block suppliers, platforms and enablement, and applications across multiple verticals. A discussion of any of these areas will be highly variable based on the player and sub area selected and when the research is conducted. 6. What are some of the major issues managers have to keep in mind in exploring IoT? When managers consider the IoT there are several important concepts to take into account. The first is organizational alignment; how does this technology fit in with the company’s current goals and resources? Second are interoperability challenges; will the company be able to use this advancement within their current infrastructure? The final issue is security; will information be able to be controlled in a manner that is required and consistent with company policy and existing law? Section 8.3 Review Questions 1. Define cloud computing. How does it relate to PaaS, SaaS, and IaaS? Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud computing offers organizations the latest technologies without significant upfront investment. In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a “cloud” and represents an evolution of all previous shared/centralized computing trends. 2. Give examples of companies offering cloud services. Companies offering such services include 1010data, LogiXML, and Lucid Era. These companies offer feature extract, transform, and load capabilities as well as advanced data analysis tools. Other companies, such as Elastra and Rightscale, offer dashboard and data management tools that follow the SaaS and DaaS models
3. How does cloud computing affect BI? Cloud-computing-based BI services offer organizations the latest technologies without significant upfront investment 4. How does DaaS change the way data is handled? In the DaaS model, the actual platform on which the data resides doesnt matter. Data can reside in a local computer or in a server at a server farm inside a cloud-computing environment. With DaaS, any business process can access data wherever it resides Customers can move quickly thanks to the simplicity of the data access and the fact that they don' t need extensive knowledge of the underly ing data 5. What are the different types of cloud platforms? Differing types include laas (Infrastructure as a Service), Paas(Platform as a Service), and Saas( Software as a Service) 6. Why is AaaS cost effective? AaaS in the cloud has economies of scale and scope by providing many virtual nalytical applications with better scalability and higher cost savings. The capabilities that a service orientation(along with cloud computing, pooled resources, and parallel processing) brings to the analytic world enable cost-effective data/text mining, large scale optimization, highly-complex multi-criteria decision problems, and distributed simulation models 7. Name at least three major cloud service providers Student selections will vary from those discussed on pages 429-440 8. Give at least three examples of analytics-as-a-service providers Student examples will vary from those discussed on pages 429-440 Section 8.4 Review Questions 1. How does traditional analytics make use of location-based data? Traditional analytics produce visual maps that are geographically mapped and based on the traditional location data, usually grouped by the postal codes. The use of postal codes to represent the data is a somewhat static approach for achiev ing a higher level view of things Copyright o201& Pearson Education, Inc
5 Copyright © 2018Pearson Education, Inc. 3. How does cloud computing affect BI? Cloud-computing-based BI services offer organizations the latest technologies without significant upfront investment. 4. How does DaaS change the way data is handled? In the DaaS model, the actual platform on which the data resides doesn’t matter. Data can reside in a local computer or in a server at a server farm inside a cloud-computing environment. With DaaS, any business process can access data wherever it resides. Customers can move quickly thanks to the simplicity of the data access and the fact that they don’t need extensive knowledge of the underlying data. 5. What are the different types of cloud platforms? Differing types include IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and Saas (Software as a Service). 6. Why is AaaS cost effective? AaaS in the cloud has economies of scale and scope by providing many virtual analytical applications with better scalability and higher cost savings. The capabilities that a service orientation (along with cloud computing, pooled resources, and parallel processing) brings to the analytic world enable cost-effective data/text mining, largescale optimization, highly-complex multi-criteria decision problems, and distributed simulation models. 7. Name at least three major cloud service providers. Student selections will vary from those discussed on pages 429 – 440. 8. Give at least three examples of analytics-as-a-service providers. Student examples will vary from those discussed on pages 429 – 440. Section 8.4 Review Questions 1. How does traditional analytics make use of location-based data? Traditional analytics produce visual maps that are geographically mapped and based on the traditional location data, usually grouped by the postal codes. The use of postal codes to represent the data is a somewhat static approach for achieving a higher level view of things
