Predictive Analytics Times
EXCLUSIVE HIGHLIGHTS
Wise Practitioner – Predictive Analytics Interview Series: Anna Kondic at Merck
 In anticipation of her upcoming conference presentation at Predictive...
Automation and Its Impact on Predictive Analytics – The Increasing Importance of the Hybrid-Part 3
 In my last article, I discussed the increasing impact...
Ten Things Everyone Should Know About Machine Learning
 This article originally appeared as an answer on Quora....
Wise Practitioner – Predictive Analytics Interview Series: Lukas Vermeer at Booking.com
 In anticipation of his upcoming conference presentation, Data Alchemy...
15 Steps for Selecting a Talent Assessment Solution that Predicts Business Performance
 Over the past 30+ years, businesses have spent billions...
Wise Practitioner – Predictive Analytics Interview Series: Feras Batarseh at George Mason University – George Washington University
 In anticipation of his upcoming conference presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Anasse Bari, New York University
  Wall Street and the New Data Paradigm In...
Improved Customer Marketing with Multiple Models
 Data miners employ a variety of techniques to develop...
Data Science: Screening by Religion a Blunt Instrument for Security
 This commentary first appeared in the San Francisco Chronicle....
Wise Practitioner – Predictive Analytics Interview Series: Steve Weiss, at LinkedIn
In anticipation of his upcoming conference presentation, The Sprint...
Wise Practitioner – Predictive Analytics Interview Series: Emilie Lavoie-Charland at The Co-operators
 In anticipation of her upcoming conference presentation, Which Predictive...
Doppelganger Discovery: How Baseball Sabermetrics Inspires Predictive Analytics
 This author will present at Predictive Analytics World, Oct 29 –...
Predicting Fraud: Another Not So Easy Task
 As I have stated in previous articles, the most...
Are You Practicing “Bad Data Science” with your Pre-Hire Talent Assessments?
 Talent Analytics uses data gathered from our own proprietary...
Wise Practitioner – Predictive Analytics Interview Series: Leslie Barrett at Bloomberg L.P.
 In anticipation of her upcoming conference presentation, Crowd-Sourcing and...
Why Data Science Argues against a Muslim Ban
 From the perspective of data science, a Muslim ban...
Wise Practitioner – Predictive Analytics Interview Series: Andrew Burt at Immuta
 In anticipation of his upcoming conference presentation, Regulating Opacity:...
Wise Practitioner – Predictive Analytics Interview Series: Feyzi Bagirov at Becker College
 In anticipation of his upcoming conference presentation, Acquisition Funnel...
Wise Practitioner – Predictive Analytics Interview Series: Jack Levis at UPS
 In anticipation of his upcoming keynote conference presentation, UPS’...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Richard Semmes at Siemens PLM
 In anticipation of his upcoming Predictive Analytics World Manufacturing Chicago,...
Wise Practitioner – Predictive Analytics Interview Series: Edward Shihadeh at Auspice Analytics, LLC
 In anticipation of his upcoming conference presentation, How to...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Emily Pelosi at CenturyLink
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Holly Lyke-Ho-Gland and Michael Sims at APQC
 In anticipation of their upcoming conference co-presentation, Change Management...
Wise Practitioner – Predictive Analytics Interview Series: Natasha Balac at Data Insight Discovery, Inc.
 In anticipation of her upcoming conference co-presentation, Identifying Unique...
Wise Practitioner – Predictive Analytics Interview Series: Bryan Bennett at Northwestern University
 In anticipation of his upcoming conference presentation, Cross-Enterprise Deployment: ...
Wise Practitioner – Predictive Analytics Interview Series: David Talby at Atigeo
 In anticipation of his upcoming conference presentation, Semantic Natural...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive Analytics World for...
Book Review: Weapons of Math Destruction by Cathy O’Neil
 Originally published in Analytics Magazine Book: Weapons of Math...
Wise Practitioner – Predictive Analytics Interview Series: Angel Evan at Angel Evan, Inc.
In anticipation of his upcoming conference co-presentation, Identifying Unique...
Wise Practitioner – Predictive Analytics Interview Series: Paul Speaker at The Dow Chemical Company
 In anticipation of his upcoming conference presentation, Creating an...
Wise Practitioner – Predictive Analytics Interview Series: George Iordanescu at Microsoft
 In anticipation of his upcoming conference presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Afsheen Alam at Allstate Insurance
 In anticipation of her upcoming conference presentation, Our Success...
Wise Practitioner – Predictive Analytics Interview Series: Jennifer Bertero at CA Technologies
 In anticipation of her upcoming conference presentation, Redefining Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Michael Dessauer at The Dow Chemical Company
 In anticipation of his upcoming conference presentation, Listening Down...
