Alex Gutman, Jordan Goldmeier
Becoming a Data Head
How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

PRAISE FOR BECOMING A DATA HEAD

Big Data, Data Science, Machine Learning, Artificial Intelligence, Neural Networks, Deep Learning … It can be buzzword bingo, but make no mistake, everything is becoming “datafied” and an understanding of data problems and the data science toolset is becoming a requirement for every business person. Alex and Jordan have put together a must read whether you are just starting your journey or already in the thick of it. They made this complex space simple by breaking down the “data process” into understandable patterns and using everyday examples and events over our history to make the concepts relatable.

—Milen Mahadevan, President of 84.51°

What I love about this book is its remarkable breadth of topics covered, while maintaining a healthy depth in the content presented for each topic. I believe in the pedagogical concept of “Talking the Walk,” which means being able to explain the hard stuff in terms that broad audiences can grasp. Too many data science books are either too specialized in taking you down the deep paths of mathematics and coding (“Walking the Walk”) or too shallow in over-hyping the content with a plethora of shallow buzzwords (“Talking the Talk”). You can take a great walk down the pathways of the data field in Alex and Jordan's without fear of falling off the path. The journey and destination are well worth the trip, and the talk.

—Kirk Borne, Data Scientist, Top Worldwide Influencer in Data Science

The most clear, concise, and practical characterization of working in corporate analytics that I've seen. If you want to be a killer analyst and ask the right questions, this is for you.

—Kristen Kehrer, Data Moves Me, LLC, LinkedIn Top Voices in Data Science & Analytics

THE book that business and technology leaders need to read to fully understand the potential, power, AND limitations of data science.

—Jennifer L. L. Morgan, PhD, Analytical Chemist at Procter and Gamble

You've heard it before: “We need to be doing more machine learning. Why aren't we doing more sophisticated data science work?” Data science isn't the magic unicorn that will solve all of your company's problems. Data Head brings this idea to life by highlighting when data science is (and isn't) the right approach and the common pitfalls to watch out for, explaining it all in a way that a data novice can understand. This book will be my new “pocket reference” when communicating complicated concepts to non-technically trained leaders.

—Sandy Steiger, Director, Center for Analytics and Data Science at Miami University

Individuals and organizations want to be data driven. They say they are data driven. Becoming a Data Head shows them how to actually become data driven, without the assumption of a statistics or data background. This book is for anyone, or any organization, asking how to bring a data mindset to the whole company, not just those trained in the space.

—Eric Weber, Head of Experimentation & Metrics Research, Yelp

What is keeping data science from reaching its true potential? It is not slow algorithms, lack of data, lack of computing power, or even lack of data scientists. Becoming a Data Head tackles the biggest impediment to data science success—the communication gap between the data scientist and the executive. Gutman and Goldmeier provide creative explanations of data science techniques and how they are used with clear everyday relatable examples. Managers and executives, and anyone wanting to better understand data science will learn a lot from this book. Likewise, data scientists who find it challenging to explain what they are doing will also find great value in Becoming a Data Head.

—Jeffrey D. Camm, PhD, Center for Analytics Impact, Wake Forest University

Becoming a Data Head raises the level of education and knowledge in an industry desperate for clarity in thinking. A must read for those working with and within the growing field of data science and analytics.

—Dr. Stephen Chambal, VP for Corporate Growth at Perduco (DoD Analytics Company)

Gutman and Goldmeier filter through much of the noise to break down complex data and statistical concepts we hear today into basic examples and analogies that stick. Becoming a Data Head has enabled me to translate my team's data needs into more tangible business requirements that make sense for our organization. A great read if you want to communicate your data more effectively to drive your business and data science team forward!

—Justin Maurer, Engineering and Data Science Manager at Google

As an aerospace engineer with nearly 15 years experience, Becoming a Data Head made me aware of not only what I personally want to learn about data science, but also what I need to know professionally to operate in a data-rich environment. This book further discusses how to filter through often overused terms like artificial intelligence. This is a book for every mid-level program manager learning how to navigate the inevitable future of data science.

—Josh Keener, Aerospace Engineer and Program Manager

A must read for an in-depth understanding of data science for senior executives.

—Cade Saie, PhD, Chief Data Officer

Gutman and Goldmeier offer practical advice for asking the right questions, challenging assumptions, and avoiding common pitfalls. They strike a nice balance between thoroughly explaining concepts of data science while not getting lost in the weeds. This book is a useful addition to the toolbox of any analyst, data scientist, manager, executive, or anyone else who wants to become more comfortable with data science.

—Jeff Bialac, Senior Supply Chain Analyst at Kroger

Gutman and Goldmeier have written a book that is as useful for applied statisticians and data scientists as it is for business leaders and technical professionals. In demystifying these complex statistical topics, they have also created a common language that bridges the longstanding communication divide that has – until now – separated data work from business value.

—Kathleen Maley, Chief Analytics Officer at datazuum

Becoming a Data Head

How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

ALEX J. GUTMAN

JORDAN GOLDMEIER

Copyright © 2021 by John Wiley & Sons, Inc., Indianapolis, Indiana

Published simultaneously in Canada

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For my children Allie, William, and Ellen.

Allie was three when she discovered dad was a “doctor.” Puzzled, she looked at me and said, “But, you don't help people… .”

In that spirit, I also dedicate this book to you, the reader.

I hope this helps you.

—Alex

For Stephen and Melissa

—Jordan

About the Authors

Alex J. Gutman is a data scientist, corporate trainer, Fulbright Specialist grant recipient, and Accredited Professional Statistician® who enjoys teaching a wide variety of data science topics to technical and non-technical audiences. He earned his Ph.D. in applied math from the Air Force Institute of Technology where he currently serves as an adjunct professor.

Jordan Goldmeier is an internationally recognized analytics professional and data visualization expert, author, and speaker. A former chief operations officer at Excel.TV, he has spent years in the data training trenches. He is the author of Advanced Excel Essentials and Dashboards for Excel. His work has been cited by and quoted in the Associated Press, Bloomberg BusinessWeek, and American Express OPEN Forum. He is currently an Excel MVP Award holder, an achievement he's held for six years, allowing him to provide feedback and direction to Microsoft product teams. He once used Excel to save the Air Force $60 million. He is also a volunteer Emergency Medical Technician.

About the Technical Editors

William A. Brenneman is a Research Fellow and the Global Statistics Discipline Leader at Procter & Gamble in the Data and Modeling Sciences Department and an Adjunct Professor of Practice at Georgia Tech in the Stewart School of Industrial and Systems Engineering. Since joining P&G, he has worked on a wide range of projects that deal with statistics applications in his areas of expertise: design and analysis of experiments, robust parameter design, reliability engineering, statistical process control, computer experiments, machine learning, and statistical thinking. He was also instrumental in the development of an in-house statistics curriculum. He received a Ph.D. in Statistics from the University of Michigan, an MS in Mathematics from the University of Iowa, and a BA in Mathematics and Secondary Education from Tabor College. William is a Fellow in both the American Statistical Association (ASA) and the American Society for Quality (ASQ). He has served as ASQ Statistics Division Chair, ASA Quality and Productivity Section Chair, and as Associate Editor for Technometrics. William also has seven years of experience as an educator at the high school and college level.

Jennifer Stirrup is the Founder and CEO of Data Relish, a UK-based AI and Business Intelligence leadership boutique consultancy delivering data strategy and business-focused solutions. Jen is a recognized leading authority in AI and Business Intelligence Leadership, a Fortune 100 global speaker, and has been named as one of the Top 50 Global Data Visionaries, one of the Top Data Scientists to follow on Twitter, and one of the most influential Top 50 Women in Technology worldwide.

Jen has clients in 24 countries on 5 continents, and she holds postgraduate degrees in AI and Cognitive Science. Jen has authored books on data and artificial intelligence and has been featured on CBS Interactive and the BBC as well as other well-known podcasts, such as Digital Disrupted, Run As Radio, and her own Make Your Data Work webinar series.

Jen has also given keynotes for colleges and universities, as well as donated her expertise to charities and non-profits as a Non-Executive Director. All of Jen's keynotes are based on her 20+ years of global experience, dedication, and hard work.

Acknowledgments

I've noticed a trend in acknowledgment sections—the author's spouse is often mentioned at the end. I suppose it's a saving-the-best-for-last gesture, but I promised my wife if I ever wrote a book, I'd mention her first to make it perfectly clear whose contributions mattered most to me. So, to my wife Erin, thank you for your love, encouragement, and smile. As I write this, you are taking our three young children on a bike ride, giving me time to write one final page. (I assure all readers this act is a representative sample of our lives this past year.)

I'd also like to thank my parents, Ed and Nancy, for being the best cheerleaders in whatever I do and for showing me what being a good parent looks like, and to my siblings Ryan, Ross, and Erin for their support.

This book is the culmination of many discussions with friends and colleagues, ranging from whether I should attempt to write a book about data literacy to potential topics that should appear in it. Thank you especially to Altynbek Ismailov, Andy Neumeier, Bradley Boehmke, Brandon Greenwell, Brent Russell, Cade Saie, Caleb Goodreau, Carl Parson, Daniel Uppenkamp, Douglas Clarke, Greg Anderson, Jason Freels, Joel Chaney, Joseph Keller, Justin Maurer, Nathan Swigart, Phil Hartke, Samuel Reed, Shawn Schneider, Stephen Ferro, and Zachary Allen.

I'm also indebted to the hundreds of engineers, business professionals, and data scientists I've interacted with, personally or online, who've taught me how to be a better data scientist and communicator. And to my “students” (colleagues) who have given candid feedback about the courses I've taught, I heard you and I thank you.

I'm fortunate to have many academic and professional mentors who've given me numerous opportunities to find my voice and confidence as a statistician, data scientist, and trainer. Thank you to Jeffery Weir, John Tudorovic, K. T. Arasu, Raymond Hill, Rob Baker, Scott Crawford, Stephen Chambal, Tony White, and William Brenneman (who kindly served as a technical editor on this book). It's impossible not to become wiser hanging around a group like that.

Thanks to the team at Wiley: Jim Minatel for believing in the project and giving us a chance, Pete Gaughan and John Sleeva for guiding us through the process, and the production staff at Wiley for meticulously combing through our chapters. And to our technical editors, William Brenneman and Jen Stirrup, we appreciate your suggestions and expertise. The book is better because of you.

Special thanks to my coauthor Jordan Goldmeier, for one obvious reason (the book in your hands) and one not so obvious. Early in my career, I complained to Jordan that people didn't share my interest in statistics and statistical thinking. He said if I'm bothered by it, then it's my obligation to change it. I've been working to fulfill that obligation ever since.

Finally, I'd like to thank my wife Erin one final time (because you've got to save the best for last).

—Alex

I would like to acknowledge the many people who brought this book together.

