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