Predictive Analytics Times
Predictive Analytics Times
Exclusive Highlights
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
  To view this content OR subscribe for free...
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
  To view this content OR subscribe for free...
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
  To view this content OR subscribe for free...
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
  To view this content OR subscribe for free...
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
  To view this content OR subscribe for free...
Defining Measures of Success for Cluster Models
  To view this content OR subscribe for free...
Good luck placing Analytics in an org chart
  To view this content OR subscribe for free...
Retail Predictive Analytics Solves the Missing Link in Cross Selling, Up Selling, and Suggestive Selling
  To view this content OR subscribe for free...
Sameer Chopra’s Hotlist of Training Resources for Predictive Analytics
  To view this content OR subscribe for free...
Wise Practitioner – Predictive Analytics Interview Series: John Smits of EMC
  To view this content OR subscribe for free...
Be a Data Detective
  To view this content OR subscribe for free...
Predicting Employee Flight Risk: My Take
  To view this content OR subscribe for free...
The Key to Modelling Success -The Variable Selection Process (Part 1)
  To view this content OR subscribe for free...
Predictive Analytics World in Color [Infographic]
  To view this content OR subscribe for free...
Space Alien Eager to Convey Thoughts on Data Science
  To view this content OR subscribe for free...
Defining Measures of Success for Predictive Models
  Excerpted from Chapters 2 and 9 of his...
Overstatement of Results in Predictive Analytics
  To view this content OR subscribe for free...
The Biggest Lever to Success in Predictive Analytics
  To view this content OR subscribe for free...
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
  To view this content OR subscribe for free...
Leveraging Open Data: Improve Customer Experience and Drive New Market Opportunities
  To view this content OR subscribe for free...
From Code to Reports with knitr & Markdown
  To view this content OR subscribe for free...
Predictive Analytics Optimizes Prices and Markdowns for Retail
  To view this content OR subscribe for free...
Wise Practitioner – Predictive Analytics Interview Series: Delena D. Spann of US Government
  To view this content OR subscribe for free...
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
 This interview is the second in a series on...
Visualizations Get Some Snap from R Shiny
 “Numbers have an important story to tell. They rely...
What Programming do Predictive Modelers Need to Know?
 In most lists of the most popular software for...
Wise Practitioner – Workforce Predictive Analytics Interview Series: John Callery at AOL
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Analytic Professionals — Share your views: Participate in the Rexer Analytics 2015 Data Miner Survey
  Data Analysts, Predictive Modelers, Data Scientists, Data Miners,...
Charlie Batch and the Cost of Obfuscation
 The hot Florida sun shone down on fans, coaches,...
Predictive Analytics for Insurance Risk: A New Level of Data Scrutiny-Part 2-Development and Implementation
 For more on the application of predictive analytics and...
Is Big Data Better?
 Big data is usually defined as lots of records...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Chad Harness at Fifth Third Bank
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Building the Optimal Retail Assortment Plan with Predictive Analytics
 After making a financial plan for the year ahead,...
Using Advanced Clustering Techniques to Better Predict Purchasing Behaviors in Targeted Marketing Campaigns
 Today’s forward-thinking retailers are seeking relevant, agile and intelligent...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Patrick Coolen of ABN-AMRO Bank
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Using Predictive Analytics to Predict and Manage Business Travel Burnout
 For more on the application of predictive analytics for...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Scott Mondore at Strategic Management Decisions, LLC
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
Wise Practitioner – Predictive Analytics Interview Series: Bob Bress of Visible World
 In anticipation of his upcoming conference presentation, TV Audience...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller of Constellation Research, Inc.
 In anticipation of his upcoming Predictive Analytics World for Workforce keynote...
The Imminent Future of Predictive Modeling
 Predictive modeling tools and services are undergoing an inevitable...
Using Statistics and Visualization as Complementary Validation
 I don’t often find myself thinking or saying common...
Predictive Analytics for Insurance: A New Level of Data Scrutiny
 For more on the application of predictive analytics and...
The Three-Legged Stool of an Analytics Project
 Perhaps you have heard rumors going around that analytics...
Defining Measures of Success for Predictive Models
 Excerpted from Chapter 2 of Mr. Abbott’s book Applied...
Effective Framing of Predictive Analytic Projects
 For more from James Taylor, see his presentation on...
Wise Practitioner – Predictive Analytics Interview Series: Richard Boire of Boire Filler Group
 In anticipation of his upcoming conference presentation, Predicting Extreme...