First, and foremost, I would like to acknowledge my coauthor-in-crime, Alex Gutman. For years, we discussed writing a book together. When the moment was right, we pulled the trigger. I couldn't have asked for a better coauthor.

Thanks to the wonderful folks at Wiley who helped put this together, including acquisition editor Jim Minatel, and project editor John Sleeva. Also, I would like to acknowledge our technical editors, William Brenneman and Jen Stirrup for your hard work reviewing the book. We took your comments to heart.

Last but not least, thank you to my partner, Katie Gray, who always believed in this project—and me.

—Jordan

Foreword

Becoming a Data Head is well-timed for the current state of data and analytics within organizations. Let's quickly review some recent history. A few leading companies have made effective use of data and analytics to guide their decisions and actions for several decades, starting in the 1970s. But most ignored this important resource, or left it hiding in back rooms with little visibility or importance.

But in the early to mid-2000s this situation began to change, and companies began to get excited about the potential for data and analytics to transform their business situations. By the early 2010s, the excitement began to shift toward “big data,” which originally came from Internet companies but began to pop up across sophisticated economies. To deal with the increased volume and complexity of data, the “data scientist” role arose with companies—again, first in Silicon Valley, but then everywhere.

However, just as firms were beginning to adjust to big data, the emphasis shifted again—around about 2015 to 2018 in many firms—to a renewed focus on artificial intelligence. Collecting, storing, and analyzing big data gave way to machine learning, natural language processing, and automation.

Embedded within these rapid shifts in focus were a series of assumptions about data and analytics within organizations. I am happy to say that Becoming a Data Head violates many of them, and it's about time. As many who work with or closely observe these trends are beginning to admit, we have headed in some unproductive directions based on these assumptions. For the rest of this foreword, then, I'll describe five interrelated assumptions and how the ideas in this book justifiably run counter to them.

Assumption 1: Analytics, big data, and AI are wholly different phenomena.

It is assumed by many onlookers that “traditional” analytics, big data, and AI are separate and different phenomena. Becoming a Data Head, however, correctly adopts the view that they are highly interrelated. All of them involve statistical thinking. Traditional analytics approaches like regression analysis are used in all three, as are data visualization techniques. Predictive analytics is basically the same thing as supervised machine learning. And most techniques for data analysis work on any size of dataset. In short, a good Data Head can work effectively across all three, and spending a lot of time focusing on the differences among them isn't terribly productive.

Assumption 2: Data scientists are the only people who can play in this sandbox.

We have lionized data scientists and have often made the assumption that they are the only people who can work effectively with data and analytics. However, there is a nascent but important move toward the democratization of these ideas; increasing numbers of organizations are empowering “citizen data scientists.” Automated machine learning tools make it easier to create models that do an excellent job of predicting. There is still a need, of course, for professional data scientists to develop new algorithms and check the work of the citizens who do complex analysis. But organizations that democratize analytics and data science—putting their “amateur” Data Heads to work—can greatly increase their overall use of these important capabilities.

Assumption 3: Data scientists are “unicorns” who have all the skills needed for these activities.

We have assumed that data scientists—those trained in and focused upon the development and coding of models—are also able to perform all the other tasks that are required for full implementation of those models. In other words, we think they are “unicorns” who can do it all. But such unicorns don't exist at all, or exist only in small numbers. Data Heads who not only understand the rudiments of data science, but also know the business, can manage projects effectively, and are excellent at building business relationships will be extremely valuable in data science projects. They can be productive members of data science teams and increase the likelihood that data science projects will lead to business value.

Assumption 4: You need to have a really high quantitative IQ and lots of training to succeed with data and analytics.

A related assumption is that in order to do data science work, a person has to be very well trained in the field and that a Data Head requires a head that is very good with numbers. Both quantitative training and aptitude certainly help, but Becoming a Data Head argues—and I agree—that a motivated learner can master enough of data and analytics to be quite useful on data science projects. This is in part because the general principles of statistical analysis are by no means rocket science, and also because “being useful” on data science projects doesn't require an extremely high level of data and analytics mastery. Working with professional data scientists or automated AI programs only requires the ability and the curiosity to ask good questions, to make connections between business issues and quantitative results, and to look out for dubious assumptions.

Assumption 5: If you didn't study mostly quantitative fields in college or graduate school, it's too late for you to learn what you need to work with data and analytics.

This assumption is supported by survey data; in a 2019 survey report from Splunk of about 1300 global executives, virtually every respondent (98%) agreed that data skills are important to the jobs of tomorrow.¹ 81% of the executives agree that data skills are required to become a senior leader in their companies, and 85% agree that data skills will become more valuable in their firms. Nonetheless, 67% say they are not comfortable accessing or using data themselves, 73% feel that data skills are harder to learn than other business skills, and 53% believe they are too old to learn data skills. This “data defeatism” is damaging to individuals and organizations, and neither the authors of this book nor I believe it is warranted. Peruse the pages following this foreword, and you will see that no rocket science is involved!

So forget these false assumptions, and turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful. This is the way the world is going, so it's time to get with the program and learn more about data and analytics. I think you will find the process—and the reading of Becoming a Data Head—more rewarding and more pleasant than you may imagine.

Thomas H. Davenport

Distinguished Professor, Babson College

Visiting Professor, Oxford Saïd Business School

Research Fellow, MIT Initiative on the Digital Economy

Author of Competing on Analytics, Big Data @ Work, and The AI Advantage

Introduction

Data is perhaps the single most important aspect to your job, whether you want it to be or not. And you're likely reading this book because you want to be able to understand what it's all about.

To begin, it's worth stating what has almost become cliché: we create and consume more information than ever before. Without a doubt, we are in the age of data. And this age of data has created an entire industry of promises, buzzwords, and products many of which you, your managers, colleagues, and subordinates are or will be using. But, despite the claims and proliferation of data promises and products, data science projects are failing at alarming rates.²

To be sure, we're not saying all data promises are empty or all products are terrible. Rather, to truly get your head around this space, you must embrace a fundamental truth: this stuff is complex. Working with data is about numbers, nuance, and uncertainty. Data is important, yes, but it's rarely simple. And yet, there is an entire industry that would have us think otherwise. An industry that promises certainty in an uncertain world and plays on companies’ fear of missing out. We, the authors, call this the Data Science Industrial Complex.

THE DATA SCIENCE INDUSTRIAL COMPLEX

It's a problem for everyone involved. Businesses endlessly pursue products that will do their thinking for them. Managers hire analytics professionals who really aren't. Data scientists are hired to work in companies that aren't ready for them. Executives are forced to listen to technobabble and pretend to understand. Projects stall. Money is wasted.

Meanwhile, the Data Science Industrial Complex is churning out new concepts faster than our ability to define and articulate the opportunities (and problems) they create. Blink, and you'll miss one. When your authors started working together, Big Data was all the rage. As time went on, data science became the hot new topic. Since then, machine learning, deep learning, and artificial intelligence have become the next focus.

To the curious and critical thinkers among us, something doesn't sit well. Are the problems really new? Or are these new definitions just rebranding old problems?

The answer, of course, is yes to both.

But the bigger question we hope you're asking yourself is, How can I think and speak critically about data?

Let us show you how.

By reading this book, you'll learn the tools, terms, and thinking necessary to navigate the Data Science Industrial Complex. You'll understand data and its challenges at a deeper level. You'll be able to think critically about the data and results you come across, and you'll be able to speak intelligently about all things data.

In short, you'll become a Data Head.

WHY WE CARE

Before we get into the details, it's worth discussing why your authors, Alex and Jordan, care so much about this topic. In this section, we share two important examples of how data affected society at large and impacted us personally.

The Subprime Mortgage Crises

We were fresh out of college when the subprime mortgage crisis hit. We both landed jobs in 2009 for the Air Force, at a time when jobs were hard to find. We were both lucky. We had an in-demand skill: working with data. We had our hands in data every single day, working to operationalize research from Air Force analysts and scientists into products the government could use. Our hiring would be a harbinger of the focus the country would soon place on the types of roles we filled. As two data workers, we looked on the mortgage crisis with interest and curiosity.

The subprime mortgage crises had a lot of contributing factors behind it.³ In our attempt to offer it up as an example here, we don't want to negate other factors. However, put simply, we see it as a major data failure. Banks and investors created models to understand the value of mortgage-backed collateralized debt obligations (CDOs). You might remember those as the investment vehicles behind the United States’ market collapse.

Mortgage-backed CDOs were thought to be a safe investment because they spread the risk associated with loan default across multiple investment units. The idea was that in a portfolio of mortgages, if only a few went into default, this would not materially affect the underlying value of the entire portfolio.

And yet, upon reflection we know that some fundamental underlying assumptions were wrong. Chief among them were that default outcomes were independent events. If Person A defaults on a loan, it wouldn't impact Person B's risk of default. We would all soon learn defaults functioned more like dominoes where a previous default could predict further defaults. When one mortgage defaulted, the property values surrounding the home dropped, and the risk of defaults on those homes increased. The default effectively dragged the neighboring houses down into a sinkhole.

Assuming independence when events are in fact connected is a common error in statistics.

But let's go further into this story. Investment banks created models that overvalued these investments. A model, which we'll talk about later in the book, is a deliberate oversimplification of reality. It uses assumptions about the real world in an attempt to understand and make predictions about certain phenomena.

And who were these people who created and understood these models? They were the people who would lay the groundwork for what today we call the data scientist. Our kind of people. Statisticians, economists, physicists—folks who did machine learning, artificial intelligence, and statistics. They worked with data. And they were smart. Super smart.

And yet, something went wrong. Did they not ask the correct questions of their work? Were disclosures of risk lost in a game of telephone from the analysts to the decision makers, with uncertainty being stripped away piece by piece, giving an illusion of a perfectly predictable housing market? Did the people involved flat out lie about results?

More personal to us, how could we avoid similar mistakes in our own work?

We had many questions and could only speculate the answers, but one thing was clear—this was a large-scale data disaster at work. And it wouldn't be the last.

The 2016 United States General Election

On November 8, 2016, the Republican candidate, Donald J. Trump, won the general election of the United States beating the assumed front-runner and Democratic challenger, Hillary Clinton. For the political pollsters this came as a shock. Their models hadn't predicted his win. And this was supposed to be the year for election prediction.

In 2008, Nate Silver's FiveThirtyEight blog—then part of The New York Times—had done a fantastic job predicting Barack Obama's win. At the time, pundits were skeptical that his forecasting algorithm could accurately predict the election. In 2012, once again, Nate Silver was front and center predicting another win for Barack Obama.

By this point, the business world was starting to embrace data and hire data scientists. The successful prediction by Nate Silver of Barack Obama's reelection only reinforced the importance and perhaps oracle-like abilities of forecasting with data. Articles in business magazines warned executives to adopt data or be swallowed by a data-driven competitor. The Data Science Industrial Complex was in full force.

