Analytics, Data Science & Artificial Intelligence:
Systems for Decision Support
11th Edition Ramesh Sharda, Dursun Delen, Efraim Turban
Table of Contents:-
Chapter 1. An Overview of Business Analytics, Decision Support Systems, Business Intelligence, Data
Science, and Artificial Intelligence
Chapter 2. Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
Chapter 3. Nature of Data, Statistical Modeling, and Visualization
Chapter 4. Data Mining Process, Methods, and Applications
Chapter 5. Machine learning Techniques for Predictive Analytics
Chapter 6. Deep Learning and Cognitive Computing
Chapter 7. Text Mining, Sentiment Analysis, and Social Analytics
Chapter 8. Prescriptive Analytics with Optimization and Simulation
Chapter 9. Big Data, Location Analytics, and Cloud Computing
Chapter 10. Robotics: Industrial and Consumer Applications
Chapter 11. Group Decision Making, Collaborative Systems, and AI Support
Chapter 12. Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal
Assistants, and Robo Advisors
Chapter 13. The Internet of Things As a Platform for Intelligent Applications
Chapter 14. Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
,CHAPTER NO. 01: AN OVERVIEW OF ANALYTICS, AND AI
LEARNING OBJECTIVES
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
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,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 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.
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, 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.
Section 1.2 Review Questions
1. Why is it difficult to make organizational decisions?
Organizational decisions may be difficult to make due to a complex process
necessary to both identify and define the problem as well as evaluate the host of
different possible solutions.
2. Describe the major steps in the decision-making process.
1.Define the problem (i.e., a decision situation that may deal with some
difficulty or with an opportunity).
2. Construct a model that describes the real-world problem.
3. Identify possible solutions to the modeled problem and evaluate the
solutions.
4. Compare, choose, and recommend a potential solution to the problem.
3. Describe the major external environments that can impact decision making.
Political factors. Major decisions may be influenced by both external and
internal politics. An example is the 2018 trade war on tariffs.
Economic factors. These range from competition to the genera and state of the
economy. These factors, both in the short and long run, need to be considered.
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