2. How can geocoded locations assist in better decision making They help the user in understanding true location-based"impacts, and allow them to view at higher granularities than that offered by the trad itional postal code aggregations. Addition of location components based on latitudinal and longitud inal attributes to the trad itional analytical techniques enables organizations to add a new dimension of where"to their trad itional business analyses, which currently answer questions of who, "what, when, and"how much. By integrating information about the location with other critical business data, organizations are now creating intelligence (Li) 3. What is the value provided by geospatial analytics? Geospatial analysis gives organizations a broader perspective and aids in decision making. Location intelligence (Li)is enabling organizations to gain critical insights and make better decisions by optimizing important processes and applications. B incorporating demographic details into locations, retailers can determine how sales by alation level and to other itors the the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint locations, and easily trace them back to the products Sales reps can better target their prospects by analyzing their geography 4. Explore the use of geospatial analytics further by investigating its use across various sectors like go us tracking, consumer marketing, and so forth Students'answers will vary 5. Search online for other applications of consumer-oriented analytical applications Students' answers will vary 6. How can location-based analytics help individual consumers? Location-based behavioral targeting can help to narrow the characteristics of users who are most likely to utilize a retailer's services or products. This sort of analytics would typically target the tech-savvy and busy consumers of the company in question 7. Explore more transportation applications that may employ location-based analytics Another app that is mentioned in the text is one deployed in Pittsburgh, Pennsylvania, and developed in collaboration with Carnegie Mellon University. This app, called ParkPGH, includes predictive capabilities to estimate parking availability. It calculates the number of spaces available in downtown Pittsburgh parking lots and garages (Student answers will vary.) 6 Copyright C2018 Pearson Education, Inc
6 Copyright © 2018Pearson Education, Inc. 2. How can geocoded locations assist in better decision making? They help the user in understanding “true location-based” impacts, and allow them to view at higher granularities than that offered by the traditional postal code aggregations. Addition of location components based on latitudinal and longitudinal attributes to the traditional analytical techniques enables organizations to add a new dimension of “where” to their traditional business analyses, which currently answer questions of “who,” “what,” “when,” and “how much.” By integrating information about the location with other critical business data, organizations are now creating location intelligence (LI). 3. What is the value provided by geospatial analytics? Geospatial analysis gives organizations a broader perspective and aids in decision making. Location intelligence (LI) is enabling organizations to gain critical insights and make better decisions by optimizing important processes and applications. By incorporating demographic details into locations, retailers can determine how sales vary by population level and proximity to other competitors; they can assess the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint locations, and easily trace them back to the products. Sales reps can better target their prospects by analyzing their geography. 4. Explore the use of geospatial analytics further by investigating its use across various sectors like government census tracking, consumer marketing, and so forth. Students’ answers will vary. 5. Search online for other applications of consumer-oriented analytical applications. Students’ answers will vary. 6. How can location-based analytics help individual consumers? Location-based behavioral targeting can help to narrow the characteristics of users who are most likely to utilize a retailer’s services or products. This sort of analytics would typically target the tech-savvy and busy consumers of the company in question. 7. Explore more transportation applications that may employ location-based analytics. Another app that is mentioned in the text is one deployed in Pittsburgh, Pennsylvania, and developed in collaboration with Carnegie Mellon University. This app, called ParkPGH, includes predictive capabilities to estimate parking availability. It calculates the number of spaces available in downtown Pittsburgh parking lots and garages. (Student answers will vary.)
