Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysisthat accounts for the randomness of business and the competitivemarketplace, creating a model that more accurately reflects thescenario at hand. With an emphasis on the importance of properanalytical tools, the book describes the analytical process fromexploratory analysis through model developments, to deployments andpossible outcomes. Beginning with an introduction to heuristicconcepts, readers will find heuristics applied to statistics andprobability, mathematics, stochastic, and artificial intelligencemodels, ending with the knowledge applications that solve businessproblems. Case studies illustrate the everyday application andimplication of the techniques presented, while the heuristicapproach is integrated into analytical modeling, graph analysis,text analytics, and more.
Robust analytics has become crucial in the corporateenvironment, and randomness plays an enormous role in business andthe competitive marketplace. Failing to account for randomness cansteer a model in an entirely wrong direction, negatively affectingthe final outcome and potentially devastating the bottom line.Heuristics in Analytics describes how the heuristiccharacteristics of analysis can be overcome with problem design,math and statistics, helping readers to:
- Realize just how random the world is, and how unplanned eventscan affect analysis
- Integrate heuristic and analytical approaches to modeling andproblem solving
- Discover how graph analysis is applied in real-world scenariosaround the globe
- Apply analytical knowledge to customer behavior, insolvencyprevention, fraud detection, and more
- Understand how text analytics can be applied to increase thebusiness knowledge
Every single factor, no matter how large or how small, must betaken into account when modeling a scenario or event—even theunknowns. The presence or absence of even a single detail candramatically alter eventual outcomes. From raw data to finalreport, Heuristics in Analytics contains the informationanalysts need to improve accuracy, and ultimately, predictive, anddescriptive power.
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.
