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
NOTE
1 1 Splunk Inc., “The State of Dark Data,“ 2019, www.splunk.com/en_us/form/thestate-of-dark-data.html .
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.1
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