By 2016, every major news outlet had invested in a prediction algorithm to forecast the general election outcome. The vast, vast majority of them by and large suggested an overwhelming victory for the Democratic candidate, Hillary Clinton. Oh, how wrong they were.

Let's contrast how wrong they were as we compare it against the subprime mortgage crisis. One could argue that we learned a lot from the past. That interest in data science would give rise to avoiding past mistakes. Yes, it's true: since 2008—and 2012—news organizations hired data scientists, invested in polling research, created data teams, and spent more money ensuring they received good data.

Which begs the question: with all that time, money, effort, and education—what happened?⁴

Our Hypothesis

Why do data problems like this occur? We assign three causes: hard problems, lack of critical thinking, and poor communication.

First (as we said earlier), this stuff is complex. Many data problems are fundamentally difficult. Even with lots of data, the right tools and techniques, and the smartest analysts, mistakes happen. Predictions can and will be wrong. This is not a criticism of data and statistics. It's simply reality.

Second, some analysts and stakeholders stopped thinking critically about data problems. The Data Science Industrial Complex, in its hubris, painted a picture of certainty and simplicity, and a subset of people drank the proverbial “Kool-Aid.” Perhaps it's human nature—people don't want to admit they don't know what is going to happen. But a key part of thinking about and using data correctly is recognizing wrong decisions can happen. This means communicating and understanding risks and uncertainties. Somehow this message got lost. While we'd hope the tremendous progress in research and methods in data and analysis would sharpen everyone's critical thinking, it caused some to turn it off.

The third reason we think data problems continue to occur is poor communication between data scientists and decision makers. Even with the best intentions, results are often lost in translation. Decision makers don't speak the language because no one bothered to teach data literacy. And, frankly, data workers don't always explain things well. There's a communication gap.

DATA IN THE WORKPLACE

Your data problems might not bring down the global economy or incorrectly predict the next president of the United States, but the context of these stories is important. If miscommunication, misunderstanding, and lapses in critical thinking occur when the world is watching, they're probably happening in your workplace. In most cases, these are micro failures subtly reinforcing a culture without data literacy.

We know it's happened in our workplace, and it was partly our fault.

The Boardroom Scene

Fans of science fiction and adventure movies know this scene all too well: The hero is faced with a seemingly unsurmountable task and the world's leaders and scientists are brought together to discuss the situation. One scientist, the nerdiest among the group, proposes an idea dropping esoteric jargon before the general barks, “Speak English!” At this point, the viewer receives some exposition that explains what was meant. The idea of this plot point is to translate what is otherwise mission-critical information into something not just our hero—but the viewer—can understand.

We've discussed this movie trope often in our roles as researchers for the federal government. Why? Because it never seemed to unfold this way. In fact, what we saw early in our careers was often the opposite of this movie moment.

We presented our work to blank stares, listless head nodding, and occasional heavy eyelids. We watched as confused audiences seemed to receive what we were saying without question. They were either impressed by how smart we seemed or bored because they didn't get it. No one demanded we repeat what was said in a language everyone could understand. We saw something unfold that was dramatically different. It often unfolded like this:

Us: “Based on our supervised learning analysis of the binary response variable using multiple logistic regression, we found an out-of-sample performance of 0.76 specificity and several statistically significant independent variables using alpha equal to 0.05.”

Business Professional: *awkward silence*

Us: “Does that make sense?”

Business Professional: *more silence*

Us: “Any questions?”

Business Professional: “No questions at the moment.”

Business Professional's internal monologue: “What the hell are they talking about?”

If you watched this unfold in a movie, you might think wait, let's rewind, perhaps I forgot something. But in real life, where choices are truly mission critical, this rarely happens. We don't rewind. We don't ask for clarification.

In hindsight, our presentations were too technical. Part of the reason was pure stubbornness—before the mortgage crisis, as we learned, technical details were oversimplified; analysts were brought in to tell decision makers what they wanted to hear—and we were not going to play that game. Our audiences would listen to us.

But we overcorrected. Audiences couldn't think critically about our work because they didn't understand what we said.

We thought to ourselves there's got to be a better way. We wanted to make a difference with our work. So we started practicing explaining complex statistical concepts to each other and to other audiences. And we started researching what others thought about our explanations.

We discovered a middle ground between data workers and business professionals where honest discussions about data can take place without being too technical or too simplified. It involves both sides thinking more critically about data problems, large or small. That's what this book is about.

YOU CAN UNDERSTAND THE BIG PICTURE

To become better at understanding and working with data you will need to be open to learning seemingly complicated data concepts. And, even if you already know these concepts, we'll teach you how to translate them to your audience of stakeholders.

You'll also have to embrace the side of data that's not often talked about—how, in many companies, it largely fails. You'll build intuition, appreciation, and healthy skepticism of the numbers and terms you come across. It may seem like a daunting task, but this book will show you how. And you won't need to code or have a Ph.D.

With clear explanations, thought exercises, and analogies, we will help you develop a mental framework of data science, statistics, and machine learning.

Let's do just that in the following example.

Classifying Restaurants

Imagine you're on a walk and pass by an empty store front with the sign “New Restaurant: Coming Soon.” You're tired of eating at national chains and are always on the lookout for new, locally owned restaurants, so you can't help but wonder, “Will this be a new local restaurant?”

Let's pose this question more formally: Do you predict the new restaurant will be a chain restaurant or an independent restaurant?

Take a guess. (Seriously, take a guess before moving on.)

If this scenario happened in real life, you'd have a pretty good hunch in a split second. If you're in a trendy neighborhood, surrounded by local pubs and eateries, you'd guess independent. If you're next to an interstate highway and near a shopping mall, you'd guess chain.

But when we asked the question, you hesitated. They didn't give me enough information, you thought. And you were right. We didn't give you any data to make a decision.

Lesson learned: Informed decisions require data.

Now look at the data in the first image on the next page. The new restaurant is marked with an X, the Cs indicate chain restaurants, and the Is indicate independent, local eateries. What would you guess this time?

Most people guess (I) because most of the surrounding restaurants are also (I). But notice not all restaurants in the neighborhood are independent. If we asked you to rate your confidence⁵ in your prediction between 0 and 100, we'd expect it to be high but not 100. It's certainly possible another chain restaurant is coming to the neighborhood.

Lesson learned: Predictions should never be 100% confident.

Over the Rhine neighborhood, Cincinnati, Ohio

Next, look at the data in the following image. This area includes a large shopping mall, and most restaurants in the area are chains. When asked to predict chain or independent, the majority choose (C). But we love when someone chooses (I) because it highlights several important lessons.

Kenwood Towne Centre, Cincinnati, Ohio

During this thought experiment, everyone creates a slightly different algorithm in their head. Of course, everyone looks at the markers surrounding the point of interest, X, to understand the neighborhood, but at some point, you must decide when a restaurant is too far away to influence your prediction. At one extreme (and we see it happen), someone looks at the restaurant's single closest neighbor, in this case an independent restaurant, and bases their prediction on it: “The nearest neighbor to X is an (I), so my prediction is (I).”

Most people, however, look at several neighboring restaurants. The second image shows a circle surrounding the new restaurant containing its seven nearest neighbors. You probably chose a different number, but we chose 7, and 6 out of the 7 are (C) chains, so we'd predict (C).

So What?

If you understand the restaurant example, you're well on your way to becoming a Data Head. Let's reveal what you learned, little by little:

■ You performed classification by predicting the label (chain or independent) on a new restaurant by training an algorithm using a set of data (restaurants’ location and their chain/independent label).

■ This is precisely machine learning! You just didn't build the algorithm on a computer—you used your head.

■ Specifically, this is a type of machine learning called supervised learning. It was “supervised” because you knew the existing restaurants were (C) chain or (I) independent. The labels directed (i.e., supervised) your thinking about how restaurant location is related to whether it's a chain or not.

■ Even more specifically, you performed a supervised learning classification algorithm called K-nearest-neighbor.⁶ If K = 1, look at the closest restaurant and that's your prediction. If K = 7, look at the 7 closest restaurants and predict the majority. It's an intuitive and powerful algorithm. And it's not magic.

■ You also learned you need data to make informed decisions. Realize, however, that you need more than that. After all, this book is about critical thinking. We want to show how stuff works but also how it fails. If we asked you to predict, given the data in this Introduction's images, if the new restaurant would be kid-friendly, you wouldn't be able to answer. To make informed decisions, not just any data will do. You need accurate, relevant, and enough data.

■ Remember the technobabble we wrote earlier? “… supervised learning analysis of the binary response variable …”? Congratulations, you just did a supervised learning analysis of a binary response variable. Response variable is another name for label, and it's binary because there were two of them, (C) and (I).

You learned a lot in this section, and you did it without even realizing it.

WHO THIS BOOK IS WRITTEN FOR

As established at the beginning of this book, data touches the lives of many of today's corporate workers. We came up with the following avatars to represent people who will benefit from becoming a Data Head:

Michelle is a marketing professional who works side-by-side with a data analyst. She develops the marketing initiatives and her data coworker collects data and measures the initiatives’ impact. Michelle thinks they can do more innovative work, but she can't articulate her data and analysis needs effectively to her data coworker. Communication between the two is a challenge. She's Googled some of the buzzwords floating around (machine learning and predictive analytics), but most of the articles she found used overly technical definitions, contained indecipherable computer code, or were advertisements for analytical software or consultation services. Her search left her feeling more anxious and confused than before.

Doug has a Ph.D. in the life sciences and works for a large corporation in its Research & Development division. Skeptical by nature, he wonders if these latest data trends are akin to snake oil. But Doug mutes his skepticism in the workplace, especially around his new director who wears a “Data is the New Bacon” t-shirt; he doesn't want to be viewed as a data luddite. At the same time, he's feeling left behind and decides to learn what all the fuss is about.

Regina is a C-level executive who is well-aware of the latest trends in data science. She oversees her company's new Data Science Division and interacts with senior data scientists on a regular basis. Regina trusts her data scientists and champions their work, but she'd like to have a deeper understanding of what they do because she's frequently presenting and defending her team's work to the company's board of directors. Regina is also tasked with vetting new technology software for the company. She suspects some of the vendors’ claims about “artificial intelligence” are too good to be true and wants to arm herself with more technical knowledge to separate marketing claims from reality.

Nelson manages three data scientists in his new role. A computer scientist by training, Nelson knows how to write software and work with data, but he's new to statistics (other than one class he took in college) and machine learning. Given his somewhat related technical background, he's willing and able to learn the details, but simply can't find time. His management has also been pushing his team to “do more machine learning,” but at this point, it all seems like a magic black box. Nelson is searching for material to help him build credibility within his team and recognize what problems can and cannot be solved with machine learning.