Wise Practitioner – Predictive Analytics Interview Series: Sarah Holder of Duke Energy
 In anticipation of her upcoming conference presentation, It’s Not...
Infographic – Predictive Analytics World by the Numbers
 Predictive Analytics World continues to grow by popular demand....
Wise Practitioner – Predictive Analytics Interview Series: Mohamad Khatib of Nielsen
 In anticipation of his upcoming conference presentation, Pizza Analytics...
A Critical Step Toward Organizational Data Maturity: Thinking in Terms of Distributions!
 For more from the Josh Hemann, see his presentation...
It is a Mistake to…. Answer Every Inquiry
 (Part 9 (of 11) of the Top 10 Data...
Wise Practitioner – Predictive Analytics Interview Series: Dominic Fortin of TD Insurance
 In anticipation of his upcoming conference presentation, A Success...
5 Ways Retail Predictive Analytics helps Fashion Retailers Maximize Gross Margin
 Fashion retailers have one of the most dynamic environments...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Pasha Roberts at Talent Analytics
 In anticipation of his upcoming conference presentation, A Transaction-Based...
Predictive Modeling Skills: Expect to be Surprised
 Excerpted from Chapter 1 of Mr. Abbott’s book Applied...
Big Data: To Analyze or Not to Analyze
 Much has been written about Big Data and how...
Wise Practitioner – Predictive Analytics Interview Series: Bryan Guenther of RightShip
 In anticipation of his upcoming conference presentation, The Impact...
Wise Practitioner – Predictive Analytics Interview Series: Aaron Lanzen of Cisco
 In anticipation of his upcoming conference presentation, Integrating Predictive...
Wise Practitioner – Predictive Analytics Interview Series: David Schey of Digitas
 In anticipation of his upcoming conference presentation, Uplift Modeling...
It is a Mistake to…. Extrapolate
 (Part 8 (of 11) of the Top 10 Data...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Carl Schleyer of 3D Results
 In anticipation of his upcoming Predictive Analytics World for Workforce conference...
The Trouble with Numbers
 Previous discussions in other publications have often revolved around...
Wise Practitioner – Predictive Analytics Interview Series: Josh Hemann of Activision
 In anticipation of his upcoming conference presentation, Cheating Detection in...
Practical Predictive Modeling: Quick Variable Selection
 This post is largely excerpted from Dean Abbott’s book...
It is a Mistake to…. Discount Pesky Cases
 (Part 7 (of 11) of the Top 10 Data...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Scott Gillespie, Managing Partner of tClara
 In anticipation of his upcoming Predictive Analytics World for...
Want to Improve Your Prototype-to-Production Analytics Process? Embrace Thinking Inside the Box
 Anyone in the business of analytics knows that the...
Using Decision Trees in Variable Creation: Minimizing Information Loss-Part 1
 Numerous articles have been written about the use of...
Predictive Analytics turns Multi Channel Retailing into Omni Channel Retailing.
 Multi Channel retailing has existed for a very long...
Eric Siegel Discusses Predictive Analytics and Civil Liberties on KCRW’s Radio Show
  Last week on “To the Point,” an NPR-syndicated...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott, Smarter Remarketer
 In anticipation of his upcoming conference keynote and workshops...
Wise Practitioner – Predictive Analytics Interview Series: Elpida Ormanidou of Walmart
 In anticipation of Predictive Analytics World for Workforce, March...
Future of Analytics: Big Data Integration, Transforming Organizations and Processes, Providing Speed and Foresight
  With Analytics being a buzzword, most business executives...
Haystacks and Needles: Anomaly Detection
 Anomalies vs Outliers Anomaly detection, or finding needles in...
Auditing the Data When Deploying Predictive Analytics Solutions
 Much of the discussion in the predictive analytics discipline...
Leveraging Dark Data: Q&A with Melissa McCormack
  Melissa McCormack,Research Manager at predictive analytics research firm...
Wise Practitioner – Predictive Analytics Interview Series: Nephi Walton, M.D., Washington University/University of Utah
 In anticipation of his upcoming conference presentation at Predictive...
Using Predictive Modeling Algorithms for Non-Modeling Tasks
 It is obvious what predictive modeling algorithms like decision...
Wise Practitioner – Predictive Analytics Interview Series: Greta Roberts of Talent Analytics
 In anticipation of her upcoming keynote conference presentation at...
Wise Practitioner – Predictive Analytics Interview Series: George Savage, M.D., Proteus Digital
 In anticipation of his upcoming keynote conference presentation at...
From Human Screen to Machine: Predictive Analytics Helps Avoid a Major Point of Hiring Failure
 What is an employer’s most business-critical corporate process? At...
Predictive Analytics in Health Care: Helping to Navigate Uncertainties and Change
 A recent billion-dollar forecasting error in Walgreen’s Medicare-related business...
The Power of Predictive Analytics for Retail Replenishment
 Replenishment is an essential process in the retail supply...
Voice of the HR Profession: “Charts and Graphs are Hard to Follow”
 In early August, our Director of Marketing reached out...
Wise Practitioner – Predictive Analytics Interview Series: John Cromwell, M.D., University of Iowa Hospitals & Clinics
 In anticipation of his upcoming keynote conference presentation at...
