Predictive Analytics Times
Predictive Analytics Times
EXCLUSIVE HIGHLIGHTS
Wise Practitioner – Predictive Analytics Interview Series: Stephen Morse, Advisor at Neudata
 In anticipation of his upcoming conference presentation, Leveraging Alternative...
Wise Practitioner – Predictive Analytics Interview Series: Dongyang Fu and Wen Shi at Concord Advice
 In anticipation of their upcoming conference presentation, Improving Credit...
Wise Practitioner – Predictive Analytics Interview Series: Ron Cowan at Snowforce Data
 In anticipation of his upcoming conference presentation, Using Mileage...
The Harvard Business Review Video Interview: Eric Siegel on Predictive Analytics
 Originally published by Harvard Business Review Prediction is reinventing...
New-Age Machine Learning Algorithms in Retail Lending
 Originally published in KDNuggets.com          ...
Machine Learning Tip: Nested Cross Validation – When (Simple) Cross Validation Isn’t Enough
 Several scientific disciplines have been rocked by a crisis...
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’...
Employee Life Time Value and Cost Modeling – Part 3
 Employee Tenure in a “Survival Analytics” Framework With a...
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...
Automation and its impact on Predictive Analytics-Creating the Analytical File
 In my last article, I discussed the increasing impact...
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
  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 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
  To view this content OR subscribe for free...
Wise Practitioner – Predictive Analytics for Healthcare Interview Series: Daniel Chertok at NorthShore University HealthSystem
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3 years ago
Meet DJ Patil: Obama’s Big Data dude

 

Deputy Chief Technology Officer for Data Policy DJ Patil with his skateboard March 19, 2015. (Photo: Chris Usher for Yahoo News)

DJ Patil is still settling into his White House office. It’s by far the most prestigious place he’s ever worked, but it looks more like a bootstrapping startup than a precinct of Washington power. His desk is tidy, and on top of his overflowing bookshelf sits a watchful Pokémon Pikachu doll, adding a touch of whimsy to the otherwise drab, government-issue office. Propped up against the wall is his bamboo longboard (a hybrid trickboard with a school of fish painted on the underbelly). The skateboard is more than just an emblem, a reminder of his days as a Silicon Valley technologist when he sometimes zipped across the campuses of LinkedIn and eBay. It’s how he gets to work on nice days, hurtling down the streets of downtown D.C., weaving around potholes, tourists and protesters as he feels the breeze in his dark brown hair, before slipping behind the security gates of 1600 Pennsylvania Avenue.

Patil is responsible for nothing short of harnessing the extraordinary power of the federal government’s hundreds of thousands of data sets.

In February, President Barack Obama added Patil, a cheerful Californian who still laments the lack of authentic Mexican food in D.C., to the growing roster of Silicon Valley-bred digerati who have been recruited to shore up the White House’s tech capabilities. His official job title is chief data scientist and deputy chief technology officer for data policy. Unofficially, he is responsible for nothing short of harnessing the extraordinary power of the federal government’s hundreds of thousands of data sets.

How much data is that, exactly? The scope of the government’s collection is so staggering that no one really knows. Thanks to the explosive use of affordable mobile electronics in the 21st century, data proliferates in such a frenzy that researchers estimate over 4 zettabytes of it exists in the world. To put that in context, if 1 byte equaled a character of text, the 1,250-page tome War and Peace would fit into a zettabyte about 323 trillion times. The U.S. is the largest collector in the Northern Hemisphere. And Patil’s job is to be both the wielder and the protector of that data, to unlock its vast potential for progress while guarding against risk of government abuse.

PAW GOV

Today, government data powers some of the most basic parts of our lives. The weather app you look at each morning before getting dressed filters statistics from the National Oceanic and Atmospheric Administration into the form of a shining sun or angry cloud. On your way to work, your local transportation office feeds information onto a screen that tells you when your train will arrive, or how much longer it’ll take to drive to your destination. The Bureau of Transportation Statistics even keeps track of how horribly delayed your flight is.

