This review was written by Eugene Kernes
Genre = Economics, Research
Overview:
Human thoughts are very complex, making many different ways
to access them subject to a variety of limitations. Big data via the internet is very revealing
about people. What people search for is
information that can reveal what they are thinking about. Providing a different way to learn about
people. There are four ways that big
data adds value to research which are 1) new types of data, 2) honest data, 3)
zoom on small subsets, and 4) generate causal experiments. The internet is a primary source of data that
uses messy traces, unlike traditional sources of data such as surveys and
questionnaires which are very neat. Big
data has many limitations as well, but it can help in a variety of situations. A problem with big data is that as it enables
better predictions about human behavior, the information can be misused by
corporations and governments leading to various forms of nefarious
discrimination activities.
People tend to be way more honest in their search terms rather than what they claim about themselves. Depending on the context, they can be honest because of potential consequences, or because there are no consequences. Surveys are usually anonymous, but people still lie on them because they want to look good. The desirability bias causes people to lie about who they are relative to who they want to be. Impersonal data tends to be more honest. People will confess when they are alone rather than in the presence of others. Facilitates knowing what people do rather than say they do.
Big data facilitates better understanding of the topics which can lead to better resolution methods. Big data reveals that individuals are not alone in their insecurities and embarrassing behavior. Making overt covert suffering. Google data can highlