Cover Page

Additional praise for The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

“In Too Big to Ignore, Phil Simon introduced us to the rapidly emerging world of Big Data. In this book, he tackles how we need to see, handle, and present this mountain of information, one unlike the old, familiar, transaction data that business people know quite well. The Visual Organization shines a much-needed light on how businesses are using contemporary data visualization tools.”

Brian Sommer

Enterprise Software Industry Analyst; ZDNet Contributor; CEO of TechVentive, Inc.

“The fourth wave of computing is upon us, and the visualization of information has never been more important. The Visual Organization arrives just in time. Simon's book helps enterprises learn from–and adapt to– this new adapt world. A must read.”

Larry Weber

Chairman and CEO of Racepoint Global and best-selling author

“Once again, Phil Simon has raised the bar. Like his other books, The Visual Organization takes a very current topic and instructs the reader on what not only what is being done, but what can be done. Simon provides a wealth of advice and examples, demonstrating how organizations can move from data production to data consumption and, ultimately, to action.”

Tony Fisher

Vice President Data Collaboration and Integration, Progress Software; Author of The Data Asset

“Today data is the new oil. Organizations need ways to quickly make sense of the mountains of data they are collecting. Bottom line: today visualization is more important than ever. The Visual Organization is a checkpoint on current dataviz methods. Simon's book represents insightful thought leadership that is sure to help any organization compete in an era of Big Data.”

William McKnight

President, McKnight Consulting Group; Author of Information Management: Strategies for Gaining a Competitive Advantage with Data

“Through fascinating case studies and stunning visuals, The Visual Organization demystifies data visualization. Simon charts the transformative effects of dataviz. Only through new tools and a new mind-set can organizations attempt to compete in a rapidly changing global environment.”

Chris Chute

Global Director, IDC

“A rollicking and incisive tour of the organizations pioneering the next big thing: putting visual data at the center of the enterprise. Simon's highly readable account points the way towards incorporating visualization into your own endeavors.”

Todd Silverstein

Entrepreneur and founder, Vizify

“Sure, Big Data is cool, but how can it move the needle? Today, it's essential to uncover insights far too often unseen, but how do you actually do that? The Visual Organization answers those questions—and more–in spades. Simon demonstrates how, when done correctly, dataviz promotes not only understanding, but action.”

Bill Schmarzo

CTO, EMC Global Services; Author of Big Data: Understanding How Data Powers Big Business

“Data visualization is a secret sauce for visionary executives in today's time-starved economy. Simon's book provides the Rosetta Stone on how to get there.”

Adrian C. Ott

CEO, Exponential Edge, and award-winning author of The 24-Hour Customer

“Phil Simon's latest book, The Visual Organization, superbly shows the potential of data visualization and how it can spark an organization's imagination. As Simon makes clear, visualization is how organizations can ask the right questions needed to create real value from their big data efforts; instead of fumbling about with them as too many do today.”

Robert Charette

President, ITABHI Corporation

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The Visual

Data Visualization, Big Data,
and the Quest for Better Decisions

Phil Simon

Wiley Logo

Other Books by Phil Simon

TO MY OTHER FAVORITE W.W.                




A good sketch is better than a long speech.
Quote often attributed to Napoleon Bonaparte

