Decision Intelligence Analytics and the Implementation of Strategic Business Management:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2021
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| Schriftenreihe: | EAI/Springer Innovations in Communication and Computing Ser
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (236 Seiten) |
| ISBN: | 9783030827632 |
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| 505 | 8 | |a Intro -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 17 Real-Time Data Visualization Using Business Intelligence Techniques in Small and Medium Enterprises for Making a Faster Decision on Sales Data | |
| 650 | 4 | |a Business-Data processing | |
| 650 | 4 | |a Strategic planning-Data processing | |
| 650 | 4 | |a Decision making | |
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| 700 | 1 | |a Abujar, Sheikh |e Sonstige |4 oth | |
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| author | Jeyanthi, P. Mary |
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| contents | Intro -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC6838893 (ZDB-30-PAD)EBC6838893 (ZDB-89-EBL)EBL6838893 (OCoLC)1291318806 (DE-599)BVBBV048830727 |
| dewey-full | 658.4012 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4012 |
| dewey-search | 658.4012 |
| dewey-sort | 3658.4012 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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Mary</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Decision Intelligence Analytics and the Implementation of Strategic Business Management</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham</subfield><subfield code="b">Springer International Publishing AG</subfield><subfield code="c">2021</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (236 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">EAI/Springer Innovations in Communication and Computing Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">17 Real-Time Data Visualization Using Business Intelligence Techniques in Small and Medium Enterprises for Making a Faster Decision on Sales Data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business-Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strategic planning-Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision making</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Choudhury, Tanupriya</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hack-Polay, Dieu</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, T. 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| id | DE-604.BV048830727 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:35:28Z |
| indexdate | 2024-07-10T09:47:11Z |
| institution | BVB |
| isbn | 9783030827632 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034096305 |
| oclc_num | 1291318806 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (236 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | EAI/Springer Innovations in Communication and Computing Ser |
| spelling | Jeyanthi, P. Mary Verfasser aut Decision Intelligence Analytics and the Implementation of Strategic Business Management Cham Springer International Publishing AG 2021 ©2022 1 Online-Ressource (236 Seiten) txt rdacontent c rdamedia cr rdacarrier EAI/Springer Innovations in Communication and Computing Ser Description based on publisher supplied metadata and other sources Intro -- 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 Business-Data processing Strategic planning-Data processing Decision making Choudhury, Tanupriya Sonstige oth Hack-Polay, Dieu Sonstige oth Singh, T. P. Sonstige oth Abujar, Sheikh Sonstige oth Erscheint auch als Druck-Ausgabe Jeyanthi, P. Mary Decision Intelligence Analytics and the Implementation of Strategic Business Management Cham : Springer International Publishing AG,c2021 9783030827625 |
| spellingShingle | Jeyanthi, P. Mary Decision Intelligence Analytics and the Implementation of Strategic Business Management Intro -- 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 Business-Data processing Strategic planning-Data processing Decision making |
| title | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_auth | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_exact_search | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_exact_search_txtP | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_full | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_fullStr | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_full_unstemmed | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_short | Decision Intelligence Analytics and the Implementation of Strategic Business Management |
| title_sort | decision intelligence analytics and the implementation of strategic business management |
| topic | Business-Data processing Strategic planning-Data processing Decision making |
| topic_facet | Business-Data processing Strategic planning-Data processing Decision making |
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