Hopefully, you can identify with one or more of these personas. The common thread among them, and likely you, is the desire to become a better “consumer” of the data and analytics you come across.

We also created an avatar to represent people who should read this book but probably won't (because every story needs a villain):

George: A mid-level manager, George reads the latest business articles about artificial intelligence and forwards his favorites up and down his management chain as evidence of his technical trendiness. But in the boardroom, he prides himself on “going with his gut.” George likes his data scientists to spoon-feed him the numbers in one or two slides, max. When the analysis agrees with what he (and his gut) decided before he commissioned the study, he moves it up the chain and boasts to his peers about enabling an “Artificial Intelligence Enterprise.” If the analysis disagrees with his gut feeling, he interrogates his data scientists with a series of nebulous questions and sends them on a wild goose chase until they find the “evidence” he needs to push his project forward.

Don't be like George. If you know a “George,” recommend this book and say they reminded you of “Regina.”

WHY WE WROTE THIS BOOK

We think a lot of people, like our avatars, want to learn about data and don't know where to start. Existing books in data science and statistics span a wide spectrum. On one side of the spectrum are non-technical books extolling the virtues and promise of data. Some of them are better than others. Even the best ones feel like the modern-day business books. But many of them are written by journalists looking to add drama around the rise of data.

These books describe how specific business problems were solved by looking at a problem through the lens of data. And they might even use words like artificial intelligence, machine learning, and the like. Don't get us wrong, these books create awareness. However, they don't delve deeply into what was done, instead focusing specifically on the problem and the solution at a high level.

On the other side of the spectrum are highly technical books. These hardbound, 500-page tomes are as intimidating physically as the content inside is intimidating mentally.

The far sides of this spectrum have mountains of books. This perpetuates the communication gap—most people either read just the business books or just the technical books. Not both.

Thankfully, the gorge between the two extremes contains a handful of excellent books. Two of our favorites are:

■ Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost and Tom Fawcett (O'Reilly Media, 2013)

■ Data Smart: Using Data Science to Transform Information into Insight, by John W. Foreman (Wiley, 2013)

We want to add one more to this list by writing a book you can read casually without a computer or pad of paper nearby. If you enjoy our book, we highly recommend taking the next step by reading one or both of the books listed to solidify your understanding. You won't regret it.

Plus, we love this stuff. If we can convey that to you and motivate you to learn more about data and analytics—and inspire you to want to learn more—we'll consider this book a success.

WHAT YOU'LL LEARN

This book will help you construct a mental model of data science, statistics, and machine learning. What is a mental model? It's “a simplified representation of the most important parts of some problem domain that is good enough to enable problem solving.”⁷ Think of it as a new storage room in your brain where you can put information.

Some books and articles start with a list of definitions: “Machine Learning is …”, “Deep Learning is …”, etc. Seeing a list of technical definitions without a mental model to fit the information into would be like someone dropping off boxes of clothes when you don't have a place to store them. Sooner or later, it's all going to end up in the garbage.

But with a newly constructed mental model, you will learn how to think, speak, and understand data. You'll become a Data Head.

Specifically, by reading this book, you will be able to:

■ Think statistically and understand the role variation plays in your life and decision making.

■ Become data literate—speak intelligently and ask the right questions about the statistics and results you encounter in the workplace.

■ Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence.

■ Avoid common pitfalls when working with and interpreting data.

HOW THIS BOOK IS ORGANIZED

Data Heads are people who know how to think critically about data, regardless of their official role. A Data Head can be the analyst behind the keyboard doing the work, or the person at the head of the boardroom table reviewing the work of others. This book will put you, the Data Head, in various roles at different points.

While the “story” of the book is chronological, each chapter is effectively a standalone lesson and could be read out of order. But we recommend reading the book from beginning to end to help construct your mental model to go from the basics to deep learning.

The book is organized into four parts:

Part I: Thinking Like a Data Head   In this part, you'll learn to think like a Data Head—to think critically and ask the right questions about the data projects your organization takes on; what data is and the right lingo to use; and, how to view the world through a statistical lens.

Part II: Speaking Like a Data Head   Data Heads are active participants in important data conversations. This part will teach you how to “argue” with data and what questions to ask to make sense of the statistics you encounter. You'll be exposed to basic statistics and probability concepts required to understand and challenge the results you see.

Part III: Understanding the Data Scientist's Toolbox   Data Heads understand the fundamental concepts of how statistical and machine learning models work. You'll gain an intuitive understanding of unsupervised learning, regression, classification, text analytics, and deep learning.

Part IV: Ensuring Success   Data Heads understand the common mistakes and traps when working with data. You'll learn about technical pitfalls that cause projects to fail, and you'll learn about the people and personalities involved in data projects. Finally, we provide direction on how to succeed as a Data Head.

ONE LAST THING BEFORE WE BEGIN

We've established that the data field is growing faster than we can articulate the problems and opportunities it creates. We showed that our past (both society's and the authors’) is filled with data failures. And only by understanding that past can we understand the future. We started you down this path by introducing you to several important concepts in the restaurant classification example.

To understand data at a deeper level, you'll need to cut through the noise, think critically about data problems, and communicate effectively with data workers. Armed with this knowledge, we know you'll be well off.

Are you ready? Your journey to become a Data Head begins on the next page.

PART I
Thinking Like a Data Head

Many companies rush to try the “next big thing” in data without ever pausing to ask the right business questions. Or learn basic data terminology. Or learn how to look at the world through a statistical lens.

Data Heads won't have that problem. Part I, “Thinking Like a Data Head,” prepares you for the road ahead and puts you in the right mindset to think about and understand data. Here's what we'll cover:

Chapter 1: What Is the Problem?

Chapter 2: What Is Data?

Chapter 3: Prepare to Think Statistically

CHAPTER 1
What Is the Problem?

“A problem well stated is a problem half solved.”

—Charles Kettering, inventor & engineer

The first step on your journey to become a Data Head is to help your organization work on data problems that matter.

That may sound obvious, but we suspect many of you have looked on as companies talked about how great data is but then went on to overpromise impact, misinterpret results, or invest in data technologies that didn't add business value. It often seems as if data projects are undertaken because companies like the sound of what they are implementing without fully understanding why the project itself is important.

This interaction leads to wasted time and money and can cause backlash against future data projects. Indeed, in a rush to find the hidden value in data many companies expect, they often fail at the first step in the process: defining a business problem.⁸ So, in this chapter, we go back to the start.

In the next sections, we'll look at the helpful questions Data Heads should ask to make sure what you're working on matters. We'll then share an example where not asking these questions leads to a project failure. Finally, we'll discuss some of the hidden costs of not clearly defining a problem right from the start.

QUESTIONS A DATA HEAD SHOULD ASK

In our experience, going back to first principles and asking the fundamental questions required to solve a problem is easier said than done. Every company has a unique culture, and team dynamics don't always lend themselves to openly asking questions, especially ones that might make others feel undermined. And many of those becoming Data Heads find that they don't have the space to even begin asking the important questions that will drive the projects forward. Which is why having a culture in which to ask these questions is as important as the questions themselves.

There's no one-size-fits-all formula for every company and every Data Head. If you are a leader, we ask that you create an open environment that will get the questions going. (This starts with inviting the technical experts into the room.) And ask questions yourself. This exhibits humility, a key leadership trait, and encourages others to join in. If you are more junior, we encourage you to try your best to ask these questions anyway, even if you're concerned it might upset the status quo. Our advice is to simply do your best. From experience, we believe simply asking the right questions always goes a lot further than not.

We want you to be prepared in the right way, trained to spot project warning signs and raise concerns at the outset. With that, we introduce five questions you should ask before attacking a data problem:

1. Why is this problem important?

2. Who does this problem affect?

3. What if we don't have the right data?

4. When is the project over?

5. What if we don't like the results?

Let's explain each in detail.

Why Is This Problem Important?

The first fundamental question is, “Why is this problem important?” It seems simple but it's one that's often overlooked. We get caught up in the hype of how we're going to solve the problem—and what we think it can do—before the project even starts. At the end of this chapter, we'll talk about the true underlying effects of not answering this question. But at a minimum, this question sets the expectations for why a project should be undertaken. This is important as data projects take time and attention—and often additional investments in technology and data. Simply identifying the importance of the problem before starting it will help optimize how company resources are best used.

You can ask the question in different ways:

■ What keeps you (us) up at night?

■ Why does this matter?

■ Is this a new problem, or has it been solved already?

■ What is the size of the prize? (What's the return on investment?)

You'll want to understand how each person sees the problem. This will help you create alignment on how everyone will end up supporting the project to solve the problem—and if they agree it should start.

During these initial discussions, you'll want to keep the focus on the central business problem and pay close attention if you hear rumblings of recent technology trends. Talk of technical trends can quickly derail the meeting away from its business focus. Be on the lookout for two warning signs:

■ Methodology focus: In this trope, companies simply think trying some new analysis method or technology will set them apart. You've heard this marketing fluff before: “If you're not using Artificial Intelligence (AI), you're already behind … .” Or, companies find some other buzzword they would like to incorporate (e.g., “sentiment analysis”).

■ Deliverable focus: Some projects go off track because companies focus too much on what the deliverable will be. They say the project needs to have an interactive dashboard, for example. You start the project, but the outcome becomes about the installation of the new dashboard or business intelligence system. Project teams need to take a step back and trace how what they want to build brings value to the organization.

It may come as a surprise—or a relief—that both warnings involve technology and how it should not be included when defining the problem. To be clear, at some point in the project, methodologies and deliverables enter the picture. To start, however, the problem should be in direct, clear language everyone can understand. Which is why we recommend you scrap the technical terminology and marketing rhetoric. Start with the problem to be solved, not the technology to be used.

Why does this matter? We've noticed project teams have a mix of people who are enamored by data or intimidated by it. Once the problem definition conversation steers toward analysis methods or technology, two things happen. First, anyone intimidated by data might freeze up and stop contributing to the discussion—defining the business problem. Second, those enamored by data quickly splinter the problem into technical subproblems that may or may not align to an actual business objective. Once the business problem morphs into data science subproblems, it may be weeks or months before failure is discovered. No one will want to revisit the main problem once the project work starts.

Fundamentally, teams must answer “Is this a real business problem that is worth solving, or are we doing data science for its own sake?” This is a good, albeit blunt, question to ask, especially now during the hype and confusion around data science and related fields.

Who Does This Problem Affect?

The next question you'll want to ask is, “Who does this problem affect?” The spirit of the problem is not only asking who this affects, but how that person's work will be different going forward.

You should think of all layers of the organization (and perhaps its clients, if any). We don't mean the data scientist who works on the problem or the engineering team who may have to maintain software. The Data Head needs to know who the end users are. Often, it's more than just the people in the room crafting the problem, so it's super important for you to find the people whose daily work will be affected and bring them into the meeting.

