Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World

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, its 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 managers and practitioners 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

Preface Acknowledgments About the Authors Chapter 1: Introduction The Monty Hall Problem Evolving Analytics Summary What Comes Next Chapter 2: Unplanned Events, Heuristics and the Randomness in Our World Heuristics Concepts The Butterfly Effect Random Walks Summary Chapter 3: The Heuristic Approach and Why We Use It Heuristics in computing Heuristic Problem solving Methods Genetic Algorithms: a Formal Heuristic Approach Summary Chapter 4: The Analytical Approach Introduction to Analytical Modeling The Competitive Intelligence Cycle Summary Chapter 5: Knowledge Applications That Solve Business Problems Customer Behavioral Segmentation Collection Models Insolvency Prevention Fraud Propensity Models Summary Chapter 6: The Graph Analysis Approach Introduction to Graph Analysis Summary Chapter 7: Graph Analysis Case Studies Case Study: Identifying Influencers Case Study: Claim Validity Detection Case Study: Fraud Identification Summary Chapter 8: Text Analytics Text Analytics in the Competitive Intelligence Cycle Linguistic Models Text Mining Models Summary Recommended Reading Bibliography   Index

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, Universit 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.