But Patil, a guy who’s spent his life molding data into powerful, cutting-edge products for companies like eBay and LinkedIn, wants to do much more than improve your morning commute. From day one on the job, he’s spearheaded a far-reaching project called precision medicine: a form of care that uses your DNA to dictate a personalized approach to treatment. Patil imagines that one day, mapping your genome may be as routine as getting a cavity filled. Doctors will then be able to tailor care based on your own genetic makeup. Cross-reference that with statistics the government provides about the environment you grew up in — the air quality, the water quality, the likeliness of disease — and, as Patil says, his brown eyes widening, “literally, data can save lives.”

Though Patil is focusing on health care for now, nearly every government entity could be revolutionized with the help of data. After collaborating with companies like Waze during Hurricane Sandy , the Federal Emergency Management Agency has invested in data-driven infrastructure that swiftly identifies areas of need during a disaster. Researchers at the University of California, Berkeley, are currently investigating data relating to mandatory sentencing in the criminal system, calculations that could save prisons money and reduce recidivism rates. Most recently, a study that found a correlation between climate change and violent conflict made its way into Obama’s State of the Union speech.

“ Literally, data can save lives.”– DJ Patil

On the flip side, Patil must also ensure that the enormous amounts of data collected under the government’s watch are both secure and free from the exploitive tendencies of private companies. Recently, for instance, the administration has focused on educational apps tailored to individual students. By tracking students’ online habits, educators can zero in on their needs. But once the data is collected, under current law it can also be sold for marketing purposes. Patil is a fierce opponent of such practices. A newcomer to Washington politics, he’ll have to wade into this and other contentious policy debates.

By turning to Patil, Obama is placing a bet on an iconoclastic risk taker, a classic disrupter in the mold of Silicon Valley techies. His trailblazing path to the pinnacle of data science involved skirting the rules and tilting against traditional institutions. He was suspended from school, roundly rejected by admissions offices, and befuddled HR directors who couldn’t figure out where he would fit in. But through a combination of blinding intelligence, a laserlike focus on the real-world applications of science and an uncanny ability to see alternative paths, he willed his way to the top of this cutting-edge profession. Now the question is: Can he succeed inside the U.S. government, the mother of all rulebound institutions, a risk-averse bureaucracy and stifler of innovation?

Meet DJ Patil: Obama’s Big Data dude

DJ Patil chats with colleagues at the White House. (Photo: Chris Usher for Yahoo News)

“The biggest challenge for DJ is going to be human and organizational, rather than technical,” said Steven Weber, a professor at Berkeley’s School of Information and Political Science who has interacted with Patil in the field. “I don’t think he’s going to be able to walk into that office and say, ‘Look at all this great data we have, we can build this and we can build that, we can build this and we can build that.’ But I know that’s going to go through his head.”

Silicon Valley High

Patil was born in 1974 as Dhanurjay Patil, and grew up in the unharvested suburbs of Cupertino back when the Bay Area was better known for its fruit orchards than its office parks. His father, Suhas, immigrated from Jamshedpur, India, to earn his PhD in electrical engineering at MIT, studying under greats like Harold Edgerton (best known for his mind-bending strobe flash photography of milk drops). After graduating, he set out for Silicon Valley to build his own successful semiconductor business, Cirrus Logic.

Growing up, DJ was bored by rote science and math lessons — the step-by-step instructions he was taught in school. But at home he became steeped in the ethos of garage-based businessmen like Dave Packard and Bill Hewlett. Patil and his father cleared out an extra room in his childhood home to re-create Edgerton’s experiments, tinkering away for hours on weekends. Captivated by the unpredictability of physical movement, he devoured James Gleick’s book “Chaos: Making a New Science,” a seminal study of chaos theory.

“I started really reading and I thought: ‘This is fascinating,’” he told me last month in an empty conference room in the Eisenhower Executive Office Building, where we were surrounded by framed pictures of iconic moments in science and tech history. “But I didn’t have the mathematical skills to really understand it.”