Wise Practitioner – Predictive Analytics Interview Series: Steven Ulinski at Health Care Service Corporation
 In anticipation of his upcoming conference presentation, Challenges of...
Wise Practitioner – Predictive Analytics Interview Series: Lauren Haynes at The University of Chicago
 In anticipation of her upcoming conference presentation, Data Science...
Wise Practitioner – Predictive Analytics Interview Series: Daqing Zhao at Macy’s
 In anticipation of his upcoming conference presentation, Macy’s Advanced...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference presentation, Combining Inferential...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kevin Zhan at The Advisory Board
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Halim Abbas at Cognoa
 In anticipation of his upcoming conference presentation, Early Screening...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Taylor at HireVue
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Employee Life Time Value and Cost Modeling
 Understanding the Most Expensive Asset Practically every business shares...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Andrew Marritt at OrganizationView GmbH
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Interview with Eric Siegel: Popularizing Predictive Analytics with Song and Dance
  Originally published in l’ADN (in French) Hilarious consultant,...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Sue Lam at Shell
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Case Study: Hotel Occupancy Forecasting’s Big Payoff
 This Predictive Analytics story started with a question as...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Mike Rosenbaum at Arena
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Darryl Humphrey at Alberta Blue Cross
 In anticipation of his upcoming conference presentation, Claim Pattern...
The Evolving State of Retail Analytics in CRM
 The Traditional State The world of retail has undergone...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Feyzi Bagirov at 592 LLC and Harrisburg University of Science and Technology
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Craig Soules at Natero
 In anticipation of his upcoming conference presentation, Using Predictive...
Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall
  In this excerpt from the updated edition of...
Wise Practitioner – Predictive Analytics Interview Series: Ashish Bansal and John Schlerf from Capital One
 In anticipation of their upcoming conference co-presentation, The Quest...
Wise Practitioner – Predictive Analytics Interview Series: Kristina Pototska at TriggMine
 In anticipation of her upcoming conference presentation, 7 Examples...
Wise Practitioner – Predictive Analytics Interview Series: Frédérick Guillot at The Co-operators General Insurance Company
 In anticipation of his upcoming conference presentation, Defining Optimal...
Predictive Analytics vs. Prescriptive Analytics
 We have all heard and seen the diagrams that...
Interview with Prof. Dr. Wil van der Aalst, Eindhoven University of Technology
 Exclusive interview with Prof. Wil van der Aalst who...
Data Story Telling: Bringing Life to Your Data
 There is no doubt that a successful Data Scientist...
Contextual Experience Innovation
  [Title Image Abbreviations: CRM – Customer Relationship Management,...
Are Random Variables a Fact of Life in Predictive Models?
 In some of the more recent literature, discussion has...
Managing Shifting Priorities in Exploratory Data Science Projects
 After working with a client’s data for over three...
Breaking into Analytics: 5 “Musts” for your Career Transition
 In our data-rich society, corporations of all types and...
How Predictive Analytics Can Fuel Innovation for Manufacturing
 Industry leaders like to use the term “culture” to...
Rexer Analytics Data Science Survey – Highlights (New)
  White Paper with 2015 survey results available now....
How Can Predictive Analytics Help Your Bank or Fintech Company?
 Predictive analytics encompasses a powerful set of methods that...
The Role of Feature Engineering in a Machine Learning World
 Artificial Intelligence(AI) continues to be the next great topic...
The Expansive Deployment of Predictive Analytics: 22 Examples
  The future is the ultimate unknown. It’s everything...
Nine Bizarre and Surprising Predictive Insights from Data Science
  Data is the world’s most potent, flourishing unnatural...
The Trick to Predictive Analytics: How to Bridge the Quant/Business Culture Gap
  This article is excerpted from Eric Siegel’s foreword...
Wise Practitioner – Predictive Analytics Interview Series: Robin Thottungal at U.S. Environmental Protection Agency
 In anticipation of his upcoming conference keynote presentation, 21st...
How Hillary for America Is (Almost Certainly) Using Uplift Modeling
  In this article, I provide evidence that Hillary...
Wise Practitioner – Predictive Analytics Interview Series: Miguel Castillo at U.S. Commodity Futures Trading Commission
 In anticipation of his upcoming conference co-presentation, Words that...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions
 In anticipation of his upcoming keynote co-presentation, Picking the...
Exploring the Toolkits of Predictive Analytics Practitioners — Part 2
 Continuing on our discussion from last month on toolkits...
The Danger of Playing It Safe
 Research shows that people tend to be overly risk...
Manufacturing Operations: Machine Learning to Separate Actionable Trends from False Alarms
 Predictive analytics is increasingly becoming the object of value...
Predictive Analytics Basics: Six Introductory Terms and The Five Effects
 Here are six key definitions—and The Five Effects of...
Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management
 In anticipation of his upcoming keynote co-presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming conference presentation, Risk Management...
The Real Reason the NSA Wants Your Data: Predictive Law Enforcement
 The NSA can leverage bulk data collection with predictive...
Wise Practitioner – Predictive Analytics Interview Series: Scott Zoldi at FICO
 In anticipation of his upcoming conference keynote presentation, Fraud...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Klein at Miles & More GMbH
 In anticipation of his upcoming conference co-presentation, Using Predictive...
Book Review: Predictive Analytics for Newcomers and Nontechnical Readers
 The book reviewed in the article, Predictive Analytics: The...
Wise Practitioner – Predictive Analytics Interview Series: Meina Zhou at Bitly
 In anticipation of her upcoming conference presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Shantanu Agrawal at Centers for Medicare & Medicaid Services
 In anticipation of his upcoming conference keynote presentation, Implementing...
Infographic – Discover Predictive Analytics World for Business 2016
 Predictive Analytics World continues to grow – take a...
Exploring the Tool kits of Predictive Analytics Practitioners — Part 1
 Tools, tools, and more tools continue to explode in...
The Power of Data Science for Predictive Maintenance is Only Just Being Tapped
 Future of Automotive Servicing and Preventive Maintenance Several months...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference presentation, Best Practices...
Need a Data Scientist? Try Building a ‘DataScienceStein’
 Organizations are finding that hiring qualified Data Scientists is...
Wise Practitioner – Predictive Analytics Interview Series: Sanjay Gupta at PNC Bank
 In anticipation of his upcoming conference co-presentation, Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Brian Reich, Former Director at The Hive
 In anticipation of his upcoming conference presentation, The Data...
AnalyticOps: A New Organizational Role So Your Company Can Monetize Analytics
 There is no doubt that data science–and predictive analytics–...
Wise Practitioner – Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming conference presentation, Predicting Behavior...
Getting Started with Predictive Analytics – an Interview with Eric Siegel
 Data science and predictive analytics are top of mind...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Sarmila Basu at Microsoft Corporation
 In anticipation of her upcoming conference presentation, Predictive &...
Are Pre-hire Talent Assessments Part of a Predictive Talent Acquisition Strategy?
  Over the past 30+ years, businesses have spent...
Wise Practitioner – Predictive Analytics Interview Series: Dae Park and Vijay D’Souza at Government Accountability Office (GAO)
 In anticipation of their upcoming conference co-presentation, Characteristics for...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of SmarterHQ
 In anticipation of his upcoming conference presentation, The Revolution...
Opportunities and Challenges: Predictive Analytics for IoT
 There is a clear sense in the marketplace today...
Feature Engineering Within the Predictive Analytics Process — Part Two
 In the last article, I discussed the concept of...
HBO Teaches You How to Avoid Bad Science
 Do you know what p-hacking is? John Oliver –...
Jim Sterne’s Book Review of “Predictive Analytics” by Eric Siegel
 Book review originally published in the journal Applied Marketing...
The Big Picture: Today’s Data Analytics Stack
 Enterprises are inundated with data from social, mobile, IoT...
Taking Action on Technical Success: A Fable of Data Science and Consequences
 Note: This story is fiction, but it is based...
Analytics is (often) a Faith-Based Business
 If you follow data science topics in various social...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Chris Labbe at Seagate Technology
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation
 In anticipation of his upcoming conference co-presentation, Personalized Medicine...
Wise Practitioner – Text Analytics Interview Series: Michael Dessauer and Justin Kauhl at The Dow Chemical Company
 In anticipation of their upcoming conference co-presentation, Understanding our...
Women in Data Science
 The field of Data Science is booming, yet comparatively...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Boosting Performance of Machine Learning Models