List of Figures and Tables

Item Description
Table I.1 Google Search Results on Three Different Types of Case Studies
Figure I.1 Vizify Phil Simon Profile
Figure I.2 Vizify Representation of @philsimon Tweets
Figure I.3 @philsimon Tweets by Hour of Day
Figure I.4 @philsimon Twitter Analytics
Figure I.5 Organizational Charts (2011)
Figure I.6 Visual Cues Ranking
Figure I.7 Preattentive Processing Test 1
Figure I.8 Preattentive Processing Test 2
Figure I.9 Graph of Google Search Results on Three Different Types of Case Studies
Figure 1.1 What Is Big Data?
Figure 1.2 The Internet in One Minute
Figure 1.3 Examples of Mainstream Open Datasets as of 2008
Figure 1.4 Nate Silver Speaking at SXSWi in 2009
Figure 1.5 LinkedIn Endorsements of Marillion Keyboardist Mark Kelly
Table 2.1 Reporting vs. Analysis vs. Dataviz
Table 2.2 Dataviz and BI Offerings of Established Enterprise Software Vendors
Figure 2.1 Breakdown of 2012 Lemonly Clients by Category
Figure 2.2 Breakdown of 2012 Lemonly Clients by Location and Category
Figure 2.3 The Startup Universe: A Visual Guide to Startups, Founders & Venture Capitalists; Investment History of Marc Andreessen
Figure 2.4 The Startup Universe: Investments in Tableau Software by Amount, Time, and Investor
Figure 2.5 The Startup Universe: Different Investments by New Enterprise over an 18-Month Period
Figure 3.1 Detailed Color Comparison of House of Cards and Macbeth
Figure 3.2 Detailed Color Comparison of Hemlock Grove, House of Cards, and Arrested Development
Figure 3.3 Screenshot of Lipstick
Figure 3.4 Netflix Content Consumed by Date, Hour, and Category
Figure 3.5 Netflix Breakdown of Streaming by Device (2011)
Figure 3.6 Netflix ISP Performance, Country Comparison—July 2013
Figure 3.7 Netflix ISP Performance, United States—July 2013
Figure 4.1 Frenemies Poll Data Breakdown
Figure 4.2 Poll: Who's the More Despicable Politician?
Figure 4.3 Jeff Gluck NASCAR Poll
Figure 4.4 Wedgies's Google Analytics, September 9, 2013
Table 5.1 UT Academic and Health Institutions
Figure 5.1 Data from UT 2009–10 Accountability Report
Figure 5.2 Student Success Dashboard
Figure 5.3 UT Time-to-Ph.D. (Subset of Programs)
Figure 5.4 UT Time-to-Ph.D. (Engineering Programs)
Figure 5.5 Total Dollars Spent under Contract, FY 2012
Figure 5.6 Total HUB Dollars Spent under Contract, FY 2012
Table 6.1 The Four-Level Visual Organization Framework (in Order of Descending Sophistication)
Figure 6.1 The Four-Level Visual Organization Framework
Figure 6.2 Potential Value and Insights from the Four-Level Visual Organization Framework
Figure 6.3 Heat Map of the Four-Level Visual Organization Framework
Figure 7.1 Series of Sequential Images from OrgOrgChart
Figure 7.2 OrgOrgChart Overview of March 17, 2009
Figure 7.3 OrgOrgChart Zoom-In of March 17, 2009
Figure 7.4 OrgOrgChart Overview of January 20, 2011
Figure 7.5 OrgOrgChart Zoom-In of January 20, 2011
Figure 8.1 Tale of 100 Entrepreneurs
Figure 8.2 Potential Fraud Network
Figure 8.3 Chart Suggestions—A Thought-Starter
Figure 9.1 Percentage Change in Enrollment by Disadvantaged Students in Russell Group Schools, 2005 to 2011
Figure C.1 Mobile App Usage

Preface: A Tale of Two IPOs

Every word or concept, clear as it may seem to be, has only a limited range of applicability.

—Werner Heisenberg

Christian Chabot had to be at least a little nervous when he woke up in Manhattan on the morning of May 17, 2013. More than a decade's worth of work would be coming to fruition in only a few hours. In 2003, Chabot—along with Chris Stolte and Pat Hanrahan—founded a little data-visualization company by the name of Tableau Software. (Tableau had started in 1996 as a research project at Stanford University funded by the U.S. Department of Defense.) Chabot served as the company's CEO, a position that he still holds today. At 9:30 a.m. EST on that May morning, Tableau would go public on the New York Stock Exchange with the apropos stock symbol of $DATA. Adding to the day's tension, Chabot and his team would be ringing the opening bell to commence the day's trading.