Creating the All-important Analytical File-The Key Step in Building Successful Predictive Analytics Solutions
 In the data audit process, canned routines are programmed...
Wise Practitioner – Predictive Analytics Interview Series: Linda Miner, Ph.D., Southern Nazerene University
 In anticipation of her upcoming conference presentation at Predictive...
It is a Mistake to…. Accept Leaks from the Future
 (Part 6 (of 11) of the Top 10 Data...
Wise Practitioner – Predictive Analytics Interview Series: Marty Kohn, M.D. of Jointly Health
 In anticipation of his upcoming conference keynote at Predictive...
What’s the Government’s Role in Big Data Surveillance?
 What if predictive analytics could help prevent acts of...
Wise Practitioner – Predictive Analytics Interview Series: John Foreman of MailChimp
  In anticipation of his upcoming conference keynote at...
Is Predictive Analytics Insidious? National Radio Interview
 Interviewing Eric Siegel on the radio, a Hollywood big...
5 Reasons Predictive Analytics World for Workforce is Different – And Better
  If you follow the workforce analytics space, you...
Should Employee Analytics “Go Fishing” or Solve Business Problems?
 Over the years, our firm has had many discussions...
Wise Practitioner – Predictive Analytics Interview Series: Sameer Chopra of Orbitz
 In anticipation of his upcoming conference keynote at Predictive...
Wise Practitioner – Predictive Analytics Interview Series: Jack Levis of UPS
 In anticipation of his upcoming conference keynote presentation, UPS Analytics...
Connecting the Experts with the Data Scientists
 “Can Machines Think?” was the cover of Time magazine...
Why analysts should master public speaking
  Industry leader and consultant Geert Verstraeten serves as...
Defining the Target Variable in Predictive Analytics- A Not so Easy Process
 Defining a target variable is one of the preliminary...
Book Review of “Applied Predictive Analytics” by Dean Abbott
  Industry leader and author Dean Abbott will be...
Recognizing and Avoiding Overfitting, Part 1
 In my last two posts I described why overfitting...
Webinar: Towards Solving Employee Attrition: Cost Modeling
  Presented by: Pasha Roberts, Chief Scientist, Talent Analytics,...
It is a Mistake to…. Listen Only to the Data
 (Part 5 of 11 of the Top 10 Data...
The Data Audit Process (Part 1)-The Initial Step in Building Successful Predictive Analytics Solutions
 Building predictive analytics solutions is very much in-vogue for...
10 Practical Actions that Could Improve Your Model
   (adapted from Chapter 13 of the Handbook of...
The Great Analytical Divide: Data Scientist vs. Value Architect
 In the analytics space, it is quite common for...
Employee Churn 202: Good and Bad Churn
 Our prior article on this venue began outlining the...
Why Overfitting is More Dangerous than Just Poor Accuracy, Part II
 In part one, I described one problem with overfitting...
Predictive Analytics is the Answer to Smart Fulfillment and Omni-Channel Retailing
 Over the past 5 years there have been several...
Employee Churn 201: Calculating Employee Value
  Much has been written about customer churn –...
Why Overfitting is More Dangerous than Just Poor Accuracy, Part I
 Arguably, the most important safeguard in building predictive models...
5 Ways to Become Extinct as Big Data Evolves
 The need to adopt sophisticated data analytics has become...
It is a Mistake to…. Ask the Wrong Question
 (Part 4 (of 11) of the Top 10 Data...
What Role can Network Analysis play in Business Intelligence?
 Network analysis is an emerging Business Intelligence technique that’s...
The Data Behind Data Scientists: Top Kaggle Performers
 Kaggle, an online platform that hosts data analytics competitions,...
It’s Predictive Analytics, not Forecasting!
 This is my final article for this year. It’s...
A Good Business Objective Beats a Good Algorithm
 Predictive Modeling competitions, once the arena for a few...
Retail Predictive Analytics for Price Optimization & Markdown Management
 There is no doubt that price is one of...
It is a Mistake to…. Rely on One Technique
 (Part 3 of 11 of the Top 10 Data...
The Musings of a (Young) Data Scientist
I quit my job as a Mathematical Statistician after...
Big Data Continued…
Big Data is not a singular concept but rather...
How to Calculate the Optimal Safety Stock using Retail Predictive Analytics.
In a perfect world, a retailer knows exactly how...
The Role of Analysts After Model Deployment
Last month I made the case for discussing model...
How predictive analytics will power the internet of things
Recently, Nissan Motor announced that they will
Prediction Isn’t Just About Stocks. Predictive Persuasion
Prediction isn’t just for the stock market. Trading is...
The Greatest Power of Big Data: Predictive Analytics
Every day’s a struggle. I’ve faced some tough challenges...
It is a Mistake to… Focus on Training Results
(Part 2 of 11 of the Top 10 Data...
7 Ways Predictive Analytics Helps Retailers Manage Suppliers
One of the most challenging aspects of the retail...
Why Don’t We Talk about Deployment?
The Cross Industry Standard Process for Data Mining...
Understanding Predictive Analytics: A Spotlight Q&A with Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
This BeyeNETWORK spotlight features Ron Powell's interview with Eric...
Predictive Analytics: “Freakonomics” Meets Big Data
While writing my book, Predictive Analytics: The Power...
5 Reasons to Not Care About Predictive Analytics
Technology: complex and alienating, or promising and fascinating?...
SHARE THIS:

3 months ago
Data Story Telling: Bringing Life to Your Data

 

There is no doubt that a successful Data Scientist must be proficient in programming, modeling, and data munging (extracting, cleaning, and feature engineering data).  However, there is another key skill that is often overlooked:  the ability to communicate findings clearly and effectively. If you as a Data Scientist cannot motivate the business buy-in to effect change, your powerful model will collect dust on a shelf.  Stakeholders will only trust your model if they understand the value it adds, what has been done to create it, and why it works.  They should not be left to trust you and your “black box” blindly.  The solution is data storytelling: using the power of narrative to communicate your findings in a way that resonates with your stakeholders.  Doing this combines your data science expertise with intuitive visualizations and—most importantly—a story to connect the dots.

Data storytelling frequently employs data visualizations, but it involves much more than presenting a graph.  Data visualization is often static: a chart may represent a single facet of the data, or layers of features for a more complex concept.  Or, it can be an interactive dashboard where the viewer is free to experiment with different scenarios and reach their own conclusions.  Data storytelling takes these ideas a step further.  It guides the viewer through the process of formulating a question and leads them towards the desired conclusion in a step-by-step fashion. In short, it takes the viewer on a journey through the data.  This difference between data visualization and data storytelling is captured in Moritz Stefaner’s analogy comparing data visualization to portraits:

“[Data] can reveal stories, help us tell stories, but they are neither the story itself nor the storyteller. Portraits have no story to them either.  Like a photo portrait of a person, a visualization portrait of a data set can allow you to capture many facets of a bigger whole, but there is not a single story there, either.” [1]

Data storytelling marries data visualization with a guided narrative.  It pairs the data and the graphics with words, not only describing what can be seen in the image, but telling a story to lead you through the analysis process.  A narrative “is the way we simplify and make sense of a complex world,” [2] and data is certainly a complex world to understand.

So then, what does good storytelling entail?

First and foremost, it involves a good story – one focused on a very clear “data ask,” much like a thesis statement in a paper.  Let this ask lead the direction of the story in the same way it leads your work as a Data Scientist.  Be careful to remove the extraneous tangents encountered along the way and summarize your ask to avoid the complex details of the analytics.

The arc of a good story includes an exposition, rising action, climax, falling action, and conclusion.  The exposition is the setting of the data stage; what is the universe of data being examined?  The rising action explores the data, building up to the questions and feeding the viewer the data ask.  Your questions can include: What is happening? To whom? Where? When? and Why/How?  The climax will be the pivotal discovery in the data that makes it possible to answer these questions, and the falling action is the ultimate answer.  Lastly, and most importantly, comes the conclusion:  what is the one thing you want the viewer to leave with?  To keep the story cohesive, the whole story should build up to and support this conclusion—anything extraneous should be stripped away.

Additionally, if it is relevant to the data at hand, create a character to follow through this process.  Follow what their experience would be like in the data.  For instance, if dealing with churn, create a customer and follow their path; point out this character’s possible motivations (since they are the problem to solve), present the action that could be taken by the company, and show how that company’s action impacts the character’s probability of staying with the company.  Creating a character can make it easier to follow the narrative as viewers imagine themselves in this role.  William Proffitt’s Predictive Analytics Times article “Taking Action on Technical Success:  A Fable of Data Science and Consequences” is a good example of using a character to illustrate your data story [3].

Because data storytelling builds on data visualization, excellent visualizations are essential.  They are the foundation to data storytelling; without strong data visualizations to support your story, it will crumble.  This foundation includes more than just making sure parts are labeled correctly; you also need to choose the best visualization for the task, avoid cluttering the graph, and ensure that your figure tells a truthful story.

The first step is picking the right visualization form for the job.  This should be led by the data ask and the conclusion.  Are you showing differences over time, differences between categories, or differences based on location?  Variations of line graphs are good for showing changes over time because they connect the dots and illustrate the peaks, falls, and growth rate (or lack thereof).  If you are wanting to compare distinct categories, this is often done with bar graphs or any other graph that shows size and proportionality.  If geographic location is important, it is a good idea to actually visualize these locations with a map (Figure 1).  Additionally, if your story involves comparing entities, ensure that the structure of your graph allows for them to be easily compared (Figure 2).  These are some of the basic types of visualizations, but they can be combined or enhanced to show more detailed and complex points.

Description: Katrina’s Diaspora

Figure 1 A map showing the dispersion of individuals impacted by Hurricane Katrina.  This shows the relative distance that people have traveled, and also uses the size of points to illustrate the volume of individuals in a location.

[4]
Description: figs/incoming/06-TT-06.pngDescription: figs/incoming/06-TT-07.png

Figure 2 These graphs show a company’s sales across the different months in its different locations (shading of bars).  The first graph focuses on answering the question of which locations have the better sales and when.  The second graph focuses more on which months overall the company has better sales. [5]

Once you have chosen the visualization for the job, you need to be sure that it is presented in a non-deceiving manner.  An important factor in this step is correctly displaying your axes.  When using bar graphs, which use size of bars to show differences, it is best to show the full size of the bars by starting the axis at zero.  When presenting line graphs, it is usually okay to not start at zero so long as it is clearly noted; line graphs are primarily meant to show increases and decreases over time, rather than absolute size.  However, when displaying multiple trends on the same line graph, keep your axes consistent to avoid showing misleading relationships.  Expanding on the point with bar graphs, when using anything that utilizes size to illustrate a point, you should always ensure that the full size and area of the representative shapes are proportional to the data.  This is particularly important with bubble graphs or other shapes.  Using the data to set the radius/diameter of a bubble graph exaggerates differences in its areas, when the data should really be used to determine the area of the bubble since this is closer to what the viewer is registering as the measured dimension.

Description: https://qzprod.files.wordpress.com/2015/12/cwxb5crwwaekaib-large.jpg?quality=80&strip=all&w=640

Figure 3 Bar graph from the White House showing how graduation rates have changed since President Obama has been in office [6]

Figure 3 shows a bar graph tweeted by the White House.  The story this graph is intending to show is how graduation rates have improved under President Obama’s leadership.  One flaw with this graph is that, as a bar graph, the origin should start at zero, but by removing the bottom half of the image the proportionality is lost.  Figure 4 plots the same information, but with the origin starting at zero, causing the relative differences between the years to appear less drastic. Secondly, because this data is intended to show changes over time, a line graph would be more appropriate.  Arguably, if this were a line graph, the y-axis not starting at zero would not be as great a concern, though it would show a greater relative change than if the Y axis started at zero.

Description: C:\Users\LEANNA~1.KEN\AppData\Local\Temp\atlas_Nk8uVAfUg@2x.png

Figure 4   Bar graph from Figure 3 adjusted to have the Y axis start at zero [6]
Description: https://qzprod.files.wordpress.com/2015/12/high_school_graduation_rates_in_the_us_1975_to_2012_rate_chartbuilder.png?w=640

Figure 5   Line graph showing graduation rates from 1975-2012 [6]

There are also problems with the story the bar graph (Figure 3) is trying to tell.  The main flaw is that the graph implies that graduation increases are due to President Obama’s time in office, yet there is no evidence to support this conclusion in the graph.  Because this graph exclusively shows graduation rates during his terms, the viewer cannot see what graduation rates were before President Obama took office.  Did the rates drastically change once he took office, or is this a part of a long trend that started beforehand?  A line graph with the time frame extended to 1975 (Figure 5) shows that graduation rates were already increasing before President Obama took office, but that the graduation rates during his terms reached record highs.  It would also be useful to mark on the graph where any significant policy changes occurred so as to associate the changes in graduation rates to relevant political or economic events.

Description: https://qzprod.files.wordpress.com/2015/12/politifact-photos-mega-center-release-graphic.jpg?quality=80&strip=all&w=640

Figure 6   Chart created by Americans United for Life illustrating changes in spending on abortion services and cancer screenings and prevention. [6]

Figure 6 is a line graph from Americans United for Life that intends to show that abortion spending by Planned Parenthood has increased while the spending for cancer screening and prevention has decreased.  One flaw with this graph is that the lines each only connect two data points:  spending from 2006 and spending from 2013.  A line graph is intended to show continuous data, which would reveal rates and timing of change in the data as well.  While this graph successfully shows increases and decreases in spending for each service, it visually implies that these changes are equivalent in value.  This misperception is caused by putting each item’s spending on different scales, which also makes it appear as if spending for cancer screenings has dropped below the total amount spent on abortions.  This graph is essentially a dual-axes graph (without labeled axes), and those are advised against.


A)    B)

Figure 7 :  Alternative improvements to plotting the information from Figure 6


Finding the missing data, and creating a line graph with a single Y-axis (Figure 7) improves the presentation.  Which graph is “better” depends on the story you wish to tell.  Option A better reveals the relative expenditures; it shows that, though cancer screening spending has been cut in half in the seven years, it still is much higher than abortion spending.  On this absolute scale, the rise in abortion service spending—the intended story of the original graph’s author—is not evident in option A.  Option B makes the spending changes within each channel evident by making the y-axis represent the percent change since 2006.  It reveals that abortion spending has increased, and allows you to compare that percent increase to cancer screening’s percent decrease.  What option B lacks is a comparison of what the absolute spending is for each service, which could be alleviated by marking those values on the graph.  A comprehensive story then, might first show option A to give the viewer an idea of scale before presenting option B, showing relative changes.

