Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else – A New York Times Reporter's Journey Through Big Data

by Steve Lohr

Steve Lohr, a technology reporter for the New York Times , chronicles the rise of Big Data, addressing cutting-edge business strategies and examining the dark side of a data-driven world. Coal, iron ore, and oil were the key productive assets that fueled the Industrial Revolution. Today, Data is the vital raw material of the information economy. The explosive abundance of this digital asset, more than doubling every two years, is creating a new world of opportunity and challenge. Data-ism is about this next phase, in which vast, Internet-scale data sets are used for discovery and prediction in virtually every field. It is a journey across this emerging world with people, illuminating narrative examples, and insights. It shows that, if exploited, this new revolution will change the way decisions are made—relying more on data and analysis, and less on intuition and experience—and transform the nature of leadership and management. Lohr explains how individuals and institutions will need to exploit, protect, and manage their data to stay competitive in the coming years. Filled with rich examples and anecdotes of the various ways in which the rise of Big Data is affecting everyday life it raises provocative questions about policy and practice that have wide implications for all of our lives.

Genres: Nonfiction, Business, Science, Technology, Artificial Intelligence, Audiobook, Economics

256 pages, Hardcover

First published January 6, 2015

Reviews

[Rion · Rating 3 out of 5]

Some of the who and what data science is about. More pros than cons. The pros are elaborated on while cons are reduced to basic quotes. No real research on the social consequences of a future that regards efficiency more important than humanity. Social Darwinism at it's worst and this book chooses not to even attempt to rationalize a future in which the only jobs left for humans are ones maintaining machines. And when machines can maintain and innovate themselves? Scary book. I understand better now why Musk is calling for his peers in Silicon Valley to slow down the artificial development projects. Data scientists should take a few philosophy and political science courses instead of just pushing the idea, "what does the data say first". All good scientists know that data is only as good as it's source and how it fits into the bigger picture. This should include human society and the very real socially genocidal consequences that will occur when the socially elite have even more tools to exploit capitalism to it's worst manifestation. Who will big data benefit at this point in humanities current economic and political evolutionary stage? I think it's obvious and this book does if nothing else, prove that we are not ready for how it's already being used. As I write this I'm painfully aware that someone other than my friends and intended audience are reading and arrogantly believing they have the right to all data. I clearly see now that the Internet has effectively been perverted. Knew it would and was happening. Wasn't completely aware of the extent. This book at least shines some light on the next greatest threat since the discovery of how to split an atom. Create a human honey pot and exploit their psychological dispositions to gain advantage, click here to gain access, don't worry about the complicated lawyer speech in our privacy statements that give all your rights to privacy away. Old game, new strategy. Let the so called willing enslavement begin. What could we expect from a largely lawless medium that allows predatory behavior to be the law of the land once again. Without knowledgeable societal restrictions Nietzsche's application of social Darwinian and realist strongman philosophy will maintain dominant and a danger to everyone not with the technical skills to protect themselves.

[Don LaFountaine · Rating 3 out of 5]

While this was not quite what I expected before I started to read this book, I did like it. The thing is that this is not a book that an average reader will enjoy if they pick it up. It is written and geared towards people that have an interest in math, business applications, technology, and how these categories are being used in the 21st century.

There is a solid historic background that the author provides the reader about how business practices have changed over time. Though not written in a linear fashion, (past...present...future...)he gets the historical prospective across. Along with that, the reader learns how people are using math, physics, computer science, and other number crunching and analyzing tools to help make decisions that affect employees and people worldwide. Many people have heard about Google doing this, as well as Facebook. What is also discussed is how this ay well be used in other fields such as medicine. The author states that the Doctor will no longer be the person in charge of medical care; that algorithms, as set up by data scientists, will be available to provide a doctor with a diagnosis of a patient's condition.

The book discusses a number of people who are helping to make this happen. All elite college educated people, they have become wealthy working for various companies throughout their lives. Though they generally last only a couple of years until they are bored and move onto another place, they are the ones designing programs, codes, and equations to make computers and machines work faster, better, and finally more intelligently than ever before.

Though exciting possibilities abound with using Big Data as a means to make decisions and make things better for others, the author glosses over any trouble that may be caused by relying so much on computers and data. He mentions in passing that human bias is in the programming, but does not really go into how dangerous that is, except to say that it is possible some people may be negatively affected by it. The back of the book says he examines the dark side of a data-driven world, but I found that there is less than 20 pages examining the "dark side".

The book is definitely biased towards how positive Big Data will be on everyone's lives, and in my opinion, downplays the potential dangers and side effects. With that said, this is a good book for managers, people that are interested in how data can be used to improve and change practices, and those that are interested in how American society is changing.

[Shubh Parekh · Rating 3 out of 5]

I listened to this book while working/ at the gym.

I enjoyed listening to the rise of Data Science as a job and as an educational path. It provides an in depth analysis on the depth of data and how we can utilize it successfully. It also shows the caveats of making poor inferences that may not be able to accurately predict real outcomes.

I more so enjoyed this because of my current role and made me think about how I can utilize my skills to do my job better.

[Yogi Saputro · Rating 2 out of 5]

Buku ini berisi "what's what" dan "who's who" tentang tren big data beberapa tahun belakangan. Kebanyakan isinya kisah orang-orang di Silicon Valley, perusahaan top macam Facebook dan IBM.

Gaya narasinya seperti dokumenter. Kalau punya versi digitalnya, lebih baik gunakan fitur text-to-speech. Itu yang saya lakukan untuk selesaikan buku ini dalam 2 hari weekend. Rasanya kaya dengar pengajian atau podcast saja. Disambi lakukan tugas rumah tangga. Terlewat beberapa bagian pun nggak masalah. Bahasannya mengalir dalam beberapa topik.

Secara substansi, ini yang bikin saya kasih bintang 3. Saya tidak mendapat banyak value dari buku ini karena saya spesialis bidang IT. Kalau anda profesional non IT yang tertarik dengan sejarah dan tren big data untuk kemajuan umat manusia, buku ini cocok.

[Elango Rathinavelu · Rating 4 out of 5]

I picked this up expecting a dry read on Data and was rewarded with an engaging tour of data in business and a view of the actors shaping this change. Part novel like, part business history, very little technology.
Written in an engaging, simple style. Recommended reading will introduce readers to the world of data and its role in our present and future.
One mirror irritation is the author’s personality driven narrative of the data world. Not a deal breaker.

[Jeff Gough · Rating 3 out of 5]

Basically a survey in Big Data trends, and the companies and personalities pursuing them. This is an easy read and sometimes seems to float all over the place.

[Nixie · Rating 1 out of 5]

Disappointing. Plenty of pages in the beginning of the book are dedicated to fanboying about Jeff Hammerbacher, and it comes back with a vengeance near the end, still not bringing much information about any actual topic. I don't care what his parents' names are. I don't care what he was wearing when the author interviewed him. I also happen to not care about itty-bitty details of some marketing campaign IBM couldn't figure out ages ago (IBM gets the spotlight after Jeff).

In contrast, social implications (from privacy to jobs) are barely addressed, tucked into the last chapter almost as an afterthought, offering vague hand-wavy "eh" and not much else.

There are a few examples of using big data in areas like wine-making and medicine, but they're buried in the raw data of who had which haircut and platitudes about greatness of technological change.

[Gregory Thompson · Rating 3 out of 5]

Doesn't Break any New Ground

This book is a fairly serious examination of big data and its impact on our daily lives. Mohr contends that not only is data getting bigger (or more voluminous), but business thinking and decisions are increasingly being framed in a data-centric manner - hence the term "data-ism". This feels as if the author is trying a little too hard to create a new business buzzword, but I have no quibble with the overarching theme.

The book is readable but does not really break any new ground - given the rich subject matter. Finding the right balance between readable, relevant anecdotes and technical substance can be challenging especially if the book is aimed at non-technical readers. I feel that the author may have underestimated the ability of business decision-makers to understand (and be keen to learn) more technical elements of data science.

[Nelle · Rating 2 out of 5]

Dit boek is erg anekdotisch geschreven. Er zijn veel uitweidingen naar nutteloze achtergrondverhaaltjes die weinig bijdragen aan het 'big data'-verhaal. Korte samenvatting: 'Heel wat grote bedrijven werkten vroeger inefficiënt en verzamelden heel veel data waar ze niets mee deden. Toen ontwikkelden ze een datastrategie en ging alles beter.' Ik had gehoopt op wat meer diepgang en inspiratie die ik ook in mijn eigen job kan gebruiken. Maar misschien ben ik hier met de verkeerde verwachtingen in beginnen lezen, teveel vanuit een pragmatisch oogpunt, en is dit boek eerder bedoeld voor data-leken, die nog overtuigd moeten worden van de kracht en waarde van big data. DNF.

[Colleendearborn · Rating 4 out of 5]

Excellent writing and narrating by this New York Times reporter. Steve Lohr brings this concept to life with interesting biographies of people who have worked on projects involving Big Data. I thoroughly enjoyed this book, and may listen to it again -- so I can understand it better. Got it from HooplaDigital.