Now, under any circumstances, any company founder/CEO would be anxious about such a historic occasion. Chabot, however, was probably more restless than most in his position. Tableau's public launch was taking place in an environment best described as ominous. This initial public offering (IPO) was by no means a slam-dunk. To Chabot, the halcyon days of the dot-com era must have seemed like a million years ago. And, more recently, May 17, 2013, was almost exactly a year to the day after Facebook went public in arguably the most botched IPO in U.S. history. It was a day that would live in infamy.

Facebook was originally scheduled to begin trading on Nasdaq at 11:00 a.m. EST on May 18, 2012. In short, all did not go as planned. Trading was delayed for half an hour, a veritable lifetime on Wall Street. Amazingly, some investors who thought they had bought $FB shares didn't know for hours whether their transactions were actually executed. Aside from investor consternation, as Samantha Murphy wrote on Mashable, “The IPO caused a series of issues for finance sites, including and”1

That was a bit of an understatement.

Once trading finally began, things continued to spiral downward for Mark Zuckerberg's company. Originally priced at $42 per share, $FB quickly lost one-third of its value during that fateful day. The Securities and Exchange Commission investigated the glitches, ultimately fining Nasdaq $10 million. Lawsuits were soon filed. Many early Facebook investors like Peter Thiel sold virtually all their shares as soon as they legally could—and looked shrewd for doing so. At one point in 2012, the stock slid under $20 per share, and only in August 2013 did Facebook rise above its IPO price. As of this writing, investor sentiment finally seems to have shifted.

The Facebook IPO debacle—and resulting media frenzy—reverberated throughout the financial markets in mid-2012 and well into 2013. Its effects were felt far beyond the offices of Mark Zuckerberg, COO Sheryl Sandberg, rank-and-file employees, and investors. The Facebook IPO allegedly deterred many a company from listing on the NYSE and Nasdaq. Generally speaking, Wall Street analysts believed that the fiasco poisoned the short-term IPO well for everyone, especially technology companies. In the aftermath of the Facebook IPO, many high-profile companies, including Twitter,4 reportedly adjusted their own plans for going public. Of course, there were a few exceptions. Enterprise software companies Workday and Jive Software bravely went public in October and December of 2012, respectively. Their stock prices have held up relatively well after their IPOs, as did Big Data play Splunk.


On many levels, Tableau Software is the anti-Facebook. Yes, both companies rely upon cutting-edge technology to a large extent, but that's just about where the similarities end. In many ways, the two are apples and coconuts, and no intelligent investor would ever confuse the two.

Facebook is a consumer company based in Silicon Valley with a world-famous CEO. Tableau is an enterprise technology company based in Seattle, Washington. Compared to Zuckerberg, relatively few people would recognize Tableau's CEO on the street. Tableau doesn't sport anywhere near 1.2 billion users. Nor do its eponymous products seem terribly sexy to John Q. Public. In fact, most people would probably consider them a bit drab. At a high level, Tableau's offerings help people and organizations visualize data. This data need not be transactional, structured, and internal to an enterprise. Rather, Tableau can handle data from a wide range of sources, including proprietary relational databases, enterprise data warehouses and cubes, open datasets, spreadsheets, and more. Tableau's products “look” at data and allow users to easily create dashboards and highly interactive data visualizations. With a few clicks, users can publish and share them.5


The dubious IPO environment did not deter Chabot, Tableau's senior team, and its investors. They decided that the company would buck the IPO trend and ignore the pall that Facebook cast over the market. Tableau would roll the dice and go public.

So, how would Tableau pan out?

That was the big question for Chabot and company on May 17, 2013. Fortunately for Tableau's top brass, its first day of trading was spectacular and even redolent of the dot-com era. The company saw its stock skyrocket an astonishing 63 percent.2 When trading closed for the day, Tableau's market capitalization exceeded a whopping $2 billion.

