Analytics Enabled Decision Making

Description

Analytics is changing the landscape of businesses across sectors globally. This has led to the stimulation of interest of scholars and practitioners worldwide in this domain. The emergence of ‘big data’, has fanned the usages of machine learning techniques and the acceptance of ‘Analytics Enabled Decision Making’. This book provides a holistic theoretical perspective combined with the application of such theories by drawing on the experiences of industry professionals and academicians from around the world. The book discusses several paradigms including pattern mining, clustering, classification, and data analysis to name a few. The main objective of this book is to offer insight into the process of decision-making that is accelerated and made more precise with the help of analytics. Foreword Preface Acknowledgements In the Name of God, Most Gracious, Most Merciful Contents Notes on Contributors List of Figures List of Tables Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions” 1 Introduction 1.1 Descriptive Analytics 1.2 Diagnostic Analytics 1.3 Predictive Analytics 1.4 Prescriptive Analytics 2 Decision-Making Framework 2.1 Step 1: Problem Identification 2.2 Step 2: Review Past Data 2.3 Step 3: Data Collection and Data Processing 2.4 Step 4: Model Building 2.5 Step 5: Strategic Implementation 3 Conclusion References Algorithms as Decision-Makers 1 Introduction 2 Literature Review 2.1 The Role of Algorithms in Decision Making 2.2 Two Types of Decision-Based Algorithms 3 Methodology 3.1 Research Design 3.2 Case Study: Wolt 3.2.1 The Relationship of Algorithm Optimization and Courier Partner as DSA: Algorithm Optimization Supporting the Courier Partner in Decision Making 3.2.2 The Relationship of Algorithm Optimization and Courier Partner as DMA: The Algorithm Optimization Decides How the Courier Works 3.2.3 Case Summary 4 Results 5 Discussions 6 Conclusions References Influence of Big Data Analytics on Business Intelligence 1 Introduction 2 Business Intelligence at a Glance 3 Data Warehousing and ETL at Core 3.1 Benefits of a Data Warehouse 3.2 ETL (Extract, Transform and Load) 3.3 Challenges of Conventional BI 4 Evolution of Big Data 4.1 How Did Netflix Get Empowered with Big Data Capabilities? 4.2 How the Cloud Is Making Big Data Adoption Much Easier? 4.3 Big Data Challenges 4.4 Future of Big Data 5 Discussion 6 Conclusion References Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models 1 Introduction 1.1 Motivation 1.2 Prior Art 2 Mathematical Developments 2.1 General Scheme 2.2 Posterior Distributions and Estimation of Worth Parameters 2.2.1 The Posterior Distribution Under Conjugate Prior 2.2.2 The Posterior Distribution Under Dirichlet Prior 3 Simulation-Based Evaluation of the Proposed Generalization 3.1 Elicitation of the Hyper-Parameters 3.2 The Posterior Means: Estimation of Worth Parameters 3.3 The Estimated Preference Probabilities 3.4 Inferential Aspects of the Proposed Generalization 4 Empirical Evaluation: An Application to Smokers’ Choice Data 5 Conclusion References Baseball Informatics—From MiLB to MLB Debut 1 Introduction 2 Problem Statement 3 Methodology 3.1 Exploratory Data Analysis (EDA) 3.2 Variable Selection 3.3 Modeling 4 Results 4.1 Exploratory Data Analysis (EDA) 4.2 Lasso in Variable Selection 4.3 Modeling 4.4 Application 5 Discussion 5.1 Machine Learning and Performance Metrics 5.2 Baseball Stats and Non-Baseball Data 5.3 Recommendations for MiLB Players 6 Limitations 7 Future Research 8 Conclusion References Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics 1 Introduction 2 Artificial Neural Networks (ANN) 3 Background Research and Applications 4 Working Example with ANN 5 Limitations of ANN 6 Conclusion References The Role of Financial Analytics in Decision-Making for Better Firm Performance 1 Introduction 1.1 Financial Analytics 1.2 Customer Profitability Analytics 1.3 Firm Performance Measurements 2 Evolution of BDA 2.1 