  People often get stuck when they are asked...
Wise Practitioner – Predictive Analytics Interview Series: Tanay Chowdhury at Zurich North America
 In anticipation of his upcoming conference presentation, Deep Learning...
Feature Engineering within the Predictive Analytics Process — Part One
 What is Feature Engineering One of the growing discussions...
The Executive’s Guide to Employee Attrition
 Much has been written about customer churn – predicting...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference presentation, Data Driven...
Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories
 In anticipation of their upcoming conference co-presentation, Enhancing search...
Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology
 In anticipation of his upcoming conference presentation, Tips and...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference presentation, Using Predictive...
Ghosts in the Data, Constructing Data Entities
 Data Entities are seldom discussed concepts that primarily hide...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Dr. Matteo Bellucci at General Electric
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
HR’s First Predictive Project? Pre-hire Candidate Screening
 Corp recruiters have a very important and difficult job....
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Banks at The Applied Research Laboratory at The Pennsylvania State University
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Text Analytics Interview Series: Frédérick Guillot at Co-operators General Insurance Company
 In anticipation of his upcoming conference presentation, Leveraging Hands...
Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech
 In anticipation of her upcoming conference co-presentation, Utilizing Advanced...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC
 In anticipation of his upcoming Predictive Analytics World for Manufacturing...
5 Common Mistakes Multi-Channel Retailers Make, and How to Avoid Them
  Multi-channel retailers are often finding themselves stuck in...
Three Critical Definitions You Need Before Building Your First Predictive Model
 Portions excerpted from Chapter 2 of his book Applied...
Measurement and Validation: An Often Underrated Aspect within the Predictive Analytics Discipline
 In our Big Data world, software applications and programming...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Improving Word Clouds as Tool for Text Analytics Data Visualization
 Rich Lanza will present Using Letter Analytic Techniques to...
Dr. Data’s Music Video: The Predictive Analytics Rap
 With today’s release of “Predict This!” – the rap...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Mid-Life Journey to Data Science
 Data Science has been hailed as the sexiest job...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
 In anticipation of his upcoming keynote conference presentation, Buy...
What are you Predicting in Customer Retention?
 Customer Retention models are arguably the most valuable models...
Wise Practitioner – Predictive Analytics Interview Series: Ken Elliott at Hewlett Packard Enterprise
 In anticipation of his upcoming conference presentation, Operationalizing Analytics:...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller at Constellation Research
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference presentation, Predicting the...
Hey FinTech, What’s Your Strategy for Leveraging Unstructured Data?
 Financial technology has sparked a global wave of startups...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Raffael Devigus at F. Hoffmann-La Roche AG
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Rebecca Pang at CIBC
 In anticipation of her upcoming conference presentation, Driving the...
Employee Engagement – a Tricky Metric for Predictive Analytics
 Our work focuses on using predictive analytics to decrease...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Daniil Shash at Eleks
 In anticipation of his upcoming Predictive Analytics World for...
The Information Age’s Latest Move: Four Predictive Analytics Developments for 2016
 Originally published in Big Think Prediction is in the...
Why Do We Stop Asking Why?
 I’ve lived through this phenomenon first hand. The environment...
Predictive Analytics and the Internet of Things
 As technology continues to empower our ability to conduct...
Wise Practitioner – Predictive Analytics Interview Series: Mario Vinasco at Facebook
 In anticipation of his upcoming conference presentation, Advanced Experimentation...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Vishwa Kolla at John Hancock Insurance
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence, Part II
 In my last post, “Coefficients are not the same...
Wise Practitioner – Predictive Workforce Analytics Interview Series: John Lee at Equifax Workforce Solutions
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Peter Bull at DrivenData
 In anticipation of his upcoming conference presentation, Predicting Restaurant...
The “Predictive Analytics” FAQ — What’s New in the Updated Edition and Who’s The Book for?
