When you read an original book, in full, you expect to get the full benefit of the author’s thinking process and have their ideas spelled out in detail. When you are just reading a summary, then you can expect something less. And, in this case, my review is of the Blinkist summary. When I’ve had the chance to check a Blinkist summary against the original book, the summary has come out pretty well. And life is short; so I’ve been reading a lot more of the summaries recently. If they really grab me, I will seek out the original. Meantime, here are a few snippets from the summary that caught my attention:
Big data is data for which computational power is required to recognize patterns.
utilizing gathered data correctly is essential to refining one’s worldview......It helps us identify more precise patterns and predictions than personal experience alone ever could.....Though a gut feeling may get us far, data refine even the most intuitive person’s perspective.
What set Google apart was that the collected data could be used efficiently.......Brin and Page’s algorithm [for Google] worked differently. They figured out that a website was likely more relevant to someone if it had more links from other sites that took a user to it....Google engineer Jeremy Ginsberg did. He showed that flu-related Google searches, such as “flu symptoms,” indicate of the spread of influenza, and can be used to track the spread of the disease across geographical areas and over time.
In a survey, two percent admitted they had graduated with a GPA lower than 2.5 on a four-point scale. However, according to official records, the number was much higher, at 11 percent.......It is a universal truth about surveying: people lie.
This behaviour of giving answers that make us look better is called social desirability bias.
Why big data is so powerful: it doesn’t lie. Because it's collected through unfiltered online behaviour, it will always reveal the truth......But big data, it isn’t flawless. Its biggest limitation becomes patently clear in datasets with many variables:.......it’s difficult to extract reliable answers because the number of variables obscures possible findings.
There are so many variables that patterns can occur randomly......For instance, Facebook can easily measure clicks and likes using big data. But doing so would tell the company nothing about people’s experience with the site......In circumstances like these, small data is essential. Facebook gathers this sort of data through other methods,
Every month, there are 3.5 million suicide-related Google searches in the United States. By contrast, the number of suicides in the country is lower than 4,000 a month.
Should governments even be allowed to possess and use search data pertaining to individuals?.......But more and more evidence points to a correlation between online searches and subsequent action.......Suicide-related Google searches are significantly correlated with actual suicide rates. But that correlation was only valid at the state level.
The key message in this book: People rarely fill out surveys honestly, which skews our understanding of the world. But with the rise of big data–that is, the collection of incredibly large amounts of data from, for example, Google searches–we are able to spot patterns in human behaviour and identify preferences that we never knew about before.
Actionable advice: Don’t fret if you have kinky sexual fantasies. You’re not alone! Although you probably won’t get everyone to admit to their fetishes, this may be just because some individuals worry they’ll be socially excluded. So, if you dare, speak up about your true preferences! You’re likely to get some weird looks, but as big data reveals, there’s almost certainly someone out there like you. Instead of hiding it, you can make all the kinky and strange stuff you normally type into Google a topic of conversation. Maybe then you can start normalizing some of the unspoken aspects of human behaviour.
My take on the book? Overall, I liked it. Learned a few new things. Learned a bit more about big data and the Google algorithm (that I should have known, but didn’t). I know my son in law’s company is making great use of big data and spending patterns. (And I suspect my Bank has been using it on me as well)......and there are some rather scary aspects to that. Especially the bias in algorithms ....which are fine unless you are the person who is always pulled aside at an airport because of some kink in the algorithm. And I was aware that surveys were always unreliable because I’ve used them in the past and they have been very poor predictors of what people actually did compared with what they said they WOULD do. Four stars from me. ( )

Believe the hype. This is not a perfect book, but it's fun, enlightening, ground-breaking, and important. Too many people don't know the potential power of the new methodologies of data analytics, and too few ppl who think they do know that power don't know the limitations. SethSD does, and he shares a lot of what he knows with us.

This is good science for arm-chair science consumers like me, and a good read for those who just like to dabble in non-fiction. It's both concise and rich. Documented with notes, and index, and the author's own website which he promises has lots more hard info.

It may turn out to be a four-star book as more on the topic get published. But right now I urge everyone to read it. Next, I do hope to read Seth's next book, and more on the subject. Yes, Seth, I did read right to the end, and still I'm glad you didn't keep struggling to say anything for the ages in your conclusion... imo, you ended it perfectly.

On a personal note, one of the key points from the intro. and one of the key points from the conclusion are amazingly relevant. Here's the thing. Our youngest is looking for a school to transfer up to, at the same time we're looking for our first post-retirement community. We're hoping to find a college & town all three of us would like, and a particular field of study for our kid. In the beginning of this book are two maps, one that reveals Trump supporters, and one that reveals pockets of closet racists as exposed by their Google searches)... which is obviously relevant data for us as we choose what part of the country to move to. And at the end of the book, Seth tells my geeky son what studies to focus on:

"I hope there is some young person reading this right now who is a bit confused on what she wants to do with her life. If you have a bit of statistical skill, an abundance of creativity, and curiosity, enter the data analytics business."

(Well, my young person has been listening to me read bits from the book, but otherwise that could have been directly tailored for him.)

Read the book. Don't be fooled by my long review; I'm only sharing a bit of what I learned from it.

Other book darts:

"[P]laces with the highest racist search rates included upstate New York, western Pennsylvania, eastern Ohio, industrial Michigan and rural Illinois, along with West Virginia... The true divide... was not South versus North; it was East versus West. You don't get this sort of thing much west of the Mississippi. And racism was not limited to Republicans...."

The 4 powers of Big Data can be summarized:
"Offering up new types of data..."
"Providing honest data..."
"Allowing us to zoom in on small subsets of people..."
"Allowing us to do many causal experiments...."

Now we get to an example of what is not perfect about the book. First, context: Seth is a careful scientist; he knows about sampling errors, biases, correlation not equaling causation, etc. However, sometimes he forgets about alternative explanations and interpretations. That is to say, when the book shows us data, it's fine, but sometimes when Seth interprets the data, he gets trapped by a fallacy. Eg, he says, "[O]f the minority of women who visit PornHub, there is a (25%) subset who search... for rape imagery... sometimes people have fantasies they wish they didn't have and which they may never mention to others." Maybe... or maybe they're victims trying to process, or maybe they're wannabee authors doing research, or they're men lying to present as female.... It looks to me like Seth didn't want to think too hard about this one....

Big data allows researchers to zoom in on subsets of demographic groups, and geographical regions.... "But another huge--and still growing--advantage of data from the internet is that is easy to collect data from around the world.... And data scientists get an opportunity to tiptoe into anthropology."

Big data could really help in the field of healthcare. When I'm done here I'm going to check out the site PatientsLikeMe.com. "Heywood hopes that you can find people of your age and gender, with your history, reporting symptoms similar to yours--and see what has worked for them."

I also want to consider reading [b:Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked|30962055|Irresistible The Rise of Addictive Technology and the Business of Keeping Us Hooked|Adam Alter|https://images.gr-assets.com/books/1479719623s/30962055.jpg|51577230] and [b:Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart|1081413|Super Crunchers Why Thinking-By-Numbers Is the New Way to Be Smart|Ian Ayres|https://images.gr-assets.com/books/1320449889s/1081413.jpg|2022993]. ( )

The author's thesis is that people tell the Google search bar truthful, shameful things that they would never reveal to another human being in a survey. He analyzes Google searches and other sources of big data to come up with interesting, often lurid insights about people's hidden nature. I found it fascinating. ( )

A light read regarding big data. He covers the positive uses and pitfalls of analyzing big data. The research does raise questions about the subjects covered. I just wish more methodology in the research used was shared. ( )

Сеть все знает «Могущество Google в том, что люди рассказывают гигантской поисковой системе то, что они не могли бы сказать никому другому», — замечает Стивенс-Давидовиц. Этот поисковик, утверждает автор, новый и уникальный инструмент для изучения человеческих страстей. Пока еще не все данные получается интерпретировать, но аналитики уже могут предсказать, за кого вы будете голосовать, — подоспели результаты разбора победы Трампа. Изучение Facebook-статусов приоткрыло завесу над секретами счастливых пар (большое число общих друзей является существенным показателем того, что отношения НЕ продлятся долго), а big data из других источников сообщает реальные размеры безработицы в стране: Pornhub регистрирует аномальные всплески посещаемости после сокращений. Сеть знает людей лучше их самих.