[Spencer Perry · Rating 5 out of 5]

Insightful Read - Well-Written and Conscientious

This book is perfect for anyone who is thinking about how to apply data sciences to their field, as was my intent when I first picked it up. It covers everything from "what is all of this talk about data being the new oil?" to "how will this all be regulated in the future?" A great read for any aspiring quant.

[Tim · Rating 1 out of 5]

This is a book more about people than data. There are some interesting stories about how data is used to improve hospitals, pharma, hotels, and retail stores, but I wasn't that interested in the detailed biographies that filled most of the book. A quick read overall.

[Lisa Solko · Rating 3 out of 5]

OK, so it might not be the most exciting book, but it is interesting if you want historical look at some of the big players in the data revolution. This read like multiple biographies laced together under the umbrella of today's technology of data.

[Kathy Nealen · Rating 4 out of 5]

More of a wide ranging, multi-disciplinary “Who’s Who” of data science than a “What” or “Why” of data science. Still it was interesting to see the data science influence (present and future) in so many different areas. Privacy concerns are discussed but not a main point.

[Suzana · Rating 5 out of 5]

Good historical book of Data

The book is interesting and well written, but at the time of my reading (October, 2024) already has historical dimension as reality of data became more impressive.

[Patrick Lum (Jintor) · Rating 2 out of 5]

It was alright, but kinda just felt like an extended magazine article - no 'flare' to it for whatever reason.

[Darcie · Rating 3 out of 5]

A great introduction to data-science and how this science emerged his application in our society his issues and more.

[Kayson Fakhar · Rating 2 out of 5]

I’m reading this book probably because I am already aware of how important big data is. The major problem was the unnecessary emphasis on this importance till it gets annoying for me.

[Allisonperkel · Rating 3 out of 5]

Very shallow view into the world of data. About what you'd expect from someone who references David Brooks and Thomas Friedman.

[Neville · Rating 3 out of 5]

Nothing really exciting about this for me. The book didn't exactly talk about anything I didn't know already. If you're someone alien to big data, you'd probably enjoy this book more than I did.

[Tim Fiscus · Rating 5 out of 5]

This was great book on a 101 for data use. I have experience in data and uses, but this was good reinforcement.

[Andrew · Rating 2 out of 5]

Not great

[Joana Simões · Rating 3 out of 5]

Interesting book about big data, opportunities, challenges and the future.

[Dany · Rating 3 out of 5]

Interesante.

[Michael Williams · Rating 5 out of 5]

This book is a nice collection on the state of both days in 2015. Lots of interviews with leaders in the field and helpful explanations of how big data impacts all of us.

[Haley Stocks · Rating 4 out of 5]

An interesting look at some of the people and companies that have led the way we think about and process big data

[Megan Mariah · Rating 5 out of 5]

A very interesting read that made me think about how we collect and use information.

[Brian Tschiegg · Rating 4 out of 5]

Good Reporting

Lohr is a good reporter, but that can make his imagery a bit dry. The book served its purpose though: I know a lot more about how big data is changing the world in ways both good and bad.

[Patrik Bystrom · Rating 2 out of 5]

To be fair, I should have read the. Bookjacket more carefully. I anticipated a more useful and practical book. This is an exposé of the history of big data and its development. Full of anecdotes and (irrelevant) descriptions of what the various scientists were wearing at the time they were interviewed.

[Christopher Lawson · Rating 4 out of 5]

√ Big Data: The New Power Brokers of Society?

DATA-ISM is an extensive look at what the author views as a coming societal/business revolution due to "big data." "Big Data technology is ushering in a revolution in measurement the promises to be the basis for the next wave of efficiency and innovation. " The author believes that big data will lead to a huge step forward in computing: "The Big Data era is the next evolutionary upheaval in the landscape of computing."

The author explains that the term "data-ism"came from his colleague David Brooks, at the New York Times. Much of the focus of this book is on the top players in the game of big data. Scientists, and other researchers. One of these researchers is studying how to use the data in hospitals. With so many people sick and in intensive care, the amount of data coming from the instruments is astonishing. But is there a way that researchers and doctors can use that medicine to develop better treatment for these patients?

With all this new data, the authors suggest that decisions will not be made so much on intuition, or even experience, but instead, "Decisions of all kind should be increasingly made based on data and analysis rather than experience and intuition."

One very interesting thing I learned from this book was a website called acxiom. In this site you input your name and some other information, and it will tell you what information it has gathered on you. (Actually, you should use, https://www.aboutthedata.com/)

All in all, DATA-ISM is a fascinating look at the possibilities--both good and bad--of using this new barrage of information. There are extensive and notes to support the author's commentary.