Facebook notwithstanding, first-day bumps in a stock's first day of trading are relatively common, although 63 percent is a pretty big one. Company founders, early investors, and employees with equity or stock options celebrate early jumps like these—and rightfully so. At the same time, though, these gains are often fleeting, as investors are tempted to cash out and take profits. (Groupon and Zynga are but two recent examples of stocks that rose early only to quickly come crashing down to earth.) It was reasonable to ask, “Would Tableau's stock price maintain its lofty valuation?” In short, yes. After its initial jump, $DATA stabilized, largely holding on to its first-day gains.

I was watching the market the day of Tableau's successful IPO with considerable interest. Its opening and subsequent performance didn't surprise me. By way of background, I'm far from an expert on investing. I certainly don't purport to understand all the vicissitudes of the stock market, much less predict it with any accuracy. I don't read these tea leaves well, and my own investment record is borderline deplorable. (It pains me to think about how much I paid for $AAPL. Just think of me as the antithesis of Warren Buffett.) In a year, $DATA may trade at a fraction of its current price. We may be laughing at Wall Street's $2-billion-dollar valuation of a data-visualization company. After all, there's plenty of precedent here. The Street is far from perfect. Exhibit A: during the dot-com boom, sported a market capitalization north of $300 million. Whoops.

Sometimes, however, Wall Street gets it right. While it's still early, Tableau appears to be one of those cases.


The importance of Tableau Software's wildly successful IPO is difficult to overstate. It underscores the burgeoning importance of dataviz. Now, make no mistake. Many large, publicly traded software vendors like IBM, Oracle, SAP, and Microsoft sell applications that allow their clients to visualize data—and have for a long time. However, each of these vendors hawks a wide array of other business and technology solutions. IBM, Oracle, SAP, and Microsoft make their money by selling many different products and services. These include databases, back-office ERP and CRM applications, consulting, and custom software development. To each of these corporations, sales of proper data-visualization applications represent relatively negligible lines of business.

By contrast, Tableau is a different breed of cat. As of this writing, it is exclusively a dataviz company. Its products don't generate and store data, per se. Rather, at a high level, Tableau's solutions help organizations and their employees represent and interpret existing data, possibly making key discoveries in the process. Equipped with data presented in such a compelling format, employees are more likely to make better business decisions.

Whether more pure dataviz companies ultimately go public is immaterial. I for one don't expect a wave of similar IPOs in the next few years. For many reasons, many companies choose to remain private these days. (Not want­ing to deal with onerous government regulations and needling activist investors are usually near the top of the list.6) Many more start-ups and private companies actively seek exit strategies, perhaps “acqui-hires” by cash-flush behemoths like Facebook, Google, Twitter, and Yahoo.

One need not be an equities expert to understand that many factors explain the rise and fall of any individual stock. (As for me, I know enough to be dangerous.) At a high level, there are two types of variables. There are macro factors like the general economy, the unemployment rate, and the GDP growth. Then there are company-specific ones, including an organization's competition, cash flow, and strength of its management team. At the risk of simplifying, though, the immediate and blistering success of the Tableau IPO manifests a much larger business trend. Thousands of companies use Tableau, with more coming on board every day.

Now, Tableau may be the only pure data-visualization firm to go public (again, as of this writing), but it is hardly unique in its objectives:

As we'll see in the following pages, Tableau is just one of many companies that offers new and exciting ways to represent and interpret data, especially the big kind. Increasingly, dataviz is becoming a critical and even sexy topic. Awash in a sea of data, many organizations want—nay, need—tools that help them make sense of it all.

Powerful tech companies like Amazon, Apple, Facebook, Google, and Twitter understand data visualization, but they are hardly alone. Powerful dataviz is not the sole purview of Google-sized companies. As you'll see in this book, a wide array of organizations is representing data in amazing ways, deploying powerful data-visualization tools and building new ones. For instance, progressive and tech-savvy institutions like the Massachusetts Institute of Technology and the New York Times are hiring proper dataviz specialists and engineers.7 The Wall Street Journal is hiring visual journalists.3

And this trend shows no signs of abating. In fact, it's just getting started.

