Predictive Analytics Times
Exclusive Highlights
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...
Wise Practitioner – Predictive Analytics Interview Series: Darryl Humphrey at Alberta Blue Cross
  In anticipation of his upcoming conference presentation, Claim...
The Evolving State of Retail Analytics in CRM
  The Traditional State The world of retail has...
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...
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...
Wise Practitioner – Predictive Analytics Interview Series: Kristina Pototska at TriggMine
  In anticipation of her upcoming conference presentation, 7...
Wise Practitioner – Predictive Analytics Interview Series: Frédérick Guillot at The Co-operators General Insurance Company
  In anticipation of his upcoming conference presentation, Defining...
Predictive Analytics vs. Prescriptive Analytics
  We have all heard and seen the diagrams...
Interview with Prof. Dr. Wil van der Aalst, Eindhoven University of Technology
  Exclusive interview with Prof. Wil van der Aalst...
Data Story Telling: Bringing Life to Your Data
  There is no doubt that a successful Data...
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...
Managing Shifting Priorities in Exploratory Data Science Projects
  After working with a client’s data for over...
Breaking into Analytics: 5 “Musts” for your Career Transition
  In our data-rich society, corporations of all types...
How Predictive Analytics Can Fuel Innovation for Manufacturing
  Industry leaders like to use the term “culture”...
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...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions
  In anticipation of his upcoming keynote co-presentation, Picking...
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...
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...
Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management
  In anticipation of his upcoming keynote co-presentation at...
Wise Practitioner – Predictive Analytics Interview Series: Frank Fiorille at Paychex, Inc.
  In anticipation of his upcoming conference presentation, Risk...
The Real Reason the NSA Wants Your Data: Predictive Law Enforcement
  The NSA can leverage bulk data collection with...
Wise Practitioner – Predictive Analytics Interview Series: Scott Zoldi at FICO
  In anticipation of his upcoming conference keynote presentation,...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Klein at Miles & More GMbH
  In anticipation of his upcoming conference co-presentation, Using...
Book Review: Predictive Analytics for Newcomers and Nontechnical Readers
  The book reviewed in the article, Predictive Analytics:...
Wise Practitioner – Predictive Analytics Interview Series: Meina Zhou at Bitly
  In anticipation of her upcoming conference presentation, Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Shantanu Agrawal at Centers for Medicare & Medicaid Services
  In anticipation of his upcoming conference keynote presentation,...
Infographic – Discover Predictive Analytics World for Business 2016
  Predictive Analytics World continues to grow – take...
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...
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...
Wise Practitioner – Predictive Analytics Interview Series: Brian Reich, Former Director at The Hive
  In anticipation of his upcoming conference presentation, The...
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...
Getting Started with Predictive Analytics – an Interview with Eric Siegel
  Data science and predictive analytics are top of...
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...
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...
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...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies
  In anticipation of his upcoming Predictive Analytics World for...
Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation
  In anticipation of his upcoming conference co-presentation, Personalized...
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...
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...
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...
Feature Engineering within the Predictive Analytics Process — Part One
  What is Feature Engineering One of the growing...
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...
Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories
  In anticipation of their upcoming conference co-presentation, Enhancing...
Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology
  In anticipation of his upcoming conference presentation, Tips...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
  In anticipation of his upcoming conference presentation, Using...
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...
HR’s First Predictive Project? Pre-hire Candidate Screening
  Corp recruiters have a very important and difficult...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Gary Neights at Elemica
  In anticipation of his upcoming Predictive Analytics World for...
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...
Wise Practitioner – Text Analytics Interview Series: Frédérick Guillot at Co-operators General Insurance Company
  In anticipation of his upcoming conference presentation, Leveraging...
Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech
  In anticipation of her upcoming conference co-presentation, Utilizing...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC
  In anticipation of his upcoming Predictive Analytics World for...
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...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.
  In anticipation of his upcoming Predictive Analytics World for...
Improving Word Clouds as Tool for Text Analytics Data Visualization
  Rich Lanza will present Using Letter Analytic Techniques...
Dr. Data’s Music Video: The Predictive Analytics Rap
  With today’s release of “Predict This!” – the...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard
  In anticipation of her upcoming Predictive Analytics World for...
Mid-Life Journey to Data Science
  Data Science has been hailed as the sexiest...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
  In anticipation of his upcoming keynote conference presentation,...
What are you Predicting in Customer Retention?
  Customer Retention models are arguably the most valuable...
Wise Practitioner – Predictive Analytics Interview Series: Ken Elliott at Hewlett Packard Enterprise
  In anticipation of his upcoming conference presentation, Operationalizing...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller at Constellation Research
  In anticipation of his upcoming Predictive Analytics World for...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
  In anticipation of his upcoming conference presentation, Predicting...
Hey FinTech, What’s Your Strategy for Leveraging Unstructured Data?
  Financial technology has sparked a global wave of...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Raffael Devigus at F. Hoffmann-La Roche AG
  In anticipation of his upcoming Predictive Analytics World for...
Wise Practitioner – Predictive Analytics Interview Series: Rebecca Pang at CIBC
  In anticipation of her upcoming conference presentation, Driving...
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...
The Information Age’s Latest Move: Four Predictive Analytics Developments for 2016
  Originally published in Big Think Prediction is in...
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...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Vishwa Kolla at John Hancock Insurance
  In anticipation of his upcoming Predictive Analytics World for...
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...
Wise Practitioner – Predictive Analytics Interview Series: Peter Bull at DrivenData
  In anticipation of his upcoming conference presentation, Predicting...
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...
Wise Practitioner – Predictive Analytics Interview Series: Matt Bentley at CanIRank.com
  In anticipation of his upcoming conference presentation, Predicting...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Lisa Disselkamp and Tristan Aubert at Deloitte
  In anticipation of their upcoming Predictive Analytics World for...
The Data Scientist’s Dilemma: Does Skipping Breakfast Kill You?
  Would skipping breakfast kill you? Not necessarily—but confusing correlation and...
Predictive Analytics Can Help with the Challenges Facing Manufacturing in the 21st Century
  Historically, data and analytics have been key to...
Wise Practitioner – Predictive Analytics Interview Series: Nate Watson at Contemporary Analysis
  In anticipation of his upcoming conference presentation, Predictive...
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...
Machine Learning: Not Necessarily a New Phenomenon in Predictive Analytics
  One of the more recent topics gaining traction...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Frank Fiorille at Paychex, Inc.
  In anticipation of his upcoming Predictive Analytics World for...
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...
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...
Four Ways Data Science Goes Wrong and How Test-Driven Data Analysis Can Help
  If, as Niels Bohr maintained, an expert is...
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...
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...
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...
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...
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...
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...
Automation: Friend or Foe to the Predictive Analytics Practitioner
  Technologies and Big Data continue to bombard our...
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...
Wise Practitioner – Predictive Analytics Interview Series: Benjamin Uminsky, Los Angeles County
  In anticipation of his upcoming conference presentation, Mining...
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
  To view this content OR subscribe for free...
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...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor
  In anticipation of his upcoming conference presentation, Picking...
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...
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...
Wise Practitioner – Predictive Analytics Interview Series: Scott Jelinsky of Pfizer, Inc.
  In anticipation of his upcoming conference presentation at...
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...
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...
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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The Key to Modelling Success -The Variable Selection Process (Part 1)
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Predictive Analytics World in Color [Infographic]
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Space Alien Eager to Convey Thoughts on Data Science
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Defining Measures of Success for Predictive Models
  Excerpted from Chapters 2 and 9 of his...
Overstatement of Results in Predictive Analytics
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The Biggest Lever to Success in Predictive Analytics
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Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Field Cady at Think Big Analytics
  In anticipation of his upcoming Predictive Analytics World for...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Thompson of Robert Bosch, LLC
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Kumar Satyam of PricewaterhouseCoopers, LLP
 In anticipation of his upcoming Predictive Analytics World for Manufacturing conference...
White Paper – Immediate Access
Thank you for your interest in the white paper,...
Predicting Rare Events In Insurance
  As we all know, predictive analytics is a...
Python, Predictive Analytics & Big Data oh my!
 Python has seen significant growth in utilization in the...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher of National Consumer Panel
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Leveraging Open Data: Improve Customer Experience and Drive New Market Opportunities
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From Code to Reports with knitr & Markdown
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Predictive Analytics Optimizes Prices and Markdowns for Retail
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Wise Practitioner – Predictive Analytics Interview Series: Delena D. Spann of US Government
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Infographic – PAW SF
5-Minute Recap
  In San Francisco this past March and April,...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
 In anticipation of his upcoming conference presentation, Buy or...
Wise Practitioner – Predictive Analytics Interview Series: Viswanath Srikanth of Cisco
 In anticipation of his upcoming conference co-presentation, Building a...
Wise Practitioner – Predictive Analytics Interview Series: Jack Levis of UPS
 In anticipation of his upcoming conference keynote presentation, UPS Analytics...
Trust in Analytics Work: Why it’s Needed and How to Build It
 Much has been written about data-driven decision making. Someone...
Guiding Principles to Build a Demand Forecast
 Demand forecasting is one of the most challenging fields...
Wise Practitioner – Predictive Analytics Interview Series: Arcangelo Di Balsamo of IBM
 In anticipation of his upcoming conference presentation, Applied Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of Smarter Remarketer
 In anticipation of his upcoming conference presentation, The Revolution...
Predictive Analytics in Sports
 The world of sports has seen exponential increases in...
5 Things I Learned at Predictive Analytics World for Workforce
  As a trained researcher, I’ve always been fascinated...
Predictive Analytics as a Strategic HR Solution
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6 months ago
Managing Shifting Priorities in Exploratory Data Science Projects

