Rights Contact Login For More Details
More About This Title Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World
English
Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more.
Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to:
- Realize just how random the world is, and how unplanned events can affect analysis
- Integrate heuristic and analytical approaches to modeling and problem solving
- Discover how graph analysis is applied in real-world scenarios around the globe
- Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more
- Understand how text analytics can be applied to increase the business knowledge
Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.
English
CARLOS ANDRE REIS PINHEIRO is Visiting Professor at KU Leuven, Belgium. He headed the Analytical Lab at Oi in Brazil, one of the largest telecommunications companies in Latin America. Pinheiro has conducted Postdoctoral Research at Katholieke Universiteit Leuven, Belgium, Université de Savoie, France and Dublin City University, Ireland. He holds a PhD in Engineering from Federal University of Rio de Janeiro, Brazil. He worked at Brazil Telecom for almost ten years and also accomplished postdoctoral research at IMPA, Brazil, one of the most prestigious mathematical institutions in the world. He has published several papers in international journals and conferences and has four books (all in Portuguese) that focus on the internet, database, web warehousing, and analytical intelligence. He is the author of Social Network Analysis in Telecommunications, published by Wiley.
FIONA McNEILL has applied analytics to business problems since she began her career in 1992 and has consistently helped companies benefit from strategic use of data and analytics. Throughout her career, she has been affiliated with data and technology companies, from information and survey providers, IBM Global Services and for over fifteen years, at SAS. McNeill has published in academic journals, conducted education seminars and presented at both academic and industry conferences over the course of her career. She holds an M.A. in Quantitative Behavioral Geography from McMaster University, and graduated cum laude with a B.Sc. in Bio-Physical Systems, University of Toronto.
English
Preface xi
Acknowledgments xix
About the Authors xxiii
Chapter 1: Introduction 1
The Monty Hall Problem 5
Evolving Analytics 8
Summary 18
Chapter 2: Unplanned Events, Heuristics, and the Randomness in Our World 23
Heuristics Concepts 26
The Butterfly Effect 30
Random Walks 37
Summary 44
Chapter 3: The Heuristic Approach and Why We Use It 45
Heuristics in Computing 47
Heuristic Problem-Solving Methods 51
Genetic Algorithms: A Formal Heuristic Approach 54
Summary 67
Chapter 4: The Analytical Approach 69
Introduction to Analytical Modeling 71
The Competitive-Intelligence Cycle 74
Summary 97
Chapter 5: Knowledge Applications That Solve Business Problems 101
Customer Behavior Segmentation 102
Collection Models 106
Insolvency Prevention 113
Fraud-Propensity Models 120
Summary 127
Chapter 6: The Graph Analysis Approach 129
Introduction to Graph Analysis 130
Summary 143
Chapter 7: Graph Analysis Case Studies 147
Case Study: Identifying Influencers in Telecommunications 149
Case Study: Claim Validity Detection in Motor Insurance 162
Case Study: Fraud Identification in Mobile Operations 178
Summary 188
Chapter 8: Text Analytics 191
Text Analytics in the Competitive-Intelligence Cycle 193
Linguistic Models 198
Text-Mining Models 200
Summary 207
Bibliography 209
Index 217