We suggest you name names. Whose work will be different if the question gets answered? If it's many people, bring in a small group to represent them. Create a list of these people and understand how they will be affected by the project. You'll want to tie these answers back to the last question.

An exercise to help you think through this is to do a solution trial run. Assume you can answer the question, and then ask your team:

■ Can we use the answer?

■ Whose work will change?

This, of course, assumes you even had the right data to answer the question. (As we'll see in Chapter 4, this can be a huge assumption.) But you should answer these questions and go through several scenarios where the problem has been solved. In many cases, answering these questions can strengthen the project and its impact, or may identify a project with no business benefit.

What If We Don't Have the Right Data?

Every dataset has a limited amount of information inside it, and at a certain point, no technology or analysis method will help you go any further.

In the authors' experience, not asking “What if we don't have the right data?” is where companies make some of the biggest mistakes—mistakes that could be avoided if only they were considered before the project started. Because what happens is this: everyone who has worked so far on the project now wants to take it to completion no matter what. Data Heads enter the project knowing that not having the right data is a possibility. They create contingencies to pivot to collecting better data to answer the question. Or, if the data doesn't exist, they go back to the original question and attempt to redefine the project scope.

When Is the Project Over?

Many of us have been part of projects that went on too long. When expectations aren't clear before the project starts, teams wind up attending meetings out of habit and generating reports no one bothers to read. Asking “When is the project over?” before the project starts can break this trend.

The question strikes at the heart of why the project was initiated and aligns expectations. Important problems are posed because some information or product is needed in the future that does not exist today. Find out what that final deliverable is. Doing this will rekindle conversations about the project's potential return on investment and whether the team has an agreed-upon metric to measure the project's impact.

So, gather project stakeholders and identify reasons the project could end. Some reasons are obvious, like when a project ends from a lack of funding or waning interest. Set those obvious failures aside and focus on what needs to be delivered to answer the business question and conclude the project. For data projects, the final deliverable is typically an insight (e.g., “how effective was the company's last marketing campaign?”) or an application (e.g., a predictive model that forecasts next week's shipping volume). Many projects will require additional work: perhaps ongoing support and maintenance, but this needs to be communicated to the team up front.

Don't assume you know the answer to this question until you've asked it.

What If We Don't Like the Results?

The last question a Data Head should ask prepares the stakeholders for something they'd rather overlook—the possibility their assumptions were wrong. “What if we don't like the results?” imagines you are at the point of no return. You've spent hours on a project only to find out the results show something different. Notice this is different from having data that can't answer the question. Here, the data can answer the question, perhaps quite confidently, but the answer is not what the stakeholders wanted.

It's never easy to get to the end of a project only to find out the results were not what you expected. This all too real scenario happens more often than we'd like to admit. Thinking first about the possibility that the project might reach an unwanted conclusion will ensure you have a plan in motion when you have to deliver the bad news.

Asking this question will also expose differences in how individuals will accept the results of the project. For instance, consider our avatar George from the introduction. George is the type of person who would ignore the results if they don't align to his beliefs, while simultaneously promoting favorable results that do. The question will hopefully uncover his bias early on before the project starts.

You don't want to start a project where you know there's only one accepted result.

UNDERSTANDING WHY DATA PROJECTS FAIL

Projects can fail for a host of reasons: lack of funding, tight timelines, wrong expertise, unreasonable expectations, and the like. Add data and analysis methods into the mix, and the list of possible failures not only grows but becomes obscured behind the analysis. A project team might apply an analysis method they can't explain on data they don't understand to solve a problem that doesn't matter—and still think they've succeeded.

Let's look at a scenario.

Customer Perception

You work for a Fortune 10 company, Company X, that recently received negative media attention for a socially insensitive marketing campaign. You've been assigned to a project to monitor “customer perception.”

The project team consists of the following:

■ The project manager (you)

■ The project sponsor (the person paying for it)

■ Two marketing professionals (who don't have data backgrounds)

■ A young data scientist (fresh out of college and eager to apply the techniques they learned)

At the kick-off meeting, the project sponsor and data scientist quickly and excitedly discuss something called “sentiment analysis.” The project sponsor heard about it at a recent tech conference after a competing company reported using it. The data scientist volunteered they knew sentiment analysis, having implemented it in their senior capstone project. They think it might be a good technique to apply to customer comments on the company's Twitter and Facebook pages. The marketers understand the technique as being able to interpret people's emotional reactions using social media data, but they don't say much.

The basic premise, you are told, is that sentiment analysis can automatically label a tweet or Facebook post as “positive” or “negative.” For instance, the phrase, “Thank you for sponsoring the Olympics.” is positive, whereas “Horrible customer service” is negative. Conceivably, the data scientist could count the daily totals of positives and negatives, plot the trends over time (and in real time!), and subsequently share the results via a dashboard for all to see. Most important: no one needs to read customer comments anymore. The machine will do it for everyone. So, it's decided. The project kicks off.

A month later, the data scientist proudly shows off Company X's Customer Perception Dashboard. It's updated each day to include the latest data and lists some of the week's “positive” comments along the side. Figure 1.1 zooms in on the main graphic in the dashboard: trendlines of sentiment over time. Only positive and negative values are shown, and most comments are neutral.

The project sponsor loves it. A week later, the dashboard is displayed on a monitor in the break room for all to see.

Success.

Six months later, the break room is renovated, and the monitor is removed.

No one notices.

FIGURE 1.1 Sentiment analysis trends

A postmortem of the project revealed no one in the company used the analysis, not even the marketers on the team. When asked why, the marketers admit they weren't really comfortable with the original analysis. Yes, it was possible to label each communication as positive or negative. But the idea that nobody would need to read comments anymore seemed like wishful thinking. They questioned the degree to which the labeling process had even been useful. Further, they countered that perception couldn't only be measured by online interaction even if that was the dataset most readily available to support sentiment analysis.

Discussion

In this scenario, it seemed like everything went well. But the fundamental question—why is the project important?—doesn't appear to have been brought up. Instead, the project team moved forward attempting to answer another question: “Can we build a dashboard to monitor the sentiment of customer feedback on the company's Twitter and Facebook pages?” The answer, of course, was yes, they could. But in the end the project wasn't useful or even important to the organization.

You would think marketers would have had more to say, but they were not identified as people who would have been affected by the project. In addition, this project exhibited two early warning signs in how the team attempted to solve the problem: methodology focus (sentiment analysis) and deliverable focus (dashboard).

Moreover, the project team in the Customer Perception scenario could have taken their problem, “Can we build a dashboard to monitor the sentiment of customer feedback on the company's Twitter and Facebook pages?” and performed a solution trial run. They could have assumed a dashboard was available and updated daily with positive/negative sentiments of social media comments:

■ Can we use the answer? The team would be thinking about the relevance of sentiment analysis on customer perception. How can the team use the information? What is the possible business benefit of knowing the sentiment of customers on social media?

■ Whose work will change? Suppose the team convinces itself that knowing sentiment is important in order to be good stewards of the business. But is someone going to monitor this dashboard? If the trends suddenly go down, do we do anything? What if they trend up?

At this point, the marketing team would have hopefully spoken up. Would they have known what to do differently in their daily work with that kind of information? Likely not. The project, in its current form, hit a wall.

If only they asked the five questions.

WORKING ON PROBLEMS THAT MATTER

So far, we've tied project failures to not defining the underlying problem correctly. Mostly, we've placed this failure in terms of losing money, time, and energy. But there's a broader issue happening all over the data space, and it's something that you wouldn't expect.

Right now, the industry is focused on training as many data workers as possible to meet the demand. That means universities, online programs, and the like are churning out critical thinkers at lightning speed. And if working in data is all about uncovering the truth, then Data Heads want to do just that.

What does it mean, then, when they sit down to a project that doesn't whet their appetite? What does it mean for them to have to work on a poorly defined issue where their skills become bragging rights for executives but don't actually solve meaningful problems?

It means many data workers are dissatisfied at their jobs. Having them work on problems overly focused on technology with ambiguous outcomes leads to frustration and disillusionment. Kaggle.com, where data scientists from all over the world compete in data science competitions and learn new analysis methods, posted a survey and asked data scientists what barriers they face at work.⁹ Several of the barriers, listed here, are directly related to poorly defined problems and improper planning:

■ Lack of clear question to answer (30.4% of respondents experienced this)

■ Results not used by decision makers (24.3%)

■ Lack of domain expert input (19.6%)

■ Expectations of project impact (15.8%)

■ Integrating findings into decisions (13.6%)

This has obvious consequences. Those who aren't satisfied in their roles leave.

CHAPTER SUMMARY

The very premise and structure of this book is to teach you to ask more probing questions. It starts with the most important, and sometimes hardest, question: “What's the problem?”

In this chapter, you learned ways to refine and clarify the central business question and why problems involving data and analysis are particularly challenging. We shared five important questions a Data Head should ask when defining a problem. You also learned about early warning signs to spot when a question starts to go off track. If the question hints of having a (1) methodology focus or a (2) deliverable focus, it's time to hit pause.

When these questions are answered, you are ready to get to work.

CHAPTER 2
What Is Data?

“If we have data, let's look at data. If all we have are opinions, let's go with mine.”

—Jim Barksdale, former Netscape CEO

Many people work with data without having a dialect for it. However, we want to ensure we're all speaking the same language to make the rest of the book easier to follow. So, in this chapter, we'll give you a brief crash course on data and data types. If you've had a basic statistics or analytics course, you'll know the terms that follow but there may be parts of our discussion not covered in your class.

DATA VS. INFORMATION

The terms data and information are often used interchangeably. In this book, however, we make a distinction between the two.

Information is derived knowledge. You can derive knowledge from many activities: measuring a process, thinking about something new, looking at art, and debating a subject. From the sensors on satellites to the neurons firing in our brains, information is continually created. Communicating and capturing that information, however, is not always simple. Some things are easily measurable while others are not. But we endeavor to communicate knowledge for the benefit of others and to store what we've learned. And one way to communicate and store information is by encoding it. When we do this, we create data. As such, data is encoded information.

An Example Dataset

Table 2.1 tells the story of a company. Each month, they run a different marketing campaign online, on television, or in print media (newspapers and magazines). The process they run generates new information each month. The table they've created is an encoding of this information and thus it holds data.

A table of data, like Table 2.1, is called a dataset.

Notice that it has both rows and columns that serve specific functions in how we understand the table. Each row of the table (running horizontally, under the header row) is a measured instance of associated information. In this case, it's a measured instance of information for a marketing campaign. Each column of the table (running vertically) is a list of information we're interested in, organized into a common encoding so that we can compare each instance.