 This is the preface to Eric Siegel’s newly-released Revised...
Wise Practitioner – Predictive Analytics Interview Series: Matt Bentley at CanIRank.com
 In anticipation of his upcoming conference presentation, Predicting Online...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Lisa Disselkamp and Tristan Aubert at Deloitte
 In anticipation of their upcoming Predictive Analytics World for Workforce conference...
The Data Scientist’s Dilemma: Does Skipping Breakfast Kill You?
 Would skipping breakfast kill you? Not necessarily—but confusing correlation and causation...
Predictive Analytics Can Help with the Challenges Facing Manufacturing in the 21st Century
 Historically, data and analytics have been key to the...
Wise Practitioner – Predictive Analytics Interview Series: Nate Watson at Contemporary Analysis
 In anticipation of his upcoming conference presentation, Predictive Sales...
Customer Experience Predictions for 2016
 As we look ahead and see 2016 unfurling in...
Predictive Analytics Book Excerpt: Hands-On Guide—Resources for Further Learning
 Here is the Hands-On Guide that appears at the...
Wise Practitioner – Predictive Analytics Interview Series: Hans Wolters at Microsoft
 In anticipation of his upcoming conference presentation, Predicting User...
Machine Learning: Not Necessarily a New Phenomenon in Predictive Analytics
 One of the more recent topics gaining traction in...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Netflix, Dark Knowledge, and Why Simpler Can Be Better
 Weary from an all-night coding effort, and rushed by...
The Case Against Quick Wins in Predictive Analytics Projects
 When beginning a new predictive analytics project, the client...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jason Noriega at Chevron
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Matthew Pietrzykowski at General Electric
 In anticipation of his upcoming conference co- presentation, Advanced Analytics...
B2B Predictive Analytics: An Untapped Sector
 Much work in predictive analytics and data science has...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Greg Tanaka at Percolata
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Michael Li at The Data Incubator
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Four Ways Data Science Goes Wrong and How Test-Driven Data Analysis Can Help
 If, as Niels Bohr maintained, an expert is a...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence
 When we build predictive models, we often want to...
Oracle’s Ten Enterprise Big Data Predictions for 2016
 Companies big and small are finding new ways to...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part III: The Expert
 So you have gathered your data and completed your...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank
 In anticipation of her upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Mobile Analytics-Mining the Visit Experience of the Customer
 Mobile technology as part of the Big Data discussion...
The Devil’s Data Dictionary – Making Fun of Big Data
 Buy it on Amazon When Stéphane Hamel coined the...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Waber of Humanyze
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
The Quest for Unicorns
 Will there be enough data scientists in the future?...
Most Swans are White: Living in a Predictive Society
 In anticipation of the forthcoming Revised and Updated, paperback...
Hiring? Approving Mortgages? It’s the Same Thing
 Imagine that Chris wants to buy a house and...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part II: The Skeptic
 So you have gathered your data and completed your...
5 Types of Analytics in Business: One to Go After and One to Avoid
 I have been lucky enough to work in some...
The Beginner’s Guide to Predictive Workforce Analytics
 Human Resources Feels Pressure to Begin Using Predictive Analytics...
Predictive Modeling Forensics: Identifying Data Problems
 Excerpted and modified from Chapters 3 and 4 of...
Five Wins for Retail with Predictive Analytics
 We’ve heard a lot about how big data is...
Faster Credit Scoring Dev With Specialized Binning Code – R Package
 Introduction One of the main concerns in a credit...
Good Predictions != Good Decisions
 A Fateful Tale Ted is having a rough week...
Using Predictive Analytics to Bring Retailers Closer to Their Customers
 Based on the amount of retailers that have been...
Visualization: Panacea for Building Analytics Solutions?
 Data,data,data everywhere and what do I do with it....
Five Challenges in Using Predictive Analytics to Improve Patient Outcomes
 In the increasingly patient-centric world of healthcare, predictive analytics...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part I: The Early Adopter
 So you have gathered your data and completed your...
Five Ways Predictive Analytics Will Shape the Future of Advertising
 Predictive analytics sounds almost mystical, and in a way,...
Five Ways Predictive Analytics Can Improve Patient Outcomes
 The use of analytics in healthcare is gaining momentum...