Business Analytics and Business Intelligence 3 Theoretical Linkages 3.1 Resource-Based View (RBV) Theory 3.2 Dynamic Capability Theory 3.3 Information Processing Theory (IPT) 4 Dimensions of Financial Analytics 4.1 Fixed Assets Analytics 4.2 General Ledger Analytics 4.3 Budgetary Control Analytics 4.4 Corporate Performance Analytics 4.5 Profitability Analytics 4.6 Accounts Payable and Accounts Receivable Analytics 5 Benefits of Financial Analytics 5.1 Management of Cash Flow 5.2 Prediction of Sales 5.3 Identify Profitability from Customer(s) 5.4 Increase Shareholder Value 5.5 Understand Profitability from the Product(s) 5.6 Builds Business Value 6 Technological Support for Financial Analytics 6.1 IoT 6.2 Big Data 6.3 Blockchain 6.4 Artificial Intelligence 7 Financial Analytics—Use Cases 7.1 PolicyBazaar 7.2 Paytm 7.3 HDFC Life Insurance 7.4 ICICI Bank Limited 7.5 Crisil 8 SWOC Analysis of Financial Analytics 8.1 Strengths 8.2 Weakness 8.3 Opportunities 8.4 Challenges 9 Conclusion References Using Analytics to Manage and Predict Employee Performance 1 Introduction 2 Analytics and Case Studies 2.1 The 16 Personality Factor Questionnaire (16PF) 2.1.1 Case Study 2.2 Belbin 2.2.1 Case Study 2.3 Cultural Transformation Tools (CTT) and Cultural Values Assessment (CVA) 2.3.1 Case Study 2.4 DiSC 2.4.1 Case Study 2.5 FIRO Element B (Fundamental Interpersonal Relations Orientation Element B) 2.5.1 Case Study 2.6 Five-Factor Model of Personality (FFM) 2.6.1 Case Study 2.7 Hogan 2.7.1 Case Study 2.8 Job Challenge Profile (JCP) 2.8.1 Case Study 2.9 Life Styles Inventory 2.9.1 Case Study 2.10 Mapping System for Team Coaching 2.10.1 Case Study 2.11 Motivational Questionnaire (MQ) 2.11.1 Case Study 2.12 Multifactor Leadership Questionnaire (MLQ-5X) 2.12.1 Case Study 2.13 Myers-Briggs Type Indicator Questionnaire (MBTI) 2.13.1 Case Study 2.14 Occupational Personality Questionnaire (OPQ32r) 2.14.1 Case Study 2.15 Saville Assessments 2.15.1 Case Study 2.16 StressScan (From Envisia Learning) 2.16.1 Case Study 2.17 Team Management Systems (TMS) 2.17.1 Case Study 2.18 VIA Survey of Character Strengths (VIA Total 24) 2.18.1 Case Study 3 Conclusion References Using Analytics to Manage Employee Behavioural Traits and Predict Employee Performance 1 Introduction 2 Importance of People Analytics for Performance Management 2.1 Employee Engagement and Performance Management Metrics 3 Predictive Models for Performance 3.1 Objective 1: To Analyze the Factors Predicting the Performance1 of the Employees 3.2 Objective 2: To Analyze the Factors Predicting Performance2 of the Employees 4 The Way Ahead References Platform Business Model for Intelligent Supply Chain Operations 1 Introduction 2 Industry 4.0 2.1 Platform Business Models 2.2 MarketPlaces as Platforms Business Models 2.3 Internal Platforms 2.4 External Platform 2.4.1 Different Types of Platforms 2.5 Services as Platform Business Model 2.5.1 Case Study: Government e-Marketplace 2.6 Why is Jio Dreaded by Many Players in India? 2.7 IoT and Platform Economy 2.8 Platform Business and Job Market 2.9 Platform Business Model and Supply Chain Operations 2.9.1 Control Dimensions in PBM 3 Platform Business Models in Enhancing Decision Making 3.1 Implications and Practical Aspects 4 Conclusion References The Role of Consumption in the Identity Formation of Conservative Women: A Web Analytics and Netnographic Exploration 1 Introduction 2 Web Analytics as an Out of the Box Research Method 2.1 A Data Collection Method from Online Communities: Netnography 2.2 A Study on Emerging Consumer Trends of Women in Turkey 3 Methods 4 Analyses and Findings 4.1 Country Rank and Engagement Scores 4.2 Visitor Traffic 4.3 Word Cloud Maps 4.4 Thematic Findings 5 Conclusion 6 Limitations and Further Research Suggestions References Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India 1 Introduction 2 Review of Literature 3 Methodology 4 Data Analysis and Results 5 Discussion and Implications 6 Conclusion References Index