The rows of each table are commonly referred to as observations, records, tuples, or trials. Columns of datasets often go by the names features, fields, attributes, predictors, or variables.

Know Your Audience

Data is studied in many different fields, each with their own lingo, which is why there are many names for the same things. Some data workers, when talking about the columns in a dataset, might prefer “features” while others say “variables” or “predictors.” Part of being a Data Head is being able to navigate conversations within these groups and their preferences.

A data point is the intersection of an observation and a feature. For example, 150 units sold on 2021-02-01 is a data point.

TABLE 2.1 Example Dataset on Advertisement Spending and Revenue

Table 2.1 has a header (a piece of non-numerical data) that helps us understand what each feature means. Note that not every dataset will have a header row. In such cases, the header row is implied, and the person working in the dataset must know what each feature means.

DATA TYPES

There are many ways to encode information, but data workers use a few specific types of encodings that store information and communicate results. The two most common data types are described as numeric or categorical.

Numeric data is mostly made up of numbers but might use additional symbols to identify units. Categorical data is made up of words, symbols, phrases, and (confusingly) sometimes numbers, like ZIP codes. Numeric and categorical data both split into further subcategories.

There are two main types of numeric data:

■ Continuous data can take on any number in a number line. It represents a fundamentally uncountable set of values. Consider the weather. The outside temperature, if collected and turned into data, would represent a continuous variable. A local news station might measure a temperature of 65.62 Fahrenheit. However, they may choose to report this number to you as 65 degrees Fahrenheit, 66 degrees Fahrenheit, or 65.6 Fahrenheit.

■ Count (or discrete) data, unlike continuous data, restricts the precision of the data to a whole number. For example, the number of cars you own can be 0, 1, 2, or more, but not 1.23. This reflects the underlying reality of the thing being measured.¹⁰

Categorical data also has two main types:

■ Ordered (or ordinal) data is categorical data with an inherent order. Surveys, for example, take advantage of ordinal data when they ask you to rate your experience from 1−10. While this looks like count data, it's not possible to say the difference between survey ratings 10 and 9 is the same as the difference between 1 and 0. Of course, ordinal categorial data does not have to be encoded as numbers. Shirt size, for example, is ordinal: small, medium, large, extra-large.

■ Unordered (or nominal) categorical data does not have an underlying order to follow. Table 2.1, for example, has a Location feature with values Print, Online, Television. Other nominal variables include Yes or No responses; or Democrat or Republican party affiliation. Their order as presented is always arbitrary—it's not possible to say one category is “greater than” another.

You'll notice Table 2.1 has a Date feature, which is an additional data type that is sequential and can be used in arithmetic expressions like numeric data.

HOW DATA IS COLLECTED AND STRUCTURED

The preceding section talked about data types within a dataset, but there are larger categories to describe data that refers to how it was collected and how it's structured.

Observational vs. Experimental Data

Data can be described as observational or experimental, depending on how it's collected.

■ Observational data is collected based on what's seen or heard by a person or computer passively observing some process.

■ Experimental data is collected following the scientific method using a prescribed methodology.

Most of the data in your company, and in the world, is observational. Examples of observational data include visits to a website, sales on a given date, and the number of emails you receive each day. Sometimes it's saved for a specific purpose; other times, for no purpose at all. We've also heard the phrase “found data” to reference this type of data; it's often created as byproducts from things like sales transactions, credit card payments, Twitter posts, or Facebook likes. In that sense, it's sitting in a database somewhere, waiting to be discovered and used for something. Sometimes observational data is collected because it's free and easy to collect. But it can be deliberately collected, as with customer surveys or political polls.

Experimental data, on the other hand, is not passively collected. It's collected deliberately and methodically to answer specific questions. For these reasons, experimental data represents the gold standard of data for statisticians and researchers. To collect experimental data, you must randomly assign a treatment to someone or something. Clinical drug trials present a common example that generates experimental data. Patients are randomly split into two groups—a treatment group and a control group—and the treatment group is given the drug while the control group is given a placebo. The random assignment of patients should balance out information not relevant to the study (age, socioeconomic status, weight, etc.) so that two groups are as similar as possible in every way, except for the application of the treatment. This allows researchers to isolate and measure the effect of the treatment, without having to worry about potential confounding features that might influence the outcome of the experiment.¹¹

This setup can span across industries, from drug trials to marketing campaigns. In digital marketing, web designers frequently experiment on us by designing competing layouts or advertisements on web pages. When we shop online, a coin flip happens behind the scenes to determine if you are shown one of two advertisements, call them A and B. After several thousand unknowing guinea pigs visit the site, the web designers see which had led to more “click-throughs.” And because ads A and B were shown randomly, it's possible to determine which ad was better with respect to click-through rates because all other potential confounding features (time of day, type of web surfer, etc.) have been balanced out through randomization. You might hear experiments like this called “A/B tests” or “A/B experiments.”

We will talk more about why this discrepancy matters in Chapter 4, “Argue with the Data.”

Structured vs. Unstructured Data

Data is also said to be structured and unstructured. Structured data is like the data in your spreadsheets or in Table 2.1. It's been presented with a sense of order and structure in the form of rows and columns.

Unstructured data refers to things like text from Amazon reviews, pictures on Facebook, YouTube videos, or audio files. Unstructured data requires clever techniques to convert it into structured data required for analysis methods (see Part III of this book).

Is Data One or Many?

We should clarify where we stand on a debate you may not have known about or cared existed: Is data one or many?

The word data is actually the plural version of the word datum. (Like criteria—the plural of criterion. Or agenda—the plural of the word agendum.) If we were following proper rules of language, we would say “these data are continuous” and not “this data is continuous.”

Both of your authors have attempted to use the correct phrasing the data are…out in the real world and it's not for us. It just sounds weird. And we're not the only ones who think so. The popular data blog FiveThirtyEight.com¹² has argued that its usage is a mass noun, like water or grass.

BASIC SUMMARY STATISTICS

Data does not always look like a dataset or spreadsheet. It's often in the form of summary statistics. Summary statistics enable us to understand information about a set of data.

The three most common summary statistics are mean, median, and mode, and you're probably quite familiar with them. However, we wanted to spend a few minutes discussing these statistics because we frequently see the colloquial terms “normal,” “usual,” “typical,” or “average” used as synonyms for each of the terms. To avoid confusion, let's be clear on what each term means:

■ The mean is the sum of all the numbers you have divided by the count of all the numbers. The effect of this operation is to give you a sense of what each observation in your series contributes to the entire sum if every observation generated the same amount. The mean is also called the average.

■ The median is the midpoint of the entire data range if you sorted it in order.

■ The mode is the most common number in the dataset.

Mean, median, and mode are called measures of location or measures of central tendency. Measures of variation—variance, range, and standard deviation—are measures of spread. The location number tells you where on the number line a typical value falls and spread tells you how spread out the other numbers are from that value.

As a trivial example, the numbers 7, 5, 4, 8, 4, 2, 9, 4, and 100 have mean 15.89, median 5, and mode 4. Notice the mean (average), 15.89, is a number that doesn't appear in the data. This happens a lot: the average number of people in a household in the United States in 2018 was 2.63; basketball star LeBron James scores an average of 27.1 points per game.

It's a common mistake for people to use the average (mean) to represent the midpoint of the data, which is the median. They assume half the numbers must be above average, and half below. This isn't true. In fact, it's common for most of the data to be below (or above) the average. For example, the vast majority of people have greater than the average number of fingers (likely 9.something).

To avoid confusion and misconceptions, we recommend sticking with mean or average, median, and mode for full transparency. Try not to use words like usual, typical, or normal.

CHAPTER SUMMARY

In this chapter, we gave you a common language to speak about your data in the workplace. Specifically, we described:

■ Data, datasets, and multiple names for the rows and columns of a dataset

■ Numerical data (continuous vs. count)

■ Categorical data (original vs. nominal)

■ Experimental vs. observational data

■ Structured vs. unstructured data

■ Measures of central tendency

With the correct terminologies in place, you're ready to start thinking statistically about the data you come across.

CHAPTER 3
Prepare to Think Statistically

“Statistical thinking is a different way of thinking that is part detective, skeptical, and involves alternate takes on a problem.”¹³

—Frank Harrell, statistician and professor

This chapter is about how you think about data—about arming yourself with a mindset to consume and think critically about data you come across in your business or everyday life. It's laying important groundwork for the rest of the book, yes, but if any of these concepts are new to you, you'll likely find yourself watching the news or reading the latest popular press science articles through a new lens—a statistical lens.

Two important callouts before we start.

First, we're just scratching the surface here. This one chapter isn't going to replace a semester of Statistics (with apologies to any students) or dive into all the psychological aspects of “thinking” like the modern classic Thinking, Fast and Slow.¹⁴ But we will introduce several concepts and establish a foundation for statistical thinking, incomplete as it may be.

Second, there is a risk in reading these next few chapters that you will become cynical about data. You might throw your arms up, claiming this statistics nonsense obfuscates the truth under complicated equations and numbers, and swear off any analysis you see. Or maybe you'll throw tomatoes at every article you read because you learned a few tricks of the statistical trade and doubt they know as much as you.

Please don't. Our goal isn't to make you reject the information you see, but to question it, understand it, know its limitations, and perhaps even appreciate it.

ASK QUESTIONS

A core tenet of statistical thinking is “ask questions.”

Many of us do this to some degree in our everyday life. We assume you, as a reader of a book about data, naturally question sure-bet claims from advertisers (“Lose 10 pounds in a month!” or “This hot stock is the next Amazon!”) and bizarre posts on social media. (Open Facebook or Twitter, and you'll probably spot one right now.) So, the muscle is inside of you somewhere. Frankly, it can be fun to sit back as an observer and tear apart the obvious lies.

But it's much harder to do to claims and data personal to us. Every political election shows this. Take a minute to think seriously and honestly about how quickly you become suspicious of claims or numbers from the other political party.¹⁵ What goes through your head? “Their sources are bad. My sources are good. Their information is false. My information is true. They just don't understand what's going on.”

Clearly, this discussion can get philosophical very quickly. Our intent isn't to stir up a political debate or dive into the many factors that shape our personal and political ideologies. But there's a lesson to be learned here—it's hard to question everything when the “everything” includes your own thinking and reasoning.

More pertinent to the mission of this book, think about the information you see in the workplace. When you see data sprawled out across spreadsheets and PowerPoint presentations—information that impacts your company's success, your job performance, your possible bonus—is it viewed with any skepticism? In our experience, it's often not. Numbers in a boardroom are viewed as cold hard facts. Truth in black ink, rounded to the nearest decimal.