“ It was this kind of moment when you realize: ‘Oh, my gosh, I am that stupid.’”– DJ Patil

It was a pattern in Patil’s life: He was too intellectually restless to settle down and focus in class. He loved computers and quickly found their subversive power. By middle school he’d managed to hack into his English course’s grading system. Within the first six months of attending Monta Vista High School, he was kicked out of his algebra class for speaking out of turn (he was forced to repeat the class that summer). Later on, he was suspended from school for setting off a stink bomb in class. Despite this rebellious streak, the very assistant principal who suspended him for that incident saw his talent.

“I don’t think he was one who was disrespectful of the system or was actively defiant against rules for the sake of being defiant against the rules,” Rich Knapp, a now retired school administrator, told me in a recent interview. “If he thought he had a better way of doing things, he wasn’t afraid to step out and say, ‘I think there’s a better way to do this’ and take a risk and do it.”

Nevertheless, by the time Patil graduated from high school in the spring of 1992, his high jinks and poor SAT scores had put him at the bottom of his class. He received a pile of thin envelopes — rejection letters from every college he wanted to attend. He cried, and now recalls it as a “soul-crushing” experience.

At the encouragement of his father, however, he pushed on. First he appealed his rejection from the University of California. At the same time, he followed his girlfriend to De Anza Community College, enrolling in the same classes she did. On the first day of their calculus course, he listened intently to the professor’s lecture but understood nothing.

imageDJ Patil in high school. (Photo: Courtesy DJ Patil)

“It was this kind of moment when you realize: ‘Oh, my gosh, I am that stupid,’” he said. “I had a choice: Either get with the program or you’re not going to be able to understand these concepts that you’ve been passionate about.”

Deeply embarrassed, he went to the Cupertino library, checked out every single high school math book he could get his hands on and — in a scene straight from Isaac Newton’s biography — taught himself math. After years of feeling clueless in the classroom, he finally found that the core mathematical concepts that had always fascinated him seemed to stick. It was a humbling moment for Patil, but also, as he recalls it, “really fun.” Meanwhile, much to his surprise, his appeal to get into college worked, and in 1993 he transferred to the University of California, San Diego, majoring in mathematics.

The graduate

Patil fit in well at UCSD, a college located in the sunny, affluent beach town of La Jolla. His dorm — a newly built canyonside housing area nicknamed “Snoopy Camp” — was a quick 15-minute walk from Black’s Beach. But he was still disengaged in class. By 1996, he’d completed his degree and found himself in a place similar to the one he’d been in four years earlier — without the grades to get into any of the programs he’d been eyeing, particularly a mathematics and physics doctorate track at the University of Maryland. He again turned to his father for advice.

“ If he thought he had a better way of doing things, he wasn’t afraid to step out and say, ‘I think there’s a better way to do this.’”– Rich Knapp

“In his infinite wisdom he suggested one thing — road trip,“ Patil recalled in a commencement speech at UC Berkeley’s School of Information. “A special one that might just ‘happen’ to take us near the campus.”

The two set out east with an aim to meet James Yorke, regarded by many as the father of chaos theory. Patil had cold-emailed Yorke before the trip, to a tepid response. But Yorke agreed to meet them for dinner at a nearby Chinese restaurant. By the time the check arrived, Patil had impressed Yorke enough to make it into the program.

Yorke saw something different in Patil, namely his drive to take his research further than the math problems on a page. “He was unusually focused on the question of ‘Why are we doing this and where do we want to go?’” Yorke, now 73, recalled during a recent interview. “One can get lost in mathematics by creating results that really don’t have impact. So he wants to know, what’s the impact? Why are we doing this? What’s the worst thing that could happen?”

To pay his way through school, Patil took a gig as a lecturer while juggling his research on nonlinear weather patterns at night. Each day around 5 p.m. he’d go to bed and sleep until midnight. Then he’d wake up and head to the campus computer lab, where he’d parse pages and pages of public data sets from the National Oceanic and Atmospheric Administration. It was during these early hours of the morning that he came to appreciate open access to the government’s vast databases, later admitting that they “helped get me through school!”

image

DJ Patil with his father after Patil’s commencement address at UC Santa Cruz’s Jack Baskin School of Engineering. (Photo: Courtesy DJ Patil)

By the time he’d completed his doctorate, Patil had made a significant improvement on mathematical models used for numerical weather forecasting, finding a more efficient way to predict chaotic temporal patterns. Finally, he saw firsthand how his work in the field could effect actual change.

