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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2 weeks ago
15 Steps for Selecting a Talent Assessment Solution that Predicts Business Performance

 

Over the past 30+ years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates.  Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments?  As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.

How Can You Know – If It is It a Real Predictive Solution vs. Marketing Fluff

What is a Real Predictive Solution?

For all of the marketing hype, Predictive Analytics boils down to three very simple steps.

  • 1st, a system reads “input” data – perhaps assessment scores or CV information.
  • 2nd, the system does some math to apply a “predictive model” to the input data.
  • Finally, the results of the model are shown as “output” data of the model – perhaps the likelihood of the candidate achieving a certain level of Sales Performance or another KPI. At heart, it takes “inputs” and turns them into “outputs” or predicted business outcomes.  But to build and validate a model, you need a healthy, logical set of both input and output data for that role in your company.

If you are using a talent assessment alone this is just input data.  The talent assessment is just one piece of the system.  There are 2 more pieces (see above).

For most companies, their current pre-hire talent assessments are wasted data.  Results are delivered in an individual report that cannot be analyzed or aggregated.  For most “legacy” talent assessments, it’s difficult or impossible to determine what positive (or negative) business affect the assessments are having.  It often comes down to the question of “how much the HR person believes the results”.  This is a bad measure of success.

http://www.personneltoday.com/hr/psychometrics-evolution-how-testing-has-won-over-hr/

But it doesn’t have to be that way.  At Talent Analytics, we include talent assessment data, generated from our own proprietary assessments, as an additional data point in every predictive project.  In predictive-speak, “our assessment data has proven to be a very strong independent variable for our predictive models”.  We repeatedly prove that our Talent Analytics scores, predict business performance, such as the probability of someone making their sales quota, or the probability of someone lasting in a contact center role for at least 12 months, the probability of a truck driver making accidents . . . and so on for most quantifiable KPIs.

If you’d like to begin a predictive talent acquisition project using talent assessments, it can be daunting to figure out what solutions are smoke and mirrors, and what solutions will actually deliver a predictive solution.

To help, I wanted to share important factors to consider to help you sort through “pretend predictive solutions” and “real, rigorous predictive solutions” that can deliver significant bottom line results.  This decision can dramatically affect your business’s bottom line.  It’s important.

Criteria for Selecting a Predictive Talent Assessment Solution

 1.   The Predictive Company Itself

Are you dealing with an assessment company, who is trying to learn how to be predictive? Or is it a predictive company that also uses assessment data?  How long have they been doing predictive work?  Are they invited to speak at predictive conferences or at basic HR conferences?

2.   Their Predictive Team

Ideally the company will have Data Scientists on staff as well as IO Psychologists.  This is important because Data Scientists tend to utilize more modern and rigorous methods for prediction and validation.  IO Psychologists tend to be focused on the instrument, while Data Scientists tend to be concerned with predictive validity and business results.

3.   Are They Predicting For Your Company, or For Everyone?

There are companies that create “Industry Benchmarks,” that is, a general performance predictions for general industry categories – such as Retail Sales or Customer Service. These predictions are significantly less accurate, because they are based on companies different from your own, with different cultures, goals, and regions.  Not all “Customer Service” is the same.  Modern computing methods enable leading providers to create and validate predictive models for your roles in your own company alone, and to continuously update the model over time.

4.   Do They Care About Your Outcome Data?

Generally these solutions predict attrition or performance for a candidate or employee. Has the assessment company asked you for the attrition or KPI data for your employees in your target role?  If they don’t know your employee outcomes, how can they predict your outcomes?  They can’t.

Most job roles have multiple KPIs that describe performance – do they predict each of these separately?  For KPIs that naturally contradict each other, e.g. speed vs. accuracy, how does the predictive solution resolve the contradiction?  Just getting a “green light” isn’t good enough in many cases.

What sample size did they ask for?  Real predictions require a reasonable sample to properly validate that you aren’t being fooled by randomness.  If they only ask for 15 top performers, your sample is too small to create a real prediction.

5.   Does the Solution Base Predictions on Outcome Data or a Job Fit, Job Match or Job Blueprint Survey?

Data Science predicts what you ask it to predict. If you want lower attrition or higher KPIs, the models must be trained and validated with those data alone.  The process looks for fact-based patterns to drive your business.

Surprisingly, many solutions don’t use this approach, but fall back to managerial bias.  These solutions ask well-meaning committees of managers to list competencies that they believe are needed for success in a role.  The resulting criteria are not predictive at all – they just find candidates that match the laundry list of beliefs and biases held by that committee.  Nowhere in this process is a connection to actual attrition or KPI outcomes.  Again, if the system doesn’t know about your outcomes, how can the process predict them?  Start with data, not bias.

6.   Does the Solution Use Machine Learning to Recalibrate Your Predictive Models? How Often?

Business needs, role descriptions, and culture changes over time.  Local labor conditions change.  For example, Service Representatives may be incentivized to cross-sell related products, or new regulations may require new compliance to be performed.  It is important to update and re-validate your predictive models 2-4 times a year to keep up to date with seen and unseen trends.  Some solutions have not changed their models for 30 years – do you expect these to find great sales reps for you?

7.   The New Validation Question: Criterion Validation?

HR has been taught to ask if the assessment is validated.  The first level of validation checks whether the assessment measures are self-consistent.  Continue to ask this question.

But ultimately you care about whether the assessment feeds predictions that accurately correspond to improved business outcomes.  That is, are the predictions actually working?  This level is called “Criterion Validation” and is a high bar that is not commonly reached by vendors.

A top tier predictive talent assessment vendor will perform Criterion Validation for the solutions several times a year – with every client. Criterion validation is the highest level of validation possible, and is the most preferred by regulatory agencies.

8.   Can You Easily Access / Download Your Company’s Talent Assessment Data

Talent assessment data is a critical dataset for your company. If your Talent Assessment vendor makes it difficult or impossible to access your talent assessment data – this is a good indication they are using pre-predictive technology and that they don’t appreciate that this data is your asset.

True predictive solutions know that your workforce data scientists will want to use your talent assessment data to find correlations and predictions in many areas of your business. You need to insist on easy and direct access to the raw assessment scores.

9.   How Easy is it to Deploy the Solution into the Talent Acquisition Process and Use the Predictions

How much training is required? Do your talent acquisition professionals need to read long text reports, or get out a calculator to use the predictions?  The complexity of a prediction should be kept out of the way of daily operations.

If your team still needs to “think” about what the answer is, it is probably not a predictive solution.

10.  Is there a Different Assessment for Every Role? Or 1 Assessment with Multiple Predictive Models?

Multiple assessments make it impossible to predict one candidate’s performance against multiple roles.  This may also be a signal that you are working with an older, legacy (less predictive) talent assessment supplier.

11.  Is There an Answer Key for their Solution on the Web?

For many assessments, there are answer keys and guides on how to fool or pass the test. One example is here:  http://on.wsj.com/29Che0n.  When you see this, it means two things: (a) that the test is easily fooled, lacking internal controls to prevent spoofing, and (b) it means that that you are looking at an “industry benchmark” with one clear set of answers.

A Data Science-driven model would be custom to your role in your company, and be continuously evolving – therefore very difficult for  answer keys and spoofing to catch.

12.  Does the Company Itself (i.e. Myers Briggs) Specifically Tell You Not to Use their Solution for Hiring / Talent Acquisition?

Some assessments, notably the Myers Briggs survey, specifically implore users to not use the tool for talent acquisition: “It is not ethical to use the MBTI instrument for hiring or for deciding job assignments.”  http://www.myersbriggs.org/my-mbti-personality-type/hiring-an-mbti-consultant/guidelines-for-hiring-an-outside-consultant.htm

13.  Ask to see their company policy on employee predictive modeling, discrimination, disparate impact and fairness.

It is important that a predictive solution has thought through the specific outcomes of their models and how they fit into creating fair opportunity for all applicants.  In particular it is vital for the solution to satisfy or exceed any government requirements for hiring and selection.

14.  Do Your Own (Internal) Data Scientists Approve of this Predictive Solution?

We recommend asking one of your own data scientists (from HR, marketing, or another area inside your own company) to accompany you in your evaluation. They know what is a rigorous approach and what is marketing fluff.

15.  How Does the Predictive Solution Regularly Prove to You that the Models Are Working?

Ideally the company you select will be able to show you 2 – 4 times a year how your predictions are working (i.e. turnover is going down, sales are going up, calls are going up, errors are going down etc.,)

Only use a predictive model during talent acquisition if the predictions are accurate.  If they’re not – you should stop using the models.  You need this feedback.

Predictive Talent Assessments Can Have a Prominent Place in Your Predictive Talent Acquisition Process…but you need to be careful in choosing the real predictive solution vs. a legacy talent assessment with a predictive marketing wrapper.

 

About the Author

Greta Roberts is an acknowledged influencer in the field of predictive workforce analytics. Since co-founding Talent Analytics in 2001, she has established Talent Analytics, Corp. as the globally recognized leader in predicting an individual’s business performance, pre-hire and post-hire.

She has led the firm to use predictive analytics to solve line of business challenges making Talent Analytics one of the only firms in the world predicting business outcomes. Examples include predicting someone’s probability of making their sales quota, or being able to process a certain number of calls, or make errors, and the like.

Greta leads the company in developing predictive solutions that can be easily deployed into employee operations, to teams without a background in analytics, statistics or math.  This strategy has led to the development of Talent Analytics’ award winning predictive cloud platform Advisor.

In addition to being a contributing author to numerous predictive analytics books, she is regularly invited to comment in the media and speak at high end predictive analytics and business events around the world. Through recognition of her commitment and leadership, Greta was elected and continues to be Chair of Predictive Analytics World for Workforce, an innovative, annual predictive analytics event dedicated to solving workforce challenges.  She is an Instructor on Predictive Analytics for HR and Workforce at UC Irvine; she is a faculty member with the International Institute for Analytics (IIA), is a member of the INFORMS Analytics Certification Board.

Follow Greta on twitter @gretaroberts 

 

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