This book presents a framework for developing an analytics strategy that includes a range of activities, from problem definition and data collection to data warehousing, analysis, and decision making. The authors examine best practices in team analytics strategies such as player evaluation, game strategy, and training and performance. They also explore the way in which organizations can use analytics to drive additional revenue and operate more efficiently. The authors provide keys to building and organizing a decision intelligence analytics that delivers insights into all parts of an organization. The book examines the criteria and tools for evaluating and selecting decision intelligence analytics technologies and the applicability of strategies for fostering a culture that prioritizes data-driven decision making. Each chapter is carefully segmented to enable the reader to gain knowledge in business intelligence, decision making and artificial intelligence in a strategic management context.
Author(s): P. Mary Jeyanthi, Tanupriya Choudhury, Dieu Hack-Polay, T. P. Singh, Sheikh Abujar
Series: EAI/Springer Innovations in Communication and Computing
Publisher: Springer
Year: 2021
Language: English
Pages: 245
City: Cham
Foreword
Preface
Acknowledgments
Contents
About the Editors
1 Analytics Techniques: Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics
1.1 Introduction
1.2 Analytics
1.2.1 Role of Analytics in Business
1.2.2 Types of Analytics
1.3 Descriptive Analytics
1.3.1 Functions of Descriptive Analytics
1.3.2 Advantages of Descriptive Analytics
1.3.3 Descriptive Analytics and Its Uses
1.3.4 Need for Other Analytics
1.4 Predictive Analytics
1.4.1 Steps in Predictive Analytics
1.4.2 Predictive Analytics and Its Uses
1.4.3 Predictive Analytics Examples
1.5 Prescriptive Analytics
1.5.1 Advantages of Prescriptive Analytics
1.5.2 Prescriptive Analytics and Its Uses
1.5.3 Prescriptive Analytics Examples
1.6 Conclusion
1.7 Future Directions
References
2 A Complete Overview of Analytics Techniques: Descriptive, Predictive, and Prescriptive
2.1 Introduction
2.2 Descriptive Analytics
2.2.1 The Ratio Analysis: An Elaborate Example of Descriptive Analytics in Business
2.2.2 The Gist of Descriptive Analytics
2.3 Predictive Analytics
2.4 Statistics
2.4.1 How Does It Work?
2.4.1.1 Classification Models
2.4.1.2 Regression Models
2.4.2 Predictive Analytics Process
2.4.3 Predictive Analytics Tools
2.4.4 Uses of Predictive Analytics in Different Industries
2.4.5 Why Now?
2.5 Prescriptive Analytics
2.5.1 Introduction
2.5.2 Background of Prescriptive Analytics
2.5.3 Methods for Prescriptive Analytics
2.5.3.1 Probabilistic Models
2.5.3.2 Machine Learning
2.5.3.3 Statistical Analysis
2.5.3.4 Mathematical Programming
2.5.3.5 Evolutionary Computation
2.5.3.6 Simulation
2.5.3.7 Logic-Based Models
2.6 Conclusion
2.7 Conclusion
References
3 Artificial Intelligence and Analytics for Better Decision-Making and Strategy Management
3.1 Introduction
3.2 India and AI
3.3 Potential of AI
3.3.1 Healthcare
3.3.2 Agriculture
3.3.3 Education
3.3.4 Smart Mobility in Transportation
3.4 Role of AI in Decision-Making
3.4.1 Strategic Management and AI
3.4.2 Key Challenges to Adopt AI in India
3.5 Conclusion
References
4 Artificial Intelligence: Game Changer in Management Strategies
4.1 Introduction
4.2 Concept of Artificial Intelligence
4.3 Background of “AI”
4.4 The Digital Business Vagueness
4.5 Digital Transformation in Management
4.5.1 Ambition of the Organization
4.5.2 To Design a Product
4.5.3 Deliver Phase
4.5.4 Scaling
4.5.5 To Refine
4.6 Artificial Intelligence in Organization
4.7 Who Is Manager?
4.8 “Strategic Management”
4.8.1 Connection Between Strategic Management and Artificial Intelligence
4.8.2 Improvement and Redefining in the Organization Strategies Vis-a-Vis AI
4.8.3 Artificial Intelligence on Strength, Weakness, Opportunities, Threat (SWOT)
4.9 Conclusion
References
5 Prospects and Future of Artificial Intelligence (AI) in Business Strategies
5.1 Introduction
5.2 Literature Review Including Research Gap
5.3 Materials and Methods
5.3.1 Worldwide Companies or Owners of Humanoid and Related to AI
5.3.2 Workforce Domain Contains Retention
5.4 Case Study and Application
5.4.1 Workforce Planning
5.4.1.1 Human Office and Oversight
5.4.1.2 Specialized Robustness and Well-being
5.4.1.3 Security and Data Organization
5.4.1.4 Straightforwardness
5.4.1.5 Assortment, Non-isolation, and Sensibility
5.4.1.6 Social and Common Success
5.4.1.7 Obligation
5.4.2 The Interest of Denmark, Finland, France, and Germany in AI is Likewise Significant
5.4.2.1 Occupation Misfortunes AI in the Work Environment Have Meanings of Mass Occupation Misfortunes
5.4.2.2 Expenses
5.4.2.3 Absence of Mindfulness
5.5 Safety Measures
5.5.1 Diminishes Human Error
5.5.2 Attempts of Hazardous Undertakings
5.5.3 Track Worker Location and That is Only the Tip of the Iceberg
5.5.4 Screens Workplace Harassment
5.5.5 Work Environment Automation
5.6 Recruitment
5.7 Authors' Contribution
5.8 Result or Conclusion with Future Scope
References
6 Artificial Intelligence: Technologies, Applications, and Policy Perspectives. Insights from Portugal
6.1 Introduction
6.2 Methodology
6.3 AI: Themes, Sectors, and Applications
6.4 Policy Reflections About Fostering Artificial Intelligence
6.5 EU Artificial Intelligence Strategy 2030
6.6 Portugal Artificial Intelligence Overview
6.7 Portuguese AI Case Studies
6.8 Artificial Intelligence Policy Ramifications
6.9 Conclusion
References
7 The Rise of Decision Intelligence: AI That Optimizes Decision-Making
7.1 The Rise of Decision Intelligence (DI)
7.2 The Rise of Decision Intelligence (DI)
7.3 An Opinion to Decision-Making
7.3.1 Decisions Are Judgements
7.3.2 Know the Criteria of Relevant Information/Data
7.3.3 Test your Opinions Against Reality/Actual Data
7.4 Decision Intelligence: The Pathway
7.5 A Framework of DI
7.6 The Emergence of Machines as Aids in Society
7.7 The Evolving Pervasiveness of AI and Capabilities in Human Life
7.8 AI, Decision Intelligence, and Business Organizations
References
8 A Survey on Analytics Technique Used for Business Intelligence
8.1 Introduction
8.2 Literature Review
8.3 Models and Techniques: An Overview
8.4 Applications
8.5 Conclusion
References
9 Decision Intelligence Analytics: Making Decisions Through Data Pattern and Segmented Analytics
9.1 Introduction
9.2 Basic Terminologies
9.2.1 Panel Data Analysis
9.2.2 Formal Concept Analysis
9.3 Formal Panel Concept Analysis
9.4 Representation of Panel Data
9.5 Experimental Results
9.6 Conclusion
References
10 Amalgamation of Business Intelligence with Corporate Strategic Management
10.1 Introduction
10.2 Strategic Management
10.3 The Role of Business Intelligence on Strategic Management Choices
10.4 The Role of Data Quality in Strategic Management
10.5 The Business Intelligence for Development of Data Integration
10.6 The Business Intelligence for the Development of a Reporting System
10.7 The Business Intelligence for Developing Future Scenarios
10.8 The Business Intelligence for Optimising Processes
10.9 The Role of Business Intelligence on Obtaining a Competitive Advantage
10.10 Case Study
10.11 Conclusion
References
11 Role of Decision Intelligence in Strategic Business Planning
11.1 Introduction
11.2 Why Does Strategic Business Planning Need Decision Intelligence? And Decision Intelligence: How it Influences SBP?
11.2.1 Strategic Business Planning
11.2.2 Decision Intelligence
11.3 Decision Intelligence in Strategic Business Planning
11.4 Decision Intelligence and Strategic Business Planning Misconceptions
11.5 Conclusion/Recommendations
References
12 Social and Web Analytics: An Analytical Case Study on Twitter Data
12.1 Introduction
12.2 Social Media Platforms and Analytics Tools
12.2.1 Social Media Platforms
12.2.2 Social Media Analytical Tools
12.3 Social Media Data Collection Using Twitter API
12.3.1 Data Collection from Twitter
12.3.2 Data Labeling
12.3.3 Data Pre-Processing and Cleaning
12.3.4 Data Analytics
12.4 Conclusion
References
13 People Analytics: Augmenting Horizon from Predictive Analytics to Prescriptive Analytics
13.1 Introduction
13.2 People Analytics Constituents
13.3 Descriptive People Analytics
13.4 Predictive People Analytics
13.5 Prescriptive People Analytics
13.6 Conclusion
References
14 Machine Learning Based Predictive Analytics: A Use Case in Insurance Sector
14.1 Introduction
14.1.1 Descriptive Analytics
14.1.2 Diagnostic Analytics
14.1.3 Predictive Analytics
14.1.4 Prescriptive Analytics
14.2 Machine Learning Empowering Predictive Analytics
14.3 A Use Case of Machine Learning and Predictive Analytics: Prediction of Insurance Premium
14.3.1 Dataset Description
14.3.2 Exploratory Data Analysis
14.3.3 Implementation of Prediction Model and Results
14.4 Conclusion
References
15 Machine Learning Applications in Decision Intelligence Analytics
15.1 Introduction
15.1.1 Application of Machine Learning
15.1.1.1 Virtual Personal Assistants (VPA's)
15.1.1.2 Traffic Predictions
15.1.1.3 Social Media Personalization
15.1.1.4 Email Spam Filtering
15.1.1.5 Online Fraud Detection
15.1.1.6 Assistive Medical Technology
15.1.1.7 Automatic Translation
15.1.1.8 Recommendation Engines
15.2 Summary and Conclusion
References
16 Demystifying Behavioral Biases of Traders UsingMachine Learning
16.1 Introduction
16.1.1 Confirmation Bias
16.1.2 Illusion of Control Bias
16.1.3 Availability Bias
16.1.4 Representativeness Bias
16.1.5 Framing Bias
16.1.6 Self-Attribution Bias
16.1.7 Recency Bias
16.1.8 Outcome Bias
16.1.9 Cognitive Dissonance Bias
16.2 Concluding Remarks
References
17 Real-Time Data Visualization Using Business Intelligence Techniques in Small and Medium Enterprises for Making a Faster Decision on Sales Data
17.1 Introduction
17.2 Business Intelligence Literature Review
17.3 Methodology
17.3.1 Data Collection Phase
17.3.2 Data Store and Processing Phase
17.3.3 Dataset
17.3.4 Equations
17.3.5 Data Visualization Phase
17.3.6 Scatter Chart
17.3.7 Sparkline
17.3.8 Cross Platform and Device View
17.4 Conclusion
References
18 To Invest or Not to Invest? A Case Study with Decision Analytics on Japanese Yen
18.1 Introduction to Foreign Exchange Market
18.2 Japan and Its PPP
18.3 Features of Japanese Economy
18.4 USD/JPY Currency
18.5 Conclusion
References
19 Broca's Area of Brain to Analyze the Language Impairment Problem and Behavior Analysis of Autism
19.1 Introduction
19.2 Related Work
19.3 Relation Between Language Processing and Brain
19.4 Functionality of Broca's Area
19.5 Language Impairment Problem Solution
19.5.1 Medical Checkup
19.5.2 Language Therapy
19.5.3 Home Care Options
19.5.4 Psychological Therapy
19.6 Methodology
19.7 Dataset
19.8 Results and Discussion
19.8.1 Behavior Analysis Result
19.9 Comparison
19.10 Conclusion
References
Index