He joined the Defense Department in 2004, at the height of the U.S.’s involvement with Iraq. There, he and two other research fellows took part in something called the Threat Anticipation Project, which researched how to combine computer science and social science in order to anticipate emerging terrorist threats.The experience was yet another instance where Patil saw the limitless power of data in almost any situation.

Manifest destiny

After the fellowship and a short stint as a professor at the University of Maryland, Patil headed home to Silicon Valley in 2006. By then, the cherry orchards he grew up with had been ripped down and replaced with a hybrid shopping center/apartment complex, complete with a Chipotle and a Borders bookstore. Over the previous 20 years, the sleepy suburban sprawl of his childhood hometown had expanded to accommodate the massive number of tech workers who were flocking west to join companies like Facebook, Google and Skype. Patil was one of them. But as he interviewed at startup after startup, it became clear that he was not as desirable a candidate as he presumed.

“I’d go to eBay and all these other companies, and I’d be like: ‘Look, these are the problems I’m interested in and I think I can help,’” he said. But he’d always receive the same response: “We don’t know what to do with you.”

Through a family connection he was able to land a job at eBay. The company created a role for him as principal architect. Eventually Patil was placed at the crux of product development for every company that fell under eBay’s purview, including Skype, PayPal and StubHub. There he used his expertise in data to strategically pinpoint problem areas in the company and developed products to improve its core operations. In just a two-year stint there, he filed eight patents relating to customer service support, machine learning, human-computer interaction, visualization, behavior insights and social network analysis.

Around the time that Patil joined Skype, LinkedIn had its own data problems. The company recognized that, though it had grown to a network of about 8 million accounts, people didn’t seem to be connecting with one another as automatically as hoped. One LinkedIn manager compared the situation to “arriving at a conference reception and realizing you don’t know anyone. So you just stand in the corner sipping your drink — and you probably leave early.” Employees like Jonathan Goldman were playing with user information to invent features like People You May Know — a built-in website feature that practically every social network uses today. But these were side projects, and sometimes openly contested within the company.

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DJ Patil speaks at the Digital Life Design Conference on Jan. 20, 2013, in Munich, Germany. (Photo: Tobias Hase/DPA/Zumapress.com)

When then-CEO Reid Hoffman recruited Patil to join the company in 2008, all that changed. Patil assembled a team of sharp data scientists, focusing their efforts on missed opportunities within an individual’s browsing experience. Once they identified a weak point, they’d use data to offer a personalized fix.

“What we realized is data, when used responsibly, is a force multiplier,” Patil said.

Patil and his team approached its projects organically, bouncing around ideas, moving quickly to test them and quickly throwing out what flopped. The result was the creation of well-known tools like Who’s Viewed My Profile, Jobs You Might Be Interested In and visualizations of a person’s professional network called InMaps. In each new feature, Patil drove home the idea that the best sign of a good data product is no obvious evidence of the data itself.

“The user doesn’t want to see raw data, they want the data in a usable form, and that usable form should help them do something more creative, be more efficient, give them superpowers,” he said. “Something you could never conceive of before.”

To Patil, it was just plain logic that regular people didn’t want a bunch of statistical noise getting in the way of their online experience. But in a profession filled with statisticians and programmers, his rare eye for the bigger picture set him apart.

Coining a profession

Around the same time, Jeff Hammerbacher was developing similarly personalized data products for Facebook. And in 2008, he and Patil separately began using the term “data scientist” when hiring employees. In 2012, Patil co-authored a Harvard Business Review article with academic Thomas Davenport titled “Data Scientist: The Sexiest Job of the 21st Century.” The piece, intended as a way to recruit talent to LinkedIn, argued that most data-collecting entities could benefit from having a data scientist to make sense of it all.