Description: False Visualizations: Sizing Circles in Infographics Revised

Figure 8   The image on the left is the original chart presented in a Vox Media article, while the one on right has adjusted the sizing of the circles [7]

A less intuitive misstep in data visualization can be seen in Figure 8.  The chart is using circles to represent the size of donations to medical causes.  The error made here is that the radius, rather than the area of the circle, was made proportional to the data.  This calculation makes the observed differences seem much more extreme than they actually are, as seen in the corrected design.  It is important to make shapes have areas that are proportional to the values in the data.  Additionally, there are arguments [8] that most viewers struggle with properly perceiving area comparisons, which these graphs require.  Though it may be less visually exciting, it may be safer to stick with traditional bar graphs to avoid these possible errors and viewer misconceptions.

Lastly, once you have chosen the right graph to represent your data, a good visualization should not be cluttered.  Only add to the visualization what needs to be there to tell the story.  With data storytelling, this often means that the visualization should take up the majority of the space. You can add words to the image to guide the story, but they should be succinct and focused on ensuring that the viewer is taking away the important points.  Let the visualization do most of the talking.

To allow the visualizations their deserved attention, the narrative should be easy to follow and not require complex explanations.  If a single visualization has too many facets for it to be quickly and easily interpreted, break it down and build it up in layers.  Start with the basic concept of the visualization, and then slowly add the layers to the graphic as you delve further into the question or problem at hand.  The building of these layers makes the story easier to digest.

Some great examples of strong data story telling are exhibited by Tampa Bay TimesWhy Pinellas County is the worst place in Florida to be black and go to public school,” the R2D3 “Visual Introduction to Machine Learning,” and Hans Rosling’s Ted Talk “The Best Stats You’ve Ever Seen.”

The Tampa Bay Times example demonstrates the power of using minimal text with the graphic and building up the story in layers.  Its images are telling the story, and the words are there just to make sure the viewer is on the right track. Text never takes up more than two sentences’ worth of space at a time.  This example builds its layers by first orienting the focus (Pinellas County and its schools), and then adds in the other schools for reference.  The gradual steps in this story make it successful.

The R2D3 example focuses less on telling the story of its data, and more on telling the story of how a machine learning process works using the data as the “character” to follow.  It presents the problem of needing to classify New York or San Francisco homes, builds up the addition of multiple variables for classification, and then shows how the method’s resulting accuracy is achieved.  This concept can be exceptionally useful when needing to convince a stakeholder of how or why a given method works.

Another positive feature of the Tampa Bay Times article and the R2D3 story is that they both rely on computer scrolls to lead through the story instead of forcing the viewer to click on small parts like a dashboard might.  This feature makes them more mobile user friendly, which is especially important in the current age.  It also makes the stories more adaptable to print, and it is easy for readers’ eyes to scroll through them.

The words presented with great data storytelling don’t always have to be printed.  The concept of data storytelling carries over to the words spoken in a presentation, which is phenomenally exemplified by Hans Rosling’s Ted Talk.  Rosling uses the added elements of his tone and pacing to build up the story, and he personifies the different countries as characters to follow and root for.  Rosling also actively talks the audience through the visualization’s progression the same way the other storytelling examples added text to advance their stories.

Most importantly, when attempting data storytelling, remember that “whatever data we work with, when we share our insights, our goal is to move people to see things they haven’t seen before.” [2]  Let me emphasize the word “move”; you want to engage the viewer in asking questions about what comes next through the building of the story.  By telling a clear, cohesive, and interesting story with your data, stakeholders are given the opportunity to understand and trust your methods, and thus it becomes much easier to encourage actions based on the data’s insights.

Author Bio:

LeAnna KentLeAnna Kent is a data scientist for Elder Research, and enjoys blending programming and mathematics to gain understanding from data and guide decision making. Ms. Kent has practical experience with trend detection, unstructured data, and policy analysis. LeAnna has a Master’s in Analytics from North Carolina State University, and a BS in Mathematics with a minor in Computer Science from Rhodes College. In her free time, she enjoys painting, baking, crime shows, and logic puzzles.

One thought on “Data Story Telling: Bringing Life to Your Data

  1. Pingback: Data Visualization: Guided Narratives |

Leave a Reply