《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，原书第4版）08 Future Trends, Privacy and Managerial Considerations in Analytics

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 8 AND DATA SCIENCE Future Trends, Privacy and A Managerial Managerial considerations in analytics Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 8 Future Trends, Privacy and Managerial Considerations in Analytics Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7

Learning Objectives (1 of2 8. 1 Explore some of the emerging technologies that may impact analytics, business intelligence(BI), and decision support 8.2 Describe the emerging internet of Things(loT) phenomenon, potential applications, and the loT ecosystem 8. 3 Describe the current and future use of cloud computing in business analytics 8. 4 Describe how geospatial and location-based analytics are assisting organizations Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 8.1 Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support 8.2 Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem 8.3 Describe the current and future use of cloud computing in business analytics 8.4 Describe how geospatial and location-based analytics are assisting organizations

Learning Objectives (2 of 2) 8. 5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8. 7 Identify key characteristics of a successful data science ofessional Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 8.5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8.7 Identify key characteristics of a successful data science professional

Opening Vignette Analysis of Sensor Data Helps Siemens Avoid Train Failures Discussion 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? 2. How would weather data be useful in analyzing a trains equipment status? 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 4. How would you propose to store such data sets? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette Analysis of Sensor Data Helps Siemens Avoid Train Failures Discussion 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? 2. How would weather data be useful in analyzing a train’s equipment status? 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. 4. How would you propose to store such data sets?

Internet of Things (loT)( of2 loT is an area with explosive growth Connecting physical world to the Internet Social Network versus lot human-to-human vs, machine-to-machine Enablers: sensors and sensing devices EXample Self driving cars Fitness trackers Smartbin-trash detectors detecting fill levels Smart refrigerators, and other appliances Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Internet of Things (IoT) (1 of 2) • IoT is an area with explosive growth • Connecting physical world to the Internet • Social Network versus IoT – human-to-human vs. machine-to-machine • Enablers: sensors and sensing devices • Example – Self driving cars – Fitness trackers – Smartbin – trash detectors detecting fill levels – Smart refrigerators, and other appliances

Internet of Things (oT)(20f2 By 2020, besides computing and communication devices (tablets, phones, and PCs), another 38B things will be connected to the internet Reasons for incredible growth in lOT Hardware -smaller, affordable, more powerful Availability of Bl tools -more capable and cheaper Emergence of new and innovative use cases There isnt a universal agreement on the term lot Web of Things Internet of Systems Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Internet of Things (IoT) (2 of 2) • By 2020, besides computing and communication devices (tablets, phones, and PCs), another 38B things will be connected to the Internet • Reasons for incredible growth in IoT: – Hardware – smaller, affordable, more powerful – Availability of BI tools – more capable and cheaper – Emergence of new and innovative use cases • There isn’t a universal agreement on the term IoT – Web of Things – Internet of Systems, …

Application Case 8.1 SilverHook Powerboats Uses Real-Time Data analysis to Inform racers and fans Questions for Discussion 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? 2. Which other sports might benefit from similar technologies? 3. What technological challenges might you face in building such systems? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 8.1 SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans Questions for Discussion 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? 2. Which other sports might benefit from similar technologies? 3. What technological challenges might you face in building such systems?

pplication Case 8.2 Rockwell Automation Monitors expensive oil and Gas Exploration Assets Questions for Discussion 1. What type of information would likely be collected by an oil and gas drilling platform? 2. Does this application fit the three vs of Big data (volume, variety, velocity)? Why or why not? 3. Which other industries could use similar operational measurements and dashboards? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 8.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets Questions for Discussion 1. What type of information would likely be collected by an oil and gas drilling platform? 2. Does this application fit the three V’s of Big Data (volume, variety, velocity)? Why or why not? 3. Which other industries could use similar operational measurements and dashboards?

loT Technology Infrastructure loT related technology components can be divided into four major blocks: 1. Hardware physical devIces, sensors, and actuators 2. Connectivity Collecting and sending sensory data to the cloud 3. Software Integrating and processing data for patterns 4. Applications Creating context specific alerts, actionable insight Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved IoT Technology Infrastructure • IoT related technology components can be divided into four major blocks: 1. Hardware ▪ physical devices, sensors, and actuators 2. Connectivity ▪ Collecting and sending sensory data to the cloud 3. Software ▪ Integrating, and processing data for patterns 4. Applications ▪ Creating context specific alerts, actionable insight

Building Blocks of loT Technology Infrastructure g稳 loT Dev cee Data storer Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Building Blocks of IoT Technology Infrastructure