Data-Driven Decision Making

• Autore: Poulose Jeanne
• Pubblicazione: Singapore : , : Palgrave Macmillan, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (331 pages)
• Altri autori: SharmaVinod MaheshkarChandan
• Nota di contenuto: Intro -- Contents -- Notes on Contributors -- List of Figures -- List of Tables -- 1 Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions -- Introduction -- The VUCA Context: Examples and Cases -- Volatility -- Uncertainty -- Complexity -- Ambiguity -- Data-Driven Decision Making in the VUCA Context -- Volatility to Visioning -- Uncertainty to Understanding -- Complexity to Clarity -- Ambiguity to Agility -- Tools and Technique's Aiding Organization's Strategic Response -- Conclusion -- References -- 2 Investigation of Predictive Power of Sentiment Analysis Model Developed Using Different Word Embedding Techniques -- Introduction -- Related Work -- Research Methodology -- Datasets -- File Pre-processing -- Word Embedding Techniques -- Dimension Reduction Techniques -- Training vs. Testing Data Split Approach -- Sentiment Classification Models -- SVM Kernel Used -- Framework of the Work -- Performance Evaluation Parameters -- Analysis of the Result -- Data Comparison and Addressing Research Questions Based on the Results -- Conclusion -- References -- 3 A Comparative Analysis of Machine Learning Algorithms for Image Classification: Evaluating Performance -- Introduction -- Machine Learning Algorithms -- Support Vector Machine -- K-Nearest Neighbour -- Naïve Bayes -- Decision Trees -- Random Forest -- Artificial Neural Network -- Application of Machine Learning Algorithms -- Research Methodology -- Sources of Data -- Tools Used -- Analysis and Findings -- Discussion -- Limitations of the Study -- Application of the Research -- Conclusion -- References -- 4 Strategic Data Analytics for Sustainable Competitive Advantage -- Introduction -- Diving into Digital Era-Issues That Drive Digital Transformation -- Structural & -- Economic Reasons-Why Business Analytics? -- The Gap Between Promise and Reality. ; Approaches to Strategic Data Management -- Changing Data Environment and Its Effects for Business -- It All Starts with the Data Strategy -- Analysing Text, Network, Location, and Imagery Data -- The 6 V's of Data and Data Mining -- Impact of Business Analytics Across Different Industries -- Optimizing Analytics for Sustainable Competitive Advantage -- Fostering a Business Analytics Ecosystem -- Resources for Embracing Business Analytics -- Organizational Value Creation with Business Analytics: KPIs -- Business Analytics Tools for Organizations -- Analytics Across the Departments/Divisions in an Enterprise -- Finance and Accounting -- Human Resource Management -- Supply Chain -- Marketing and Sales -- Enterprise Risk Management -- Challenges in Crafting Organizational Analytics Platform -- How Much Data Is the Right Data? -- Identifying and Defining Problems and Opportunities in Business Analytics -- Evaluating Appropriate Analytics Platform -- Implementing and Managing the Enterprise Analytics Project -- Privacy and Security Challenges Associated with Business Analytics -- Conclusion -- References -- 5 Data Mining and Business Intelligence Trends -- Introduction -- Background -- Methodology: Understanding KDD in Data Mining -- Practical Trends of Data Mining -- Techniques for Data Mining -- Types of Data Mining in Business -- Analysis and Discussion: Data Mining vs. Business Intelligence -- Overview of Data Mining Process -- Algorithms and the Business -- Support Vector Machine (SVM) -- Classification in Data Mining -- Artificial Neural Networks -- Machine Learning-based Approach -- Machine Learning-based Approach to Medical Diagnosis -- Association Rule Mining -- Data Mining and Its Working: -- Applications-Real-time Applications of Data Mining in Business Intelligence -- Industrial Applications of Data Mining -- Real-World Case Study of DM and BI. ; Pros of Data Mining -- Advantages of Business Intelligence and Data Mining -- Shortcomings of Data Mining -- Future of Data Mining in Business Intelligence -- Conclusion -- References -- 6 Generational Cohort Analysis to Purchase Fashion Products in India -- Introduction -- Literature -- Fashion Industry in India -- Buying Behaviour of Fashion Products -- Objectives -- Formulation of Hypothesis -- Methodology -- Research Instrument -- Data Collection and Analysis -- Analysis -- Discussion and Implications -- Conclusion -- References -- 7 Marketing Analytics and Consumer Behavior: A Systematic Literature Review for Future Research Agenda -- Introduction -- Research Methodology -- Screening and Selection of Papers -- Data Analysis -- Theoretical Frameworks -- Decision-Making Models -- Marketing Analytics Technique -- Impact on Consumer Behavior -- Ethical Considerations -- Challenges and Future Directions -- Conclusion -- References -- 8 Application of Artificial Intelligence on Smart Tourism Eco Space: An Integrated Approach in Post-COVID-19 Era -- Artificial Intelligence -- Smart Tourism -- Artificial Intelligence in Smart Tourism -- Opportunities of Artificial Intelligence in Smart Tourism -- Challenges of Artificial Intelligence (AI) on Smart Tourism -- Role of Smart Tourism Post-COVID-19 Era -- Digital Ecosystem for the Service Sector -- Design Approaches and Smart Tourism Experiences -- Conclusion -- References -- 9 Measurement Model of CO-PO Attainment in Higher Education: A Simplified Approach -- Introduction and Background of the Study -- Outcome-Based Assessment -- Frame the Program Outcome and Course Outcome -- Methods of Measuring COs -- Assessment Analysis -- Attainment Methodology -- Measurement of CO Attainment -- CO Mapping in Continuous Internal Assessment -- Procedure for Attainment of POs/PSOs -- Conclusion and Discussion. ; References -- 10 Do You Have an AI Mindset? Development and Validation of AI Mindset Questionnaire -- Introduction -- How Are Humans Developing the AI Mindset? -- AI Mindset and Decision-Making -- The Rise of AI Mindset and the Impact on Skills -- Development of a Scale to Measure the AI Mindset in Humans -- Objectives -- Research Methodology -- Research Design -- Operational Definition -- AI Mindset -- Sample Size -- Sampling Technique -- Inclusion Criteria -- Exclusion Criteria -- Measure -- Procedure -- Data Analysis -- Data Screening -- Exploratory Factor Analysis -- Principal Component Analysis -- Factor Interpretation -- Results and Discussion -- Exploratory Factor Analysis -- Reliability -- Principal Component Analysis -- Rotated Component Matrix -- Item Communality -- Factor Labelling -- Results for Part B of the Scale -- Exploratory Factor Analysis -- Reliability -- Principal Component Analysis and Scree Test -- Factor Identification -- Assessment of Communalities -- Factor Labelling -- Results on the Scores -- Conclusion -- Limitations and Future Implications -- References -- 11 HR Analytics: An Indispensable Tool for Effective Talent Management -- Introduction to HR Analytics -- Types of Analytics -- HR Metrics -- Data Sets -- Data Pre-processing -- Descriptive Analysis of Data -- Algorithms -- Accuracy measures -- Result -- Discussion -- Theoretical Implications -- Practical Implications -- Conclusion -- References -- 12 Career in Artificial Intelligence, Machine Learning, and Data Science -- Introduction -- Theoretical Understanding of AI/ML/DS -- The AI Myths -- Opportunities in Data Science, AI, and ML -- Careers in Data Science -- Careers in AI/ML -- Case: AI/ML/DS in Auto Industry -- Data Science Framework and Career in Auto Industry -- Conclusion -- References. ; 13 Harnessing the Power of Big Data Analytics to Transform Supply Chain Management -- Introduction -- Traditional vs Modern Supply Chain Management -- Applications in Digital Supply Chain -- Sources of Data in Supply Chain Management -- Research Methodology -- I Phase: Data collection -- II Phase: Data analysis and synthesis phase -- III Phase: Reporting meta-analysis -- Discussion and Implications -- Theoretical Implication -- Practical Implication -- Conclusion -- Limitations and Future Directions -- References -- Index.
• Titolo autorizzato: Data-Driven Decision Making
• ISBN: 9789819729029; 9789819729012
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878055003321
• Lo trovi qui: Univ. Federico II