The guide to targeting and leveraging business opportunities using big data & analyticsBy leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments.The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.* Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics* Offers the results of research and the author's personal experience in banking, retail, and government* Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business* Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysisFor organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Preface xiiiAcknowledgments xvChapter 1 Big Data and Analytics 1Example Applications 2Basic Nomenclature 4Analytics Process Model 4Job Profiles Involved 6Analytics 7Analytical Model Requirements 9Notes 10Chapter 2 Data Collection, Sampling, and Preprocessing 13Types of Data Sources 13Sampling 15Types of Data Elements 17Visual Data Exploration and Exploratory Statistical Analysis 17Missing Values 19Outlier Detection and Treatment 20Standardizing Data 24Categorization 24Weights of Evidence Coding 28Variable Selection 29Segmentation 32Notes 33Chapter 3 Predictive Analytics 35Target Definition 35Linear Regression 38Logistic Regression 39Decision Trees 42Neural Networks 48Support Vector Machines 58Ensemble Methods 64Multiclass Classification Techniques 67Evaluating Predictive Models 71Notes 84Chapter 4 Descriptive Analytics 87Association Rules 87Sequence Rules 94Segmentation 95Notes 104Chapter 5 Survival Analysis 105Survival Analysis Measurements 106Kaplan Meier Analysis 109Parametric Survival Analysis 111Proportional Hazards Regression 114Extensions of Survival Analysis Models 116Evaluating Survival Analysis Models 117Notes 117Chapter 6 Social Network Analytics 119Social Network Definitions 119Social Network Metrics 121Social Network Learning 123Relational Neighbor Classifier 124Probabilistic Relational Neighbor Classifier 125Relational Logistic Regression 126Collective Inferencing 128Egonets 129Bigraphs 130Notes 132Chapter 7 Analytics: Putting It All to Work 133Backtesting Analytical Models 134Benchmarking 146Data Quality 149Software 153Privacy 155Model Design and Documentation 158Corporate Governance 159Notes 159Chapter 8 Example Applications 161Credit Risk Modeling 161Fraud Detection 165Net Lift Response Modeling 168Churn Prediction 172Recommender Systems 176Web Analytics 185Social Media Analytics 195Business Process Analytics 204Notes 220About the Author 223Index 225