8. What other applications can you imagine if you were able to access cell phone location data? Do a search on location-enabled services Students'answers will vary Section 8.5 Review Questions 1. List some legal issues of analytics What is the value of an expert opinion in court when the expertise is encoded in a Who is liable for wrong advice (or information) provided by an intelligent application? What happens if a manager enters an incorrect judgment value into an analytic application and the result is damage or a disaster? Who owns the knowledge in a knowledge base? Can management force experts to contribute their expertise? 2. Describe privacy concerns in analytics neral is the right to be left alone and the right to be free unreasonable personal intrusions. The Internet, in combination with large-scale databases, has created an entirely new d imension of accessing and using data. The inherent power in systems that can access vast amounts of data can be used for the good of society. For example, by matching records with the aid of a computer, it is possible to eliminate or reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support filching, employment of illegal aliens, and so on. The same is true on the corporate level. Private information about employees may aid in better decision making, but the employees' privacy may be affected Similar issues are related to information about customers 3. In your view, who should own the data about your use of a car? Student responses will vary. 4. List ethical issues in analytics Representative ethical issues that could be of interest in MSS implementations include the following Electronic surveillance · Ethics in dss design Software piracy Invasion of individuals privacy se of proprietary databases Copyright C2018 Pearson Education, Inc
7 Copyright © 2018Pearson Education, Inc. 8. What other applications can you imagine if you were able to access cell phone location data? Do a search on location-enabled services. Students’ answers will vary. Section 8.5 Review Questions 1. List some legal issues of analytics. • What is the value of an expert opinion in court when the expertise is encoded in a computer? • Who is liable for wrong advice (or information) provided by an intelligent application? • What happens if a manager enters an incorrect judgment value into an analytic application and the result is damage or a disaster? • Who owns the knowledge in a knowledge base? • Can management force experts to contribute their expertise? 2. Describe privacy concerns in analytics. In general, privacy is the right to be left alone and the right to be free from unreasonable personal intrusions. The Internet, in combination with large-scale databases, has created an entirely new dimension of accessing and using data. The inherent power in systems that can access vast amounts of data can be used for the good of society. For example, by matching records with the aid of a computer, it is possible to eliminate or reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support filching, employment of illegal aliens, and so on. The same is true on the corporate level. Private information about employees may aid in better decision making, but the employees’ privacy may be affected. Similar issues are related to information about customers. 3. In your view, who should own the data about your use of a car? Student responses will vary. 4. List ethical issues in analytics. Representative ethical issues that could be of interest in MSS implementations include the following: • Electronic surveillance • Ethics in DSS design • Software piracy • Invasion of individuals’ privacy • Use of proprietary databases
Use of intellectual property such as knowledge and expertise Exposure of employees to unsafe environments related to computers Computer accessibility for workers with disabilities curacy of data, information, and knowledge Protection of the rights of user Accessibility to information How much decision making to delegate to computers poses Ise of corporate computers for non-work-related purpo Section 8.6 Review Questions 1. List the impacts of analytics on decision makin Analytics can change the manner in which many decisions are made and can Sequently change managers' jobs. They can help managers gain more knowledge, experience, and expertise, and consequently enhance the quality and speed of their decision making. In particular, information gathering for decision making is ompleted much more quickly when analytics are in use. This affects both strategic planning and control decisions, changing the decision-making process and even decision-making styles 2. List the impacts of analytics on other managerial tasks Less expertise(experience) is required for making many decisions Faster decision making is possible because of the availability of information and the automation of some phases in the decision-making process. Less reliance on experts and analysts is required to provide support to top executives. Power is being redistributed among managers. The more information and analysis capability they possess, the more power they have. ) Support for complex decisions allows decisions to be made faster and of better quality. Information needed for high-level decision making is expedite or even self-generated. Automation of routine decisions or phases in the decision- naking process(e.g, for frontline decision making and using ADS)may eliminate some managers, especially middle level managers. Routine and mundane work can be done using an analytic system, freeing up managers and knowledge workers to do more challenging tasks 3. Describe new organizational units that are created because of analytics One change in organizational structure is the possibility of creating an analytics department, a BI department, or a knowledge management department in which analytics play a major role. This special unit can be combined with or replace a quantitative analysis unit, or it can be a completely new entity Copyright C2018 Pearson Education, Inc
