Praise for Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World
Analytics is too often treated as a subject where the cleverness of computers generates the best answers. Fiona and Carlos return the focus to the cleverness of people, providing practical guidance about dealing with unknowns. Their text, with entertaining examples, puts the art back into the science of data mining.
Gordon S. Linoff, Founder, Data Miners, Inc., Author of Data Mining Techniques for Marketing, Sales, and Customer Relationship Management and Data Analysis Using SQL and Excel (Wiley)
With all the hype about big data, it s refreshing to find a book that discusses the practical aspects of analytics. Heuristics in Analytics makes a clear case for adding human experience and common sense to technology in order to solve real world business problems. Written in clear, non–mathematical language, the book explains how using heuristics together with analytics is often the fastest way to deliver decisions that are suitable for a specific use case, and quickly enough to fit the fast pace of business. The descriptions of heuristics concepts and guidance for how to use a heuristic approach to analytics should make this book a valuable addition to the manager s and practitioner s libraries.
Sue Feldman, Synthexis, Author of The Answer Machine
Heuristics in Analytics will provide patient readers with a second–to–none peek under the analytical tent. Analytics is no longer a black box as Dr. Pinheiro and Ms. McNeill describe with surgical precision processes associated with defining problems, describing scenarios, developing and deploying models and selecting appropriate tools and techniques. The case studies are powerful and illuminating. There is much truth talking in these pages.
Thornton May, Futurist & Author of The New Know: Innovation Powered by Analytics
About the Authors xxiii
Chapter 1: Introduction 1
The Monty Hall Problem 5
Evolving Analytics 8
Chapter 2: Unplanned Events, Heuristics, and the Randomness in Our World 23
Heuristics Concepts 26
The Butterfly Effect 30
Random Walks 37
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
Chapter 4: The Analytical Approach 69
Introduction to Analytical Modeling 71
The Competitive–Intelligence Cycle 74
Chapter 5: Knowledge Applications That Solve Business Problems 101
Customer Behavior Segmentation 102
Collection Models 106
Insolvency Prevention 113
Fraud–Propensity Models 120
Chapter 6: The Graph Analysis Approach 129
Introduction to Graph Analysis 130
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
Chapter 8: Text Analytics 191
Text Analytics in the Competitive–Intelligence Cycle 193
Linguistic Models 198
Text–Mining Models 200
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.