 

After working with a client’s data for over three weeks with no real progress, you finally hit upon a real breakthrough. You’ve been searching for insights that will help identify which customers are most likely to turn into regular purchasers; the ultimate goal is to focus the company’s marketing efforts on this group in order to earn more revenue per advertising dollar. Studying customer purchase history has been unfruitful. Suddenly, you find that customer geography seems to be a better predictor of future purchases. You have a few more weeks to explore that connection, so you should be able to find some real value for the customer, right? Unfortunately, you’re trapped in your current course. Because you listed it in the initial Work Breakdown Structure, you are committed to delivering a production quality purchase history model. If you spend the rest of the project building and deploying that model, you won’t have time left to look into a geography-based model. On the other hand, if you spend the rest of the project exploring the geographic connection to repeat purchasers, you run the risk of failing to complete a major project deliverable. So what now?

This problem occurs all too frequently on data science projects. Customers rightfully require documentation of what exactly the team will accomplish at the kickoff of the project. Yet once the project begins, this initial project planning can quickly become irrelevant as new events, priorities, and insights into the data drive the team in new directions. In today’s constantly evolving information and technology economy, these types of changing requirements are common in many industries, though the field of data analytics is particularly susceptible to the problem. The condition of the data, corporate business rules surrounding data usage, technology requirements, and the exploratory nature of data analysis all combine to make it nearly impossible to define up front exactly what tasks will be needed and how long each will take. As shown in the image below, changing requirements can lead to excessive rework in a traditional Waterfall management approach, as each change sends the team back to the drawing board, negating much of the progress that has been made.

Shifting priorities often threaten to derail data analysis projects, but that doesn’t have to be the end of the story. This challenge is not unique to data science, and solutions may be gleaned from other domains. We can learn key lessons from: Agile Software Development (agilealliance.org), and the Lean Startup Method proposed by Eric Ries (theleanstartup.com).

Agile Software Development

In the field of software engineering, the problem of evolving requirements has been recognized for many years. Through the 1990’s and early 2000’s, software practitioners began developing new methods that embraced these changing requirements rather than shunning them in favor of plans established before the project kickoff. The methodologies that emerged out of this movement are collectively known as Agile Software Development. Though many specific methodologies fall under the “Agile” umbrella, what they have in common is an emphasis on working in close collaboration with the client, releasing or demoing working software early and often in the process, and encouraging the customer to review and adjust development priorities as the product emerges and requirements become more clear. As illustrated below, an Agile approach can save huge amounts of rework through more frequent iterations and adjustments.

Machine Learning Image
Changing requirements cause rework in the Waterfall Method as all work is completed before the customer validates the product. A more agile approach focuses on incremental releases of working software, allowing for earlier course correction.

I experienced the benefits of an Agile approach firsthand during a recent software development project. At our regular biweekly progress review, the client mentioned that he had been fielding questions from customers all week about why a particular table couldn’t be exported to a spreadsheet. It turns out that the workflow for a number of users required them to pull data from our system in this way and upload it into another system — a use case that hadn’t been anticipated in the initial product design. Rather than push back and explain that this feature wasn’t in the original design, we were able to pull up the current development priorities and allow the client to decide where the export functionality fell in terms of business value. Unsurprisingly, he placed this feature at the top of the list. With discussion, we agreed to begin work on the export feature and to push off our development of a new graph on another page. If we had stubbornly stuck to our initially agreed-upon feature list, we would have produced an application which was worthless to an entire segment of users who required the export functionality in their daily work-flow. Instead, Agile management processes allowed us to respond to evolving requirements and develop the tool which actually drove the most value for the customer.