Wise Practitioner – Predictive Analytics Interview Series: Scott Lancaster at State Street Corp.
 In anticipation of his upcoming conference presentation, Predictive Analytics...
Can Employee Development Lead to Business Mediocrity?
 Our predictive workforce assignments yield staggering results; saving /...
Empathy and Data Science: A Fable of Near-Success
 Editor’s Note: While the story is fiction, the events...
Wise Practitioner – Predictive Analytics Interview Series: Jeff Butler at IRS Research, Analysis, and Statistics organization
 In anticipation of his upcoming conference presentation, The Changing...
A Look at How Big Data is Changing Sports on the Field and in the Press Box
 While major rules rarely change, everything else about professional...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Satyam Priyadarshy at Halliburton
 In anticipation of his upcoming conference presentation, Challenges in...
Automation: Friend or Foe to the Predictive Analytics Practitioner
 Technologies and Big Data continue to bombard our working...
Winning Roles: Moneyball 2.0, for your Hiring and Succession Planning Processes
 Business can learn a lot from sports in terms...
Wise Practitioner – Predictive Analytics Interview Series: Werner Britz at RCS Group
 In anticipation of his upcoming conference presentation, Recoveries: External...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Michael Dulin, Carolinas Healthcare System
 In anticipation of his upcoming keynote conference presentation at...
Wise Practitioner – Predictive Analytics Interview Series: Benjamin Uminsky, Los Angeles County
 In anticipation of his upcoming conference presentation, Mining the...
Wise Practitioner – Predictive Analytics Interview Series: Jessica Taylor of St. Joseph Healthcare
 In anticipation of her upcoming conference co-presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: COL William Saxon, Department of the Army
 In anticipation of his upcoming conference presentation, From Wisdom...
Defensive Data Science: What we can Learn from Software Engineers
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Wise Practitioner – Predictive Analytics Interview Series: Patty Larsen, Co-Director, National Insider Threat Task Force
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Wise Practitioner – Predictive Analytics Interview Series: Bin Mu at MetLife
 In anticipation of his upcoming conference presentation, Establishing Value:...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor
 In anticipation of his upcoming conference presentation, Picking the...
Wise Practitioner – Predictive Analytics Interview Series: Catherine Templeton, PAWGOV Keynote Speaker
 In anticipation of her upcoming keynote conference presentation, Reforming...
Wise Practitioner – Predictive Analytics Interview Series: William Wood of St. Joseph Healthcare
 In anticipation of his upcoming conference co-presentation at Predictive...
The Key to Modelling Success-The Variable Selection Process (Part 2)
 Last month, I discussed the importance of variable selection...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference presentation, Predicting Behavioral...
Wise Practitioner – Predictive Analytics Interview Series: Scott Jelinsky of Pfizer, Inc.
 In anticipation of his upcoming conference presentation at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Chris Franciskovich at OSF Healthcare System
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Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien at Paychex
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Wise Practitioner – Predictive Analytics for Healthcare Interview Series: Daniel Chertok at NorthShore University HealthSystem
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Wise Practitioner – Predictive Analytics Interview Series: Herman Jopia of American Savings Bank
 In anticipation of his upcoming conference presentation, Driving Superior...
Stop Hiring Data Scientists Until You’re Ready for Data Science
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How to manage projects in Predictive Analytics
 In the previous five years, the analytical scene has...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan of Cicero Group
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Defining Measures of Success for Cluster Models
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Good luck placing Analytics in an org chart
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Retail Predictive Analytics Solves the Missing Link in Cross Selling, Up Selling, and Suggestive Selling
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Sameer Chopra’s Hotlist of Training Resources for Predictive Analytics
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Wise Practitioner – Predictive Analytics Interview Series: John Smits of EMC
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Be a Data Detective
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Predicting Employee Flight Risk: My Take
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1 year ago
Feature Engineering within the Predictive Analytics Process — Part One

