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