Today, data and dataviz are downright cool. In a few years, we may look back at May 17, 2013, as the dawn of a new type of company: the Visual Organization.

And that is the subject of this book.

Phil Simon

Henderson, Nevada

January 2014



Kudos to Tim Burgard, Sheck Cho, Stacey Rivera, Helen Cho, Evelyn Martinez, Andy Wheeler, Shelley Sessoms, Chris Gage, and the rest of Team Wiley for making this book possible. Additional kudos to Karen Gill, Johnna VanHoose Dinse, and Luke Fletcher.

Paula Bales, Stephanie Huie, Justin Matejka, Drew Skau, John T. Meyer, Jimmy Jacobson, Porter Haney, Joris Evers, Scott Kahler, Ernesto Olivares, and Scott Murray were generous with their time and expertise.

I am particularly grateful to Melinda Thielbar for helping me crystallize the Four-Level Visual Organization Framework in Chapter 6. Knowing a true data scientist has its advantages.

A tip of the hat to Adrian Ott, Terri Griffith, Bruce Webster, Scott “Caddy” Erichsen, Dalton Cervo, Jill Dyché, Todd Hamilton, Ellen French, Dick and Bonnie Denby, Kristen Eckstein, Bob Charette, Andrew Botwin, Mark Frank, Thor and Keri Sandell, Michael DeAngelo, Jennifer Zito, Chad Roberts, Mark Cenicola, Colin Hickey, Brian and Heather Morgan, Michael West, Kevin J. Anderson, John Spatola, Marc Paolella, and Angela Bowman.

Next up are the usual suspects: my longtime Carnegie Mellon friends Scott Berkun, David Sandberg, Michael Viola, Joe Mirza, and Chris McGee.

My heroes from Rush (Geddy, Alex, and Neil), Dream Theater (Jordan, John, John, Mike, and James), Marillion (h, Steve, Ian, Mark, and Pete), and Porcupine Tree (Steven, Colin, Gavin, John, and Richard) have given me many years of creative inspiration through their music. Keep on keepin' on!

A very special thank-you to Vince Gilligan, Bryan Cranston, Aaron Paul, Dean Norris, Anna Gunn, Betsy Brandt, Jonathan Banks, Giancarlo Esposito, RJ Mitte, Bob Odenkirk, and the rest of the cast and team of Breaking Bad. You took us on an amazing journey over the past six years. Each of you has made me want to do great work.

Finally, my parents. I wouldn't be here without you.

How to Help This Book

Thank you for buying The Visual Organization. I truly hope that you enjoy reading it and have learned a great deal in the process. Beyond some level of enjoyment and education (always admirable goals in reading a nonfiction book), I also hope that you can apply your newfound knowledge throughout your career.

And perhaps you are willing to help me. I am a self-employed author, writer, speaker, and consultant. I'm not independently wealthy and I don't have a large marketing machine getting my name out there. My professional livelihood depends in large part on my reputation, coupled with referrals and recommendations from people like you. Collectively, these enable me to make a living.

You can help this book by doing one or more of the following:

I don't expect to get rich by writing books. Michael Lewis, John Grisham, Stephen King, and Phil Simon. Hmmm . . . which one doesn't belong in that group? I write books for four reasons. First, I believe that have something meaningful to say. I like writing, editing, crafting a cover, and everything else that goes into writing books. To paraphrase the title of an album by Geddy Lee, it's my favorite headache. Second, although Kindles, Nooks, and iPads are downright cool, I really enjoy holding a physical copy of one of my books in my hands. Creating something physical from scratch just feels good to me. Next, I get a sense of satisfaction from creating a physical product. Finally, I believe that my books will make other good things happen for me.

At the same time, though, producing a quality text takes an enormous amount of time, effort, and money. Every additional copy sold helps make the next one possible.

Thanks again.



Book Overview and Background

Part I lays the foundation for the entire book. It covers why dataviz matters more than ever and includes the following chapters: