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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

NOTE

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