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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