SOLUTIONS MANUAL
** All Chapters included
** Review Questions
** Discussion Questions
** End Of Chapter Excercises
,Table of Contents are given below
1. An Overview of Business Analytics, Decision Support Systems, Business
Intelligence, Data Science, and Artificial Intelligence
2. Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business
Applications
3. Nature of Data, Statistical Modeling, and Visualization
4. Data Mining Process, Methods, and Applications
5. Machine learning Techniques for Predictive Analytics
6. Deep Learning and Cognitive Computing
7. Text Mining, Sentiment Analysis, and Social Analytics
8. Prescriptive Analytics with Optimization and Simulation
9. Big Data, Location Analytics, and Cloud Computing
10. Robotics: Industrial and Consumer Applications
11. Group Decision Making, Collaborative Systems, and AI Support
12. Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal
Assistants, and Robo Advisors
13. The Internet of Things As a Platform for Intelligent Applications
14. Implementation Issues: From Ethics and Privacy to Organizational and Societal
Impacts
, Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
• Understand the need for computerized support of managerial decision making
• Understand the development of systems for providing decision-making support
• Recognize the evolution of such computerized support to the current state of analytics/data
science and artificial intelligence
• Describe the business intelligence (BI) methodology and concepts
• Understand the different types of analytics and review selected applications
• Understand the basic concepts of artificial intelligence (AI) 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, 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. As
technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and
workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized
support portfolio 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. The book presents the
fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE
(exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to
various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how
various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe
that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component
of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be
used for developing such applications. However, the book is not limited to any one software tool, so 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. In our own teaching experience, projects undertaken in the class
1
, facilitate such exploration after students have been exposed to the myriad of applications and concepts in the book and
they have experienced specific software introduced by the professor. This chapter has the following sections:
CHAPTER OUTLINE
1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
1.3 Decision-Making Processes and Computer Decision Support Framework
1.4 Evolution of Computerized Decision Support to Business Intelligence/ Analytics/Data Science
1.5 Analytics Overview
1.6 Analytics Examples in Selected Domains
1.7 Artificial Intelligence Overview
1.8 Convergence of Analytics and AI
1.9 Overview of the Analytics Ecosystem
1.10 Plan of the Book
1.11 Resources, Links, and the Teradata University Network Connection
ANSWERS TO END OF SECTION REVIEW QUESTIONS
Opening Vignette Questions
1. It is said that KONE is embedding intelligence across its supply chain and enables smarter buildings. Explain.
KONE uses a variety of IoT applications to record and communicate a wide variety of systems status and
performance information that can then be used to identify issues and collect important data for future applications.
2. Describe the role of IoT in this case.
IoT allows for the collection of multiple discrete points of data throughout the systems that can be used in a
variety of applications.
3. What makes IBM Watson a necessity in this case?
IBM Watson serves to both collect and analyze the wide variety of information presented. It can then
communicate this information to other systems and establish patterns based on the data collected.
4. Check IBM Advanced Analytics. What tools were included that relate to this case?
The tools available have many possible applications to the case, specifically the ability to evaluate the data
collected across a large number of systems and different parameters.
5. Check IBM cognitive buildings. How do they relate to this case?
This solution uses many similar technologies that appears to focus primarily on the ability to detect issues and
potential issues within the building.
2
** All Chapters included
** Review Questions
** Discussion Questions
** End Of Chapter Excercises
,Table of Contents are given below
1. An Overview of Business Analytics, Decision Support Systems, Business
Intelligence, Data Science, and Artificial Intelligence
2. Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business
Applications
3. Nature of Data, Statistical Modeling, and Visualization
4. Data Mining Process, Methods, and Applications
5. Machine learning Techniques for Predictive Analytics
6. Deep Learning and Cognitive Computing
7. Text Mining, Sentiment Analysis, and Social Analytics
8. Prescriptive Analytics with Optimization and Simulation
9. Big Data, Location Analytics, and Cloud Computing
10. Robotics: Industrial and Consumer Applications
11. Group Decision Making, Collaborative Systems, and AI Support
12. Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal
Assistants, and Robo Advisors
13. The Internet of Things As a Platform for Intelligent Applications
14. Implementation Issues: From Ethics and Privacy to Organizational and Societal
Impacts
, Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
• Understand the need for computerized support of managerial decision making
• Understand the development of systems for providing decision-making support
• Recognize the evolution of such computerized support to the current state of analytics/data
science and artificial intelligence
• Describe the business intelligence (BI) methodology and concepts
• Understand the different types of analytics and review selected applications
• Understand the basic concepts of artificial intelligence (AI) 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, 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. As
technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and
workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized
support portfolio 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. The book presents the
fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE
(exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to
various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how
various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe
that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component
of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be
used for developing such applications. However, the book is not limited to any one software tool, so 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. In our own teaching experience, projects undertaken in the class
1
, facilitate such exploration after students have been exposed to the myriad of applications and concepts in the book and
they have experienced specific software introduced by the professor. This chapter has the following sections:
CHAPTER OUTLINE
1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
1.3 Decision-Making Processes and Computer Decision Support Framework
1.4 Evolution of Computerized Decision Support to Business Intelligence/ Analytics/Data Science
1.5 Analytics Overview
1.6 Analytics Examples in Selected Domains
1.7 Artificial Intelligence Overview
1.8 Convergence of Analytics and AI
1.9 Overview of the Analytics Ecosystem
1.10 Plan of the Book
1.11 Resources, Links, and the Teradata University Network Connection
ANSWERS TO END OF SECTION REVIEW QUESTIONS
Opening Vignette Questions
1. It is said that KONE is embedding intelligence across its supply chain and enables smarter buildings. Explain.
KONE uses a variety of IoT applications to record and communicate a wide variety of systems status and
performance information that can then be used to identify issues and collect important data for future applications.
2. Describe the role of IoT in this case.
IoT allows for the collection of multiple discrete points of data throughout the systems that can be used in a
variety of applications.
3. What makes IBM Watson a necessity in this case?
IBM Watson serves to both collect and analyze the wide variety of information presented. It can then
communicate this information to other systems and establish patterns based on the data collected.
4. Check IBM Advanced Analytics. What tools were included that relate to this case?
The tools available have many possible applications to the case, specifically the ability to evaluate the data
collected across a large number of systems and different parameters.
5. Check IBM cognitive buildings. How do they relate to this case?
This solution uses many similar technologies that appears to focus primarily on the ability to detect issues and
potential issues within the building.
2