No organization faced a larger challenge in this realm than the U.S. government. In 2012, a survey of government IT workers revealed just how little data was put to use, revealing that one-third of all the information the government collected was “unstructured and therefore substantially less useful.” Not only were agencies not putting to work a large chunk of statistics they’d collected, a huge portion of them were of practically no value to begin with.

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Clockwise from top left: Megan Smith, David Recordon, Jason Goldman and Alexander Macgillivray. (Photos: Joi Ito via Flickr, Brian Solis via Flickr, Joi Ito via Flickr (2))

It was around this time that the Obama administration began scouring Silicon Valley for talent that could give the White House a digital boost. In 2012, prodigy health-tech entrepreneur Todd Park was poached to be Obama’s second chief technology officer, setting off a spree of similar hires. In the past two years alone the administration has hired Megan Smith, the VP of Google’s business development, to replace Park as CTO; Alexander Macgillivray, previously Twitter’s general counsel, was named her deputy. In March, one of Facebook’s lead engineers, David Recordon, joined the White House as its director of IT. And Jason Goldman, a Silicon Valley veteran who’s worked at Blogger, Twitter and Medium, was just named the White House’s first-ever chief digital officer.

When it came time to add a data scientist to the roster, Patil was the obvious choice.

“He’s an incredibly respected leader from the entire technology community, and being able to bring somebody in of his caliber is a signal to all of the technologists in the United States that [the White House is] really serious about bringing in some of the smartest people from outside of government,” Brian Forde, a former senior adviser for mobile and data innovation at the Office of Science and Technology Policy (OSTP), told Yahoo News.

Silicon Valley transplant

In February 2015, after years of ducking the traditional rules of educational and professional institutions, Patil got a job at the White House. Rather than simply put out a press release, the OSTP made digital waves with his hiring. The news first broke on Wired, and was followed up by a “memo” from Patil on Medium, a publishing platform darling among the media and Silicon Valley technologists. The post, which included a listicle made of SoundCloud embeds, declared that “the data age has arrived.” The next day, at the annual Strata + Hadoop World conference (a Comic-Con for data scientists), Patil gave a talk that was prefaced with a video of Obama welcoming him to the OSTP, stressing that “understanding and innovating with data has the potential to change almost anything for the better.”

Even before Patil joined, Obama made a concerted effort to make the White House more data-centric. In 2013, the president signed a long-overdue executive order that required machine-readable, open data to be the record-collecting standard in every government agency. Over the past few years, his administration has also released more than 135,000 data sets to the public, hosting events like hackathons, “Data Jams” and “Datapaloozas” — meetings in which statisticians, academics, industry leaders and bureaucrats gather to imagine new, helpful data applications and sometimes even build them. It has also developed the Presidential Innovation Fellows program, assigning young data scientists to start projects in agencies ranging from the Internal Revenue Service to the National Aeronautics and Space Administration. In 2014, the executive office of the president published its first “ Big Data report,” outlining the major benefits and concerns of the swaths of information it had accumulated over the years.

As Forde sees it, Obama’s decision to make these data sets public and foster a creative community around them marked the beginnings of a data revival after a long, period of inactivity.

“You had this really fertile farmland that was cemented over like a parking lot,” Forde said, referring to the government’s data sets. “We had to come in and — working with our agency partners — jackhammer all of that cement and just clear it out. Now we have the fertile farmland. You need someone like DJ Patil, who can harvest that crop. That’s what he’s doing right now.”

Patil’s first petri-dish initiative is to marry bioinformatics and health care in precision medicine. Doctors have been using this technique in a range of capacities for more than a century. Blood typing, for example, allows physicians to offer blood transfusions. Studying the unique genetic changes in cancer cells has led to the development of new drugs for degenerative diseases. By collecting data from these treatments, it’s possible to predict diseases in direct descendants of patients, or those who have similar genetic makeup.

By turning to Patil, Obama is placing a bet on an iconoclastic risk taker, a classic disrupter in the mold of Silicon Valley techies.

The health care private sector already does this to some extent. Organizations like Kaiser Permanente maintain records about which treatments are most cost-effective and encourage doctors to diagnose accordingly. But Patil’s goals go beyond the realm of cost efficiency, exploring the ways that, say, mapping someone’s genome could prevent future health risks in a family. Or demonstrating how the pollution of a certain area may affect the health of a community.

Patil is collaborating with one of his personal heroes, Dr. Francis Collins, the man who mapped the human genome, to explore the ways that data could inform medical treatments. A little over a month on the job, Patil is still finding his way. But he’s brimming with ideas for the future of health care.

“The patient has to be at the center of this,” he said, pausing for a second to think and diving into all the questions running through his mind. “What does it mean to have the opportunity to have care that is really tailored to a specific population? What are the right privacy mechanics? Will you get a magic pill that’s customized for you? That’s a long way of saying that it’s not obvious what it means when you walk in and you get your genome sequenced.”

But even the “Big Data report,” authored by counselor to the president John Podesta and other White House staffers, admits that the health care industry’s current privacy infrastructure might not yet be secure enough to ensure bioinformatics are used responsibly. “The nation needs to adopt universal standards and an architecture that will facilitate controlled access to information across many different types of records,” the report says. “Modernizing the health care data privacy framework will require careful negotiation between the many parties involved in delivering health care and insurance to Americans.”

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DJ Patil ascends the stairs at the White House. (Photo: Chris Usher for Yahoo News)

Yet another area that requires careful negotiation is the indiscriminate use of data collection by private companies in public settings. Take, for instance, educational apps that offer personalized learning experiences. By recording students’ online activities, these tools are able to adjust lesson plans based on the students’ strengths and weaknesses and evaluate their interests based on their Web habits. A recent report from The New York Times revealed that educational companies have bypassed school district privacy rules by marketing this technology directly to teachers, using student data at their own discretion. Though a federal law exists to protect students’ privacy in the classroom, critics argue that it doesn’t adequately address some of the advanced tracking techniques that companies are using.

In his January State of the Union address, Obama called on Congress to “protect our children’s information,” outlining a new bill to protect consumers and students under the age of 18. Though legislation supporting these efforts was supposed to be submitted on March 23, the bill remains stuck in limbo. Its latest draft would keep companies from using the data of students 18 and under for marketing purposes but allow it to be given away for “employment opportunities.” It would also allow companies to change privacy policies after a school commits to a contract for their services.

In his position, Patil won’t be a major driver of policy or politics, even in the Big Data realm. The best he can hope for is influencing the debate, at least on the margins, through the force of his arguments. Still, he is pleased with the White House’s stance on the issue.

“ The biggest challenge for DJ is going to be human and organizational, rather than technical.”– Steven Weber

“The thing that I’m really happy to see in the Podesta report is the fact that they called it out,” he said, referring to the “Big Data” report’s warning against the insidious collection techniques used by educational companies. “Let’s make sure the student data is not only utilized for the benefit of a student, but to make sure that student isn’t being marketed to in a way that I think we would all fundamentally say is not acceptable for the public good.”

In some ways it’s a topic close to his heart, considering that Patil himself could’ve benefited from targeted learning as a restless kid back at Monta Vista High School.

“If someone had just given me more flexibility to understand and look at the world differently and try new things, I probably would’ve gravitated to that,” he said.

This is how Patil sees the world: not as a dichotomy between data and intuition, but as a combination of both. Each project is an opportunity to readjust his perspective and try things a different way. It was that approach that got him into college, grad school, LinkedIn and ultimately the White House.

For more on government and healthcare applications of analytics, see Predictive Analytics World for Government, October 13-16, 2015 and PAW healthcare, September 27-Oct 1, 2015.

By: Alyssa Bereznak, Yahoo!
Originally published at www.yahoo.com

One thought on “Meet DJ Patil: Obama’s Big Data dude

  1. This article is very well written, Kudos to Ms. Alyssa Bereznak @Yahoo.
    The white Hiuse Initiatives on Personalized Medicine and on the Big Data Initiative for all .gov Databases with a focus on HealthCare is as big in importance as the NASA initiative launched by JFK.

     

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