 

What is Feature Engineering

One of the growing discussions and debates within the data science community is the determination of inputs or variables that should be included in any predictive analytics algorithm. This type of process is more commonly referred to as feature engineering. Historically, this process is typically the most time-consuming element in building any predictive analytics solution as the practitioner can usually create hundreds of variables that might be considered in a predictive model. But what is involved in this process. It is not simply the consumption of all this information into a data lake and then simply inputting this information into a predictive model. Instead, we might think of feature engineering as consisting of two key components. The first component is the creation and derivation of fields/variables from raw data while the second component is a filtering-out process that identifies the set of variables to be considered within a predictive analytics solution. In this article, we will focus on the first component which is the creation and derivation of fields and/or variables. The next article on feature engineering will focus more on the techniques that are used in filtering out variables.

Within the first component, there are a number of stages that are involved in this “feature engineering” process. The first stage is the extraction stage which has changed quite dramatically in our Big Data world as new capabilities are required to extract data beyond just the traditional structured data environment . The ability to read in simple rows and columns of data from structured data has now evolved to the extraction of meaningful information from social media posts,images, sensor data,etc. Although the extraction process has evolved into higher levels of complexity from a technical perspective, the approach to extracting the right information is no different than the extraction of the right information within the more traditional structured data environment. The practitioner needs to understand the business problem and to then identify the critical information that is required to potentially solve the business problem. At this point, the practitioner has the necessary data elements required to solve the business problem. But as stated above, this is only the first stage. It is the second stage of this process where most of the actual data mining/data science work is done in building a predictive model. Intensive data manipulation is conducted against these extracted data elements in order to convert and transform this information into meaningful variables. Arguably, this is the most critical component in the entire predictive analytics exercise. Here the data scientist relies on their knowledge of data structure and linkages as well as their understanding of the business and the underlying business challenge or problem. Let’s cite a few examples in both the structured and semi-structured/unstructured to better illustrate the tasks involved in this stage.

Feature Engineering within the Structured World

In the structured world, many different tables and/or files could be identified as the source data from the extraction process. Under this scenario, the data scientist needs to understand what files to link and how to link them. For example, how are the files linked? Are they one to one, one to many, or many to many. Once the linkage approach between files has been determined, the data scientist can then derive potentially hundreds of variables from the source extracted data. Routines are written to generate the following type of variables:

  • Summary and ratio variables around given behaviours and characteristics
  • Binary and discrete variables that indicate the occurrence of some kind of event and/or activity.
  • Identification of the target variable to be predicted.
  • Change variables which look at how behavior and/or activity is changing overtime.

Typically, the routines to generate these above type of variables represent the most laborious part of the model-building exercise. But at the end of this process, an analytical file is created where a dependant or target variable is created alongside hundreds of independent variables.

Feature Engineering in Semi-Structured Data/Unstructured Data

In semi-structured data, the practitioner looks at the non-text data or meta data which relates to the characteristics of that event. For example , in twitter feeds, information relates to when the feed occurred, the number of followers(who you are following vs. who is following you), type of device, what URL they came from , location, etc. Our challenge with this data is to identify the unique person or the record of interest that will be ultimately used in creating the analytical file. Once this information has been identified, the derivation routines described above can then be employed to create twitter-related variables. The same sort of approach can also be applied to other types of social media such as Linked-In, Facebook, Youtube, etc. The critical fields, though, in building any analytical file using meta data within this medium are the unique ID that relates to a unique user and of course the time stamp pertaining to the occurrence of this event. It is this ability to identify the record of interest or the user ID along with when that event occurred which is the key to building powerful solutions that might be used in a predictive analytics solution.

Besides social media data, sensor data represents another source of semi-structured data that is relatively new to the information arsenal of the predictive analytics practitioner. Within the mobile world, information is collected around the device if the phone is WIFI enabled. Each phone has a unique ID which is referred to as the MACID and information can now be collected around this unique ID. If I am at a restaurant and my phone is WIFI enabled, information pertaining to where I am sitting(i.e. distance from the router),when I both entered and exited the restaurant, as well as the specific restaurant can all be collected around this specific event . As long as the phone is unique to me, the MACID in a way represents a type of unique customer ID. New customer behavior about me is now gathered through my movements which are now being tracked through my phone. An analytical file can now be built using the MACID as the record of interest in creating all the derived behavioral variables. Examples of this rich behavior are listed below:

  • How often do I go that restaurant
  • When was the last time that I went to that restaurant
  • What other restaurants within that chain do I visit
  • What days and time of day am I most likely to visit that restaurant

Besides mobile data, we are increasingly seeing the use of more sensor type data in predictive analytics solutions particularly within manufacturing processes. The Internet of Things as more and more devices become digitally enabled will just add to the potential of using sensor information to create meaningful variables in any predictive analytics solution.

At this point, we have discussed feature engineering in both structured and semi-structured environments. Although, some might argue that sensor data is unstructured, the discussion above is referring to the use of the meta data where the data can be contained in some semi-structured type format. Most unstructured data, though, is often discussed within the realm of text mining and text analytics. Emails, phone calls,tweets,facebook posts all represent typical forms of unstructured data that can leverage the utilization of text mining techniques. Here, text mining tools and processes are applied to this unstructured data where the practitioner or data scientist is attempting to identify themes or topics from the text data. At the end of this process, each individual is then assigned to the topic or theme based on the content of the communication with the end result being variables that can potentially be used in a model.

The Filtering-Out Process as the Next Topic of Discussion

We all know that the development of predictive analytics solutions is not simply trying to find the optimum machine learning or mathematical algorithm. The recognition of what you input as variables is arguably the most critical factor to success presuming that the business problem has been properly identified. In this discussion, we have attempted to summarize some key points when going through this process of creating the right variables which is the first c omponent of feature engineering. But besides the creation of these variables, the ability to filter out certain variables is the second component within feature engineering that will optimize the input variables into any predictive analytics solution. This will be discussed in the next article.

Author Bio:

Richard Boire, B.Sc. (McGill), MBA (Concordia), is the founding partner at the Boire Filler Group, a nationally recognized expert in the database and data analytical industry and is among the top experts in this field in Canada, with unique expertise and background experience. Boire Filler Group  was recently acquired by  Environics Analytics where I am currently senior vice-president.

Mr. Boire’s mathematical and technical expertise is complimented by experience working at and with clients who work in the B2C and B2B environments. He previously worked at and with Clients such as: Reader’s Digest, American Express, Loyalty Group, and Petro-Canada among many to establish his top notch credentials.

After 12 years of progressive data mining and analytical experience, Mr. Boire established his own consulting company – Boire Direct Marketing in 1994. He writes numerous articles for industry publications, is a well-sought after speaker on data mining, and works closely with the Canadian Marketing Association on a number of areas including Education and the Database and Technology councils. He is currently the Chair of Predictive Analytics World Toronto.

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