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

by Reis Pinheiro, Carlos Andre; McNeill, Fiona

• ISBN13: 9781118347607
• ISBN10: 1118347609
• Edition: 1st
• Format: Hardcover
• Copyright: 2014-03-03
• Publisher: Wiley

Summary

A practical guide to deploying mathematical and statistical models when performing analytics The Heuristics in Analytics describes analytic processes and how they fit into the heuristic world around us. In spite of the strong heuristic characteristics of the analytical processes, this important book emphasizes the need to have the proper tools to engage analytics. It describes the analytical process from the exploratory analysis in respect to business scenarios and corporate environments, to model developments; and from statistics, probability, stochastic, mathematics, and artificial intelligence; to the deployments and possible outcomes. Describes the overall analytical process in terms of modeling, deployment, and application Presents distinct analytical approaches such as statistical, probabilistic, stochastic, and mathematical Offers a new perspective of the randomness in analytical modeling Includes case studies on the entire analytical process using telecom companies based in Brazil, Ireland, Turkey, United Sates, and Canada Randomness holds a strong impact in the everyday world. It makes sense, then, that analytics are put in place to understand business occurrences, marketplace scenarios, and consumer behavior. The Heuristics in Analytics uniquely shows how random events on a daily basis might completely change expectations, predictions, and behaviors, particularly in corporate environments, and how companies can build a proper analytical strategy to diminish the effect of randomness in business actions.

Author Biography

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.

Table of Contents

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