Why is this? Probably because you don't have time to question, poke and prod, or collect more data. This is the data you have, the data you must act on, and the data you can point to and blame if things don't work out your way. When faced with these constraints and limitations, skepticism turns off, almost reflexively. Another reason, you might suggest, is even if you understand issues with the data, your boss probably doesn't. A chain reaction occurs where everyone assumes that someone else, up the management chain (or even down below), will take the number at face value, and that assumption permeates down to those of us staring at the spreadsheet. They'll assume it's true, so we will act on it as if it is.

Data Heads can push back. And it starts with understanding variation.

Comment on “Statistical Thinking”

We're using “statistical thinking” in a general sense, defined in the quote at the beginning of the chapter. You might prefer probabilistic thinking, statistical literacy, or mathematical thinking. No matter which phrase you prefer, all deal with the evaluation of data or evidence.

Some might wonder why this line of thinking is important. Businesses and life in general have gone on without it. So why now? Why should Data Heads care?

In an article titled, “Data Science: What the Educated Citizen Needs to Know,” Harvard economist and physician Alan Garber explains why:¹⁶

The benefits of data science are real and have never been more salient or important. Increasingly accurate predictions will make the products of data science more valuable than ever, and will increase interest in the field. The advances can also breed complacency and blind us to flaws. Workers of the future need to recognize not only what data science does to assist them in their work, but also where and when it falls short… . a deeper understanding of probabilistic reasoning and the evaluation of evidence is a general skill that will serve all of them well.

THERE IS VARIATION IN ALL THINGS

Observations vary. This isn't earth-shattering news.

The stock market fluctuates daily, political poll numbers change depending on the week (and the polling firm generating the data), gas prices move up and down, and your blood pressure spikes when the doctor is present but not the nurse. Even your daily commute to work, if you broke it down and measured it down to the second, would be slightly different each day depending on traffic, weather, having to drop kids off at school, or stopping for a coffee. There is variation in all things. How comfortable are you with this?

You've probably accepted—or at least tolerated—this variation into your everyday life and may, in fact, be comfortable with it. (Well, maybe not the stock market swings.) Overall, though, we understand that some things change for reasons we can't always explain. And when it comes to things like filling our tires, pumping gas, or paying the electric bill, we live with these figures being different every time they're measured, so long as they make some intuitive sense. But as we established in the previous section, it's harder to put data personal to our careers or our business under the same microscope.

A business's sales fluctuate daily, weekly, monthly, and yearly. Customer satisfaction survey results can vary wildly one day to the next. If we accept the reality of variation in our lives, we don't need to explain every peak and valley. And yet, this is what businesses will attempt to do. What was done differently the week of high sales? leadership asks. Let's repeat the good, reduce the bad, they'll say. Variation makes people feel helpless about the very things they're paid to know and influence.

When it comes to business, maybe we're not as comfortable with variation as we'd like to imagine.

In fact, there are two types of variation. One type of variation stems from how data is collected or measured. This is called measurement variation. The second is the randomness underlying the process itself. This is called random variation. The difference might seem trivial at first, but this is where statistical thinking becomes important. Are decisions being made in response to random variation that can't be controlled? Or is the variation reflective of some true underlying process that, when surfaced correctly, can be controlled? We all hope for the latter.

Put simply, variation creates uncertainty.

Let's look at one hypothetical scenario and one historical case study of variation causing uncertainty.

Scenario: Customer Perception (The Sequel)¹⁷

You're the manager of a retailer, and the corporate office closely monitors customer satisfaction data from your store, collected from customers when they call into the 1-800-number at the bottom of a receipt. The survey asks customers to rate their satisfaction on a scale from 1−10, 10 being “completely satisfied.” (A bunch of other questions are included, but it's only the first question that matters.)

To add to the ploy, corporate only wants 9s and 10s. 8s are as good as 0s. Numbers are aggregated weekly and sent to you, as the store manager, and the corporate office in a PDF file with colorful graphs whose page count is just a little too long for the amount required to deliver this information. And yet, these numbers influence your bonus, and your boss's bonus, and are nervously and obsessively pored over each week as you try to hit your 85% weekly success rate, calculated as the number of 9s and 10s divided by the total number of surveys.

We'll pause here to talk about one source of variation—how the survey measures results. Rating anything on a 1−10 scale is notoriously problematic. One person's 10 (“They didn't have what I was looking for, but an employee helped me find a substitute!”) is another person's 5 (“They didn't have what I was looking for! An employee had to help me find a substitute.”). We'll ignore other potential sources of variation like the presence of a rude employee, an overcrowded store, an economic downturn that has everyone on the edge, whether the customer is shopping with children, plus … countless others.

We're not saying that the survey itself ought to be disbanded. Rather, we want to show that the design of (that is, the way we measure) data introduces variation that we often overlook. Ignoring variation means thinking deviations from our expectations reflect low-quality service rather than expected differences inherent in the question itself. And yet businesses will attempt to chase elusive high target numbers (9s and 10s in this case) without understanding that their choices in how they measured the data was the very cause of the underlying variation.

Here's how this could play out. Suppose 50 people leave a review each day, every day, for 52 weeks. This makes 350 surveys a week and 18,200 for the year. With participation like that, you seem to have a good representation of customer perception. Then, at the end of each week, results are tallied—corporate adds up the number of 9s and 10s and divides it by the weekly total, 350—and reports the results on a graph, shown in Figure 3.1. Numbers above the 85% mark get you a pat on the back. Results below 85%, and you're sweating.

Every Monday you get the report and have a phone call with corporate about the results. Imagine the stress of these conversations in weeks 5–9. You're just below the threshold. And when you finally break above in week 10, no doubt caused by the motivation of your boss, week 11 comes along and gives you a new low. This goes on and on.

But what you're looking at in Figure 3.1 is pure randomness. We generated 18,200 random numbers that were either an 8, 9, or 10—representing how different customers vote on positive customer service—and shuffled them like a deck of cards.¹⁸ Each “week” we took 350 numbers and calculated the metric. The average percentage of 9s and 10s in the dataset was 85.3% (very close to the true value of 85%), meeting the corporate standard, but each week, simply due to random variation, bounced around that threshold.

FIGURE 3.1 Weekly Customer Survey Results: Percent of Positive Reviews. The horizontal line at 85% represents the target.

Not thinking statistically led to everyone—you, your boss, and the corporate office—chasing improvements in service to drive an arbitrary number up, even as this number was not influenced by such activities.

We term this type of activity the illusion of quantification. It's the pursuit of driving metrics without a clear statistical foundation around what they mean.

Do you see the illusion of quantification in your workplace?

Case Study: Kidney-Cancer Rates

The highest kidney-cancer rates in the United States, measured by the number of cases per 100,000 of population, occur in very rural counties sprawled out across the Midwest, South, and West regions of the country.

Pause to think about why this might be.

Perhaps you suspect, being in rural areas in the interior of the country, the residents have less access to adequate healthcare. Or maybe it's the result of unhealthy living caused by meat-heavy, salt-laden high-fat diets—or too much beer and spirits. It's easy, natural really, to start building narratives around facts. You can already picture researchers starting to devise remediation measures to alleviate the problem.

But here's another fact: the lowest kidney-cancer rates in the United States also occur in very rural counties sprawled out across the Midwest, South, and West—often neighboring the borders of the highest-rate counties.¹⁹

How can both be true? How can two cities with similar demographics have so very different results? Every reason you might think of to explain why very rural counties have high rates of kidney cancer would surely apply (to some degree) to their neighboring counties. So, something else must be going on.

Let's take two neighboring counties in rural Midwest, County A and County B, and assume they each have only 1000 residents. If County A has zero cases, its rate would be 0, obviously in the lowest rate category. But if County B has a single case of kidney cancer, its rate would be 100 cases per 100,000 population, giving it the highest rate in the country. It's the low population in the counties causing the high variation, simultaneously producing the highest and lowest rates. In contrast, one additional case in New York County (Manhattan, New York City), with a population over 1.5 million residents, would barely move the needle. Going from 75 cases to 76 cases would change the number of cases per 100,000 from 5 to 5.07.

In fact, this variation was real and measured in an American Scientist article titled, “The Most Dangerous Equation.”²⁰ Figure 3.2 summarizes the results for U.S. counties. The sparsely populated counties, on the left side of the plot, show much higher variation in cancer rates, from 0 up to 20, the highest in the country. As population increases, moving left to right in the plot, the variation starts to reduce, giving a triangular shape. There's much less variation on the right side of the figure, indicating densely populated counties are more robust to additional cases and stabilize around 5 cases per 100,000 of population.

The article shared other examples where small numbers cause high variation. For instance, would you be surprised to learn that small schools have both the best and the worst test scores? One or two students not passing a test can cause a huge swing in overall percentages. Small numbers can lead to extreme results.

FIGURE 3.2 Reprint of American Scientist figure

PROBABILITIES AND STATISTICS

In the last few sections, we explained variation and talked about how it's a source of uncertainty for many businesses. Uncertainty, in fact, can be managed and this is where probability and statistics enter the picture.

We often use the terms probability and statistics interchangeably, if not together, when describing the mathematics of outcomes. But here we can go a little deeper to truly understand the difference.

Imagine a big bag of marbles. Inside, you don't know what color they are. You don't know their shape or size. You really don't even know how many marbles are in the bag, but you reach in the bag and blindly grab a handful.

Let's stop for a moment. You have a bag of marbles you haven't peeked into and a fistful of glass rolling between your fingers you haven't looked at. You really have no information about what's in your hand or in the bag.

Now here's the difference. In probability, you find out exactly what's in the bag, and use the information to guess what's in your hand. In statistics, you open your hand and use the information to tell us what's in the bag.

Probabilities drill-down; statistics drill-up. Makes sense?

Let's look at two real-life examples:

■ Las Vegas casinos are built on probability. Every time you play a casino game you are pulling from their bag of marbles, made up of wins and losses. There are just enough winning marbles within the casino bag to keep you interested in playing. Casinos understand variation—indeed they've commercialized it through payouts and losses optimized to keep you interested and exhilarated. Over the long term, however, casinos know they will make money because they created the bag from which all marbles are pulled, and they know exactly what's inside. With every bet made, chip laid at the table, and lever pulled on the side of a slot machine, casinos know the underlying probability of your success. If you think about how much data casinos have, you can see that they both live in a world of variation but also have a clear sense of probable outcomes.

■ Political polling is based on statistics. In a casino, the bag of marbles is meticulously designed and is constantly sampled from.

In an election, however, politicians don't know what's really inside the entire bag until election day when all the marbles (i.e., votes) are revealed.²¹ Politicians only get this one chance to learn what's in the bag—and whether it contains enough winning marbles for them. Before the election, politicians and political parties only have access to a small set of random marbles (called surveys), and they pay a lot of money for that access. Using that sample, they infer the patterns inside the bag and adjust their campaigns accordingly. Because their information is incomplete (and because they often introduce bias and error), they don't always get it right. But when they do, it's the difference between winning an election and not.

Let's take a quick look at some important concepts in probability and statistics in the following sections.

Probability vs. Intuition

Earlier in this chapter, we said that random variation cannot be controlled. But it can be measured, and probability gives us the tools to do it.

Sometimes probabilities make complete sense to us. If you've rolled a fair die or spun a dreidel, you recognize you have a known chance of landing on a specific number (1 in 6) or letter (1 in 4). Simple games of chance make a lot of sense to us. Simple probabilities feel intuitive. Indeed, they make so much sense to us, that they often obscure underlying complexity. Commercials, for instance, play on appeal to simple probabilities by reducing them to something that feels like we intuitively understand it.

TABLE 3.1 Probability Dentists Agree to an Advertising Claim

You've likely seen this commercial before: “4 out of 5 dentists agree” to an advertising claim, X (X can be whatever you want—chewing gum reduces cavities, or baking soda whitens teeth—it doesn't matter).

Now, suppose five dentists are sitting in front of you. Knowing that 80% of all dentists agree that X, how likely is it that exactly four out of the five dentists in front of you agree?²²

100%? 90%? or 80%?

The actual answer is 41%.

This seems too low intuitively, but it is correct. Let's look at why. Table 3.1 shows one way a sample of five dentists could agree to X.

Or, for brevity,

But there are five different combinations of agreement, where each dentist could the one “No,” shown in Table 3.2

Thus, multiply the original probability by five: , or 41% for short.

Four out of five dentists may agree, on average, but that's no guarantee that in every sample of five dentists that four will agree to claim X. Going back to our marble analogy, if the bag of marbles contained 80% marbles with yeses and 20% noes, some handfuls of five will have all five yeses. And in the rare case, all five noes. (That's variation for you.)

We share this exercise to highlight, once again, how people underestimate variation, especially when dealing with small numbers. What people expect to see, based on intuition, rarely matches reality when we calculate the probabilities. And underestimating variation causes people to overestimate their confidence in small data. This has been coined the “law of small numbers.” It's “the lingering belief … small samples are highly representative of the populations from which they are drawn.”²³

TABLE 3.2 Possible Combinations of 4 out of 5 Dentists Agreeing

Thinking statistically, like a Data Head should, means being mindful of our intuition, realizing it can play tricks on us. (We'll explore several more of these examples and misconceptions in the coming chapters.)

Discovery with Statistics

Statistics is often broken out into descriptive statistics and inferential statistics. You are probably familiar with descriptive statistics even if you don't use the phrase. Descriptive statistics are the numbers that summarize data—the numbers you read in the newspaper or see on the projection screen at work. Average sales last quarter, year-over-year increases, unemployment rates, etc. Measures like mean, median, range, variance, and standard deviation are descriptive statistics and require specific formulas to calculate. Your old Statistics book is full of them.

Descriptive statistics are a deliberate oversimplification of data—a way to condense an entire spreadsheet of company sales data into a few key measures that summarize the main information. Going back to the marble analogy, descriptive statistics is simply counting and summarizing the marbles in your hand.

While useful, we're rarely content to stop here. We want to go the extra step and understand how we can take the information in our hand and make a principled guess to infer the general contents of the entire bag. This is inferential statistics. It's the process of “going from the world to the data, and then from the data back to the world.”²⁴ (We will go deeper into this in Chapter 7.)

For now, let's consider an example. Imagine how you’d react seeing the headline, “75% of Americans Believe UFOs Exist!” after learning it was sampled from 20 tourists at the International UFO Museum and Research Center in Roswell, New Mexico. Do you think you can accurately infer the true underlying percentage of Americans who believe in UFOs based off what you now know about the study?

A Data Head's skepticism meter would go off immediately. The statistic, 75%, is not trustworthy based on the

■ Biased sample. People visiting Roswell would be much more likely to believe in UFOs than the general public.

■ Small sample size. You've learned how much variation small sample sizes introduce. Inferring what millions think based on 20 people doesn't make much sense.

■ Underlying assumptions. The headline specified “Americans” as believing in UFOs simply because the test was taken in America. But the museum, as you might recall, is an international attraction. You don't know that everyone who participated in the survey is an American.

Concepts like bias and sample size are tools of statistical inference that help us understand if the statistics we see or calculate are nonsense. And they're an important part of your toolkit. The underlying assumptions are equally as important to consider, as well. Thinking like a Data Head requires that you don't take assumptions said in a conclusion at face value.

So, as you see data in your work, don't blindly acquiesce to the information you see or even the intuition you feel.

Think statistically. Ask questions. That's what Data Heads do. The upcoming chapters will tell you the questions to ask to help you think statistically.

Statistical Thinking Resources

Earlier in this chapter, we made clear that we can only scratch the surface on statistical thinking. Fortunately, several great books go into more depth about the topic. Our favorites are:

■ Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, by Joel Best (University of California Press, 2001)

■ How Not to Be Wrong: The Power of Mathematical Thinking, by Jordan Ellenberg (Penguin Books, 2015)

■ How to Lie with Statistics, by Darrell Huff (W. W. Norton & Company, 1993)

■ Naked Statistics: Stripping the Dread from the Data, by Charles Wheelan (W. W. Norton & Company, 2013)

■ Proofiness: How You're Being Fooled by the Numbers, by Charles Seife (Penguin Books, 1994)

■ The Drunkard's Walk: How Randomness Rules Our Lives, by Leonard Mlodinow (Pantheon, 2008)

■ The Signal and the Noise: Why So Many Predictions Fail—But Some Don't, by Nate Silver (Penguin Press, 2012)

■ Thinking, Fast and Slow, by Daniel Kahneman (Farrar, Strauss and Giroux, 2013)

CHAPTER SUMMARY

In this chapter, we laid a foundation of statistical thinking from which we'll continue to build upon throughout the book.

Specifically, we described the importance of variation and understanding how it exists within the context of the things we measure. We showed that surveys can have wide variation when they solicit customer opinion. Not because the service was bad (though, it may have been) but because the question itself predisposes wildly different responses that might be characterized as similar until measured.

We also talked about probability and statistics. They help us manage variation by demonstrating that some of it is predictable and that some of it won't even matter over the long term.

Probability drills-down: it uses a large universe of information to tell us what we'll find if we grab random scoops from it. Statistics drills-up: it tells us about the larger universe of information by using the small bits we have access to. Both probability and statistics are tools to help us learn more about when a complete picture of what we want to know remains obscure. Finally, we discussed how you can use your knowledge of probability and statistics to hone your skepticism.

PART II
Speaking Like a Data Head

Part II, “Speaking Like a Data Head,” continues the previous part’s charge to think statistically and question everything. In fact, Part II gives you questions to ask and things to think about whether you're viewing someone else's data project or doing the work yourself. Many of the forthcoming section headers will be named for the very same questions you'll need to ask. Consider it your reference guide for asking tough questions. Here's what we'll cover:

Chapter 4: Argue with the Data

Chapter 5: Explore the Data

Chapter 6: Examine the Probabilities

Chapter 7: Challenge the Statistics

Equipped with these chapters, you'll be able to ask intelligent questions about the data and analytics you encounter in the workplace.

CHAPTER 4
Argue with the Data

“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.”

—John Tukey, famous statistician

As you become a Data Head, your job is to demonstrate leadership in asking questions about the data used in a project.

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Splunk Inc., “The State of Dark Data,“ 2019, www.splunk.com/en_us/form/thestate-of-dark-data.html .

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2

Venture Beat. “87% of data science projects failing”: venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production

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3

www.brookings.edu/wp-content/uploads/2016/06/11_origins_crisis_baily_litan.pdf

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4

Nate Silver wrote a series of articles describing this in great detail ( fivethirtyeight.com/tag/the-real-story-of-2016 ). Pollsters wrongly assuming independence, just like in the mortgage crisis, was one mistake.

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5

Note to our fellow statisticians: We just mean regular confidence, not statistical confidence.

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6

K-nearest-neighbor can also be used to predict numbers instead of classes. These are called regression problems, and we'll cover them later in the book.

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7

This idea is discussed in an amazingly helpful book: Wilson, G. (2019). Teaching tech together. CRC Press.

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8

A robust data strategy can help companies mitigate these issues. Of course, an important component of any data strategy is to solve meaningful problems, and that's our focus in this chapter. If you'd like to learn more about high-level data strategy, see Jagare, U. (2019). Data science strategy for dummies. John Wiley & Sons.

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9

2017 Kaggle Machine Learning & Data Science Survey. Data is available at www.kaggle.com/kaggle/kaggle-survey-2017. Accessed on January 12, 2021.

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10

There are additional levels of continuous data, called ratio and interval. Feel free to look them up, but we rarely see the terms used in a business setting. And there are situations when the distinction between continuous and count data doesn't really matter. High count numbers, like website visits, are often considered continuous for the purpose of data analysis rather than count. It's when the count data is near zero that the distinction really matters. We'll explore this more in the coming chapters.

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11

Here's a quick example of confounding. In a drug trial, if the treatment group consists of only children and no one got sick, you'd be left wondering if their protection from the disease was caused by an effective drug treatment or because children had some inherent protection from the disease. The effect of the drug would be confounded with age. Random assignment between the control and treatment groups prevents this.

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12

“Data Is” vs. “Data Are”: fivethirtyeight.com/features/data-is-vs-data-are

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13

F. Harrell, Professor and founding Chair of the Department of Biostatistics, Vanderbilt University: www.fharrell.com/post/introduction

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14

Thinking, Fast and Slow, by Daniel Kahneman (Farrar, Strauss and Giroux, 2013).

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15

The United States has a two-party system.

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16

Link to article in the Harvard Data Science Review: hdsr.mitpress.mit.edu/pub/pjl0jtkp.

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17

If it seems like we're focused on customer perception too much, it's because it is (1) hard to measure accurately, (2) highly influenced by a biased subset of people, and (3) over-scrutinized by management.

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18

In our simulation, the chance of an 8 rating was 15%, the chance of a 9 was 40%, and the chance of a 10 was 45%. Therefore, since we generated this data, we know the true value of the satisfaction metric, receiving a 9 or 10, is exactly 85%.

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19

Suppose we flip-flopped the story and told you the rural areas had the lowest kidneycancer rates to start the case study. What reasons would you have listed? Give it a shot. You'll see just how easy it is to craft a story around data.

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20

Wainer, H. (2007). The most dangerous equation. American Scientist, 95(3), 249.

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21

We're oversimplifying. In an election, political parties are trying to influence the makeup of the bag, both in number of marbles and their color. But even in that case, they still don't know everything inside and rely on sampling.

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22

Example from www.johndcook.com/blog/2008/01/25/example-of-the-law-of-smallnumbers

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23

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.

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24

O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc.

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