8 Copyright © 2018Pearson Education, Inc. • Use of intellectual property such as knowledge and expertise • Exposure of employees to unsafe environments related to computers • Computer accessibility for workers with disabilities • Accuracy of data, information, and knowledge • Protection of the rights of users • Accessibility to information • Use of corporate computers for non-work-related purposes • How much decision making to delegate to computers Section 8.6 Review Questions 1. List the impacts of analytics on decision making. Analytics can change the manner in which many decisions are made and can consequently change managers’ jobs. They can help managers gain more knowledge, experience, and expertise, and consequently enhance the quality and speed of their decision making. In particular, information gathering for decision making is completed much more quickly when analytics are in use. This affects both strategic planning and control decisions, changing the decision-making process and even decision-making styles. 2. List the impacts of analytics on other managerial tasks. Less expertise (experience) is required for making many decisions. Faster decision making is possible because of the availability of information and the automation of some phases in the decision-making process. Less reliance on experts and analysts is required to provide support to top executives. Power is being redistributed among managers. (The more information and analysis capability they possess, the more power they have.) Support for complex decisions allows decisions to be made faster and of better quality. Information needed for high-level decision making is expedited or even self-generated. Automation of routine decisions or phases in the decisionmaking process (e.g., for frontline decision making and using ADS) may eliminate some managers, especially middle level managers. Routine and mundane work can be done using an analytic system, freeing up managers and knowledge workers to do more challenging tasks. 3. Describe new organizational units that are created because of analytics. One change in organizational structure is the possibility of creating an analytics department, a BI department, or a knowledge management department in which analytics play a major role. This special unit can be combined with or replace a quantitative analysis unit, or it can be a completely new entity
4. Identify other examples of analytics applications to redesign work space or team Behavior Examples can include the use of HR systems to identify potential job candidates that ill be the best fit within an existing organization. Another example is analyzing how employees move through the organization and who they collaborate with. This is data that can be used to design office space that is more efficient 5. How is cognitive computing affecting industry structure? Cognitive computing will have a large impact in many different industries because jobs that were historically completed by humans may be automated. This would have arge cultural implications as well as business implications. From a business perspective, automation has the possibility to decrease cycle time while increasing quality. Conversely, startup cost for automation may be significant 6. Which jobs are most likely to change as a result of automation? Accord ing to the articles, initial job losses will focus on areas that are not skill-based and that may require repetitive actions that do not require a high amount of knowledge 7. Study The Economist(Standage, 2016)report mentioned in this section. What other impacts of automation did you find interesting? Student perceptions may vary, but most will comment on the significant cultural changes and implications of automation and the resulting loss of jobs Section 8.7 Review Questions 1. What is a data scientist? What makes them so much in demand? Data scientists use a combination of their business and technical skills to investigate Big Data, looking for ways to improve current business analytics practices(from descriptive to predictive and prescriptive) and hence to improve decisions for new business opportunities. One of the biggest differences between a data scientist and a business intelligence user--such as a business analyst-is that a data scientist investigates and looks for new possibilities, while a bi user analyzes existing business situations and operations. Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have. But there is a common understand ing of what skills and qualities they are expected to possess, which involve a combination of soft and hard skills What are the common characteristics of data scientists? which one is the most m Copyright C2018 Pearson Education, Inc
9 Copyright © 2018Pearson Education, Inc. 4. Identify other examples of analytics applications to redesign work space or team behavior. Examples can include the use of HR systems to identify potential job candidates that will be the best fit within an existing organization. Another example is analyzing how employees move through the organization and who they collaborate with. This is data that can be used to design office space that is more efficient. 5. How is cognitive computing affecting industry structure? Cognitive computing will have a large impact in many different industries because jobs that were historically completed by humans may be automated. This would have large cultural implications as well as business implications. From a business perspective, automation has the possibility to decrease cycle time while increasing quality. Conversely, startup cost for automation may be significant. 6. Which jobs are most likely to change as a result of automation? According to the articles, initial job losses will focus on areas that are not skill-based, and that may require repetitive actions that do not require a high amount of knowledge. 7. Study The Economist (Standage, 2016) report mentioned in this section. What other impacts of automation did you find interesting? Student perceptions may vary, but most will comment on the significant cultural changes and implications of automation and the resulting loss of jobs. Section 8.7 Review Questions 1. What is a data scientist? What makes them so much in demand? Data scientists use a combination of their business and technical skills to investigate Big Data, looking for ways to improve current business analytics practices (from descriptive to predictive and prescriptive) and hence to improve decisions for new business opportunities. One of the biggest differences between a data scientist and a business intelligence user—such as a business analyst—is that a data scientist investigates and looks for new possibilities, while a BI user analyzes existing business situations and operations. Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have. But there is a common understanding of what skills and qualities they are expected to possess, which involve a combination of soft and hard skills. 2. What are the common characteristics of data scientists? Which one is the most important?
One of the most sought-out characteristics of a data scientist is expertise in both technical and business application domains. Data scientists are expected to have soft skills such as creativity, curiosity, communication/interpersonal skills domain expertise, problem definition skills, and managerial skills as well as sound technical skills such as data manipulation, programming/hacking/scripting, and knowledge of Internet and social media/networking technologies. Data scientists are supposed to be creative and curious, and should be excellent communicators with the ability to tell compelling stories about their data 3. Where do data scientists come from? What educational backgrounds do they have? Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have Master of Science(or Ph. D )in Computer Science, MIS, Industrial Engineering, of postgraduate analytics are common examples. But many data scientists have advanced degrees in other disciplines, like the physical or social sciences, or more specialized fields like ecology or system biology 4. What do you think is the path to becoming a great data scientist? Becoming a great data scientist requires you to delve deeply into developing quantitative and technical skills, as well as interpersonal and communication skills. In add ition, you will need to gain significant domain knowledge(e.g, in business). This effort will most likely require an advanced degree. It also require a continuous thirst for knowledge and an intense curiosity; you will always be learning in this profession. In add ition to meticulous analytical skills, it also requires creativity and imagination. (Students will vary in their answers to this question. ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION O Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and fans What type of information might the sensors on a race boat generate that would be important for the to know? What about for the fans? Student opinions will vary, but examples may include information on boat systems(engine, etc. ) location and speed as well as biometric information on the racers Which other sports might benefit from similar technologies? Student opinions will vary, but may include car racing, motocross and mountain Copyright C2018 Pearson Education, Inc
10 Copyright © 2018Pearson Education, Inc. One of the most sought-out characteristics of a data scientist is expertise in both technical and business application domains. Data scientists are expected to have soft skills such as creativity, curiosity, communication/interpersonal skills, domain expertise, problem definition skills, and managerial skills as well as sound technical skills such as data manipulation, programming/hacking/scripting, and knowledge of Internet and social media/networking technologies. Data scientists are supposed to be creative and curious, and should be excellent communicators, with the ability to tell compelling stories about their data. 3. Where do data scientists come from? What educational backgrounds do they have? Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have. Master of Science (or Ph.D.) in Computer Science, MIS, Industrial Engineering, of postgraduate analytics are common examples. But many data scientists have advanced degrees in other disciplines, like the physical or social sciences, or more specialized fields like ecology or system biology. 4. What do you think is the path to becoming a great data scientist? Becoming a great data scientist requires you to delve deeply into developing quantitative and technical skills, as well as interpersonal and communication skills. In addition, you will need to gain significant domain knowledge (e.g., in business). This effort will most likely require an advanced degree. It also requires a continuous thirst for knowledge and an intense curiosity; you will always be learning in this profession. In addition to meticulous analytical skills, it also requires creativity and imagination. (Students will vary in their answers to this question.) ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans 1. What type of information might the sensors on a race boat generate that would be important for the racers to know? What about for the fans? Student opinions will vary, but examples may include information on boat systems (engine, etc.), location and speed as well as biometric information on the racers. 2. Which other sports might benefit from similar technologies? Student opinions will vary, but may include car racing, motocross and mountain