Dataism: The Revolution Transforming Decision-Making, Consumer Behavior, And Almost Everything Else, by Steve Lohr
As a data-oriented person who works as a reporting analyst and is interested in the use of data-driven decision making process that can help overcome biased thinking, this is the sort of book I enjoy reading . In many ways, this is an immensely enjoyable book, written with skill, by someone who knows data well and also is able to capture the story of the people who are helping to make big data possible, people who in profound and sometimes alarming ways reminded me of myself. Yet at the heart of this book is a deep tension between a lot of factors, in that the technologies we seek for freedom and convenience often end up reducing our privacy and leaving us vulnerable to greater surveillance and control. This book speaks a lot about the promise of data analysis in leading to smarter decisions in areas like healthcare and self-driving cars and making buildings more energy efficient, all of which is good, but it also talks about the creepy and uncomfortable side of data analysis in terms of defining us in ever smaller boxes which determine our choices and constrain our freedom every more tightly based upon what we are assumed to be and do.
In terms of its organization and structure, this book is divided into reasonably short chapters, each of which contains various corporate and individual stories that serve as case studies for some aspect of data analysis, whether it is the biographical history of important people in the creation of data techniques and infrastructure, or whether it is the progress that has been made, and the visions and ambitions for the future, in the use of data to help resolve some of our thornier problems by having self-learning computers with ever-greater capabilities. At its core, this book is trying to prompt people to think about the tradeoffs and the repercussions of our decisions, and the fact that our problems are not often simple and straightforward but are networked. In the main, despite its warnings, this book is relatively optimistic, talking about how human beings and their thought processes are at the core of every aspect of technology, something that people tend to forget, if they ever knew it. And so in looking at ever more increasing levels of mechanized life, this book still looks over and over again at the sort of people who are responsible for these shifts in the collection, storage, and use of data in ever increasing amounts and detail.
And yet looking at these people can often be troubling as well. The people discussed in this book share many characteristics–they are relentlessly curious about many areas of life yet also are deeply technical and quantitative in their approach, even as they are skilled at verbal communication as well, seeking to bridge the gulf between the arts and sciences. They are hard to put in a box, but skilled at putting others into boxes. Their work extols balance and harmony and rationality, yet they struggle with the deep burden of mental illness, including crippling anxiety, sometimes to dangerous degrees. They seek meaningful work and a decent living, but often come from backgrounds of privation and poverty, and many of them have a certain ambivalence or even hostility to authority, yet their endless thirst for knowledge and for solving problems through data makes them powerful people in their own right. For all of the talk about the democratization of data, reading this book reminds one that it is far more likely that big data will enslave than it will set free, even if it may not enslave in a straightforward fashion. All the more ominous, it is likely to do so in a way that is not transparent, that is difficult to trace, and that is difficult to fight because the humans behind the trouble are themselves hard to find, because technology serves as the interface, obscuring the human factors that are most important. Let us hope that my fellow quantitative folk are able to avoid the pitfalls of arrogance and authoritarianism in the search for greater efficiency, for it is by no means certain that our good intentions in increasing knowledge will end up in noble and enlightened institutional behavior. The past does not give us much hope that things will be different this time, after all.
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