While Agile Software Development is focused on delivering software products that create business value for customers, we can draw a strong parallel to data science projects, where the main products are data insights that similarly deliver business value to customers. Just as with projects in the software world, data science projects can benefit from working in close collaboration with the customer and delivering insights as early as possible in the analytic process. The idea here is that customers are going to have a much stronger understanding of their domain and the main problems they face than the consultants analyzing their data do – certainly at first.

Delivering insights as early as possible in the process allows the customer to see the direction that the project is going and course correct where necessary as early as possible. A major concept in Agile Software Development is to “maximize the work left undone.” This means allowing the customer to direct development effort towards the most impactful areas before much time is spent developing less valuable features. In the same way, encouraging the customer of a data analysis to review preliminary results and offer feedback as early as possible can allow them to correct any improper assumptions and ensure that the consultant spends the greatest percentage of their time solving the problems that will give the customer the most value.

A major challenge in any data science project is that the customer may not completely understand their business problem before the project begins. Surely then, the understanding of the consultant in that area will be even farther from the ground truth. This makes it extremely difficult to define requirements for the project before it begins. The requirements that are created before the project begins are sure to change as the project goes on due to the constantly shifting business environment as well as new insights uncovered as the data is analyzed. Again drawing from the Agile approach, we can embrace these changing requirements by developing only a preliminary list of requirements and tasks before the project begins, and inviting the client to reprioritize and change these lists at regular intervals as the project goes on. In this way, we can ensure that the team is adding value based on the most recent understanding of the business environment, and not on only what was known before the project began.

The Lean Startup

We can see similar planning challenges in the domain of entrepreneurial start-ups. The main goal of a startup is to turn new ideas or technologies into products or services that deliver value to end-users. Of course, before the product or service is created, it can only be assumed that these ideas or technologies can be leveraged in particular ways to drive value. In his groundbreaking The Lean Startup, Eric Ries posits that the most successful startups are the ones that can test these assumptions efficiently and either double down on validated ideas or pivot quickly to new ideas when assumptions prove incorrect. This way of thinking revolutionized the world of startups, and the methodology laid out in The Lean Startup has become best practice across that domain.

The full process behind the Lean Startup method is beyond the scope of this article, but in short, the book argues that to be successful, startups should get a minimum viable product into customers’ hands as early as possible, then proceed by iterating over three steps. First, they should define a metric which measures the success or failure of improvements to their product (such as the percentage of visitors who actually return and become regular users for a website). Second, they should begin making changes or adding features which test individual assumptions (for example, adding a “share” button to test the assumption that users want to use a website in a more social way). Finally, they should observe the changes in the metric based on the changes made to the product or service and use this information to readjust their assumptions before beginning the process again in an iterative manner.

Just as in new business ventures, initial assumptions going into a data analysis project are just that: assumptions. Following the Lean Startup Method, we should first make these assumptions explicit and choose (or design) a metric by which to measure the value of the insights we are drawing. Then, we can iteratively test assumptions by measuring the effect of small changes or new features on our metric, revising assumptions accordingly, and repeating with new changes or features. In this way, we can revise or throw out any assumptions which prove incorrect and pivot towards changes which drive greater value.

To illustrate this process, let’s look at an example. I began this article with the hypothetical story of a data scientist who is analyzing the data of a consumer products business in an attempt to predict the customers most likely to become regular purchasers. To apply the method outlined above, the first step would be to identify any assumptions and define a metric to measure the success or failure of experiments that will be used to test them. Data scientists employ a number of metrics to evaluate their models – such as lift charts, ROC curves, Gini coefficients, mean absolute error, etc; there are many ways to evaluate the effectiveness of a model at predicting a particular target. Using these metrics is a good way to measure the effectiveness of model changes, though I would argue that, since the goal is to measure the value to the customer, it is most helpful to go one step further and create a metric which directly ties to business value. Perhaps in this case, we could measure the money that would be saved by not sending marketing information to users who were unlikely to become repeat purchasers.

Once assumptions and a viable metric have been established, we can begin testing and revising these assumptions through iterative experiments. At first, this client believed that customer purchase history would be predictive of repeat purchase behavior. To test this, we could create a basic model focused on that predictor, then calculate the value of this model according to our metric. If the results of the model pointed to an improvement in our metric (by allowing us to more accurately target our marketing and achieve similar results on a lower marketing budget), we could consider this assumption validated and move on to test another assumption. If our experiment did not point to an improvement in the metric, we could pivot in our analysis approach by revising or throwing out this assumption. We could continue in this way, continuously testing and revising assumptions, thereby improving our understanding of the business problem and our model (as measured by the metric we chose to represent value to the customer).

Obviously, this is an overly-simplified example of the complex business problems which many data science analyses attempt to solve, but it illustrates the way that, by systematically defining and testing our assumptions, we can continually refocus our efforts into areas that drive the most value for our clients.

Challenges

While there is a lot of value to be gained by embracing, rather than fighting or ignoring, changing requirements in today’s fast-paced business environment, this method also presents some challenges. The most basic of these challenges is customer buy-in. Many customers will be used to more traditional project lifecycles, with deliverables and deadlines set in stone up front. It will take a good amount of trust, especially for external customers, to buy into a plan without a well-defined deliverable. The best way to work around this issue is to work as closely as possible with the customer and integrate them completely in the decision process through which priorities and deliverables can be shifted or changed.

A client of a recent data science project was having a hard time accepting a more Agile management approach. How could he be sure he was getting everything he paid for if the project plan didn’t define precisely which models would be included in the final deliverable — a visualization designed to prioritize cases for investigators? Upon explaining that it was impossible to say for sure which models would be most useful before their development, he reluctantly agreed to our approach. Throughout the course of the project, he found that Agile’s focus on embracing changing requirements actually gave him more control over what models were included in the final tool. Rather than having to guess what would be useful before we began work, he was able to listen to the feedback of his investigators as models were built and refined and direct our work towards the areas that made more of a difference in the investigators’ daily workflows. When the time came to begin a new project for this client, he insisted that we apply an Agile approach towards deliverable and requirements definition!

Even when a customer buys into the process, they may still have a requirement for formal documentation. Traditional project plans often require an SOW (statement of work), which lays out deliverables and hard deadlines. These plans don’t leave much room for shifting priorities or changing requirements. A great area for future analysis would be seeking the best way to create a truly effective Agile project plan that can provide flexibility while meeting reporting and communication requirements. A possible starting point for such a document would be an Agile Project Initiation Document, of which there are a number of different templates floating around the internet. Another possibility would be an adaptation of Alexander Osterwalder’s Business Model Canvas (businessmodelgeneration.com), a light-weight business plan template designed within the principles of the Lean Startup.

Beyond customer buy-in and process documentation, there are the additional concerns of scope creep or lack of vision. Precise deliverables aren’t fully defined before the project begins, and the customer is invited to change and shift priorities over the life of the project. This can lead to frequent pivots without an overarching vision, which can make it difficult for the project to gain momentum in any one direction. The opposite problem is also possible, with the customer requesting more and more work, without a well-defined scope to keep these requests to a reasonable level for the project. The new more dynamic approach to priorities, requirements and deliverables certainly requires a strong project management focus to avoid these problems.

Conclusion

In today’s business climate, priorities can change in the blink of an eye. It is challenging to know at the outset of any project exactly what outcomes would drive the most value for the customer. To make matters worse, data science projects are exploratory in nature, meaning that it is impossible to know, at the outset, what direction the findings will lead. As more data becomes available and projects become more complex, it will only get harder to predict the lifecycle and requirements of a data analysis project before it begins. All of these problems necessitate an approach which embraces changing requirements and uses them to course correct in order to drive the greatest possible value. This can only be done by iterating quickly and working as closely as possible with the customer.

Similar problems have been researched extensively in the domains of software engineering and entrepreneurship. Data Science practitioners would do well to draw on the lessons learned from these fields to improve their own processes. Still, in doing so, there are a number of challenges that remain. Further analysis and wisdom on how these principles can be applied to data science projects, and especially how they fit with formal project documentation, would be extremely helpful.

Author Bio:

Andy JanaitisAndy Janaitis is a Software Engineer and Scrum Master for Elder Research (elderresearch.com). In those roles, Andy works collaboratively with developers and customers alike to deliver desktop and web-delivered data analysis and visualization applications. Andy is most passionate about designing elegant solutions around clients’ business needs and driving efficiency through process improvement. Andy is a proud alumnus of the University of Pittsburgh, where he received bachelor’s degrees in Industrial Engineering and History. When not in the office he enjoys running, brewing beer, and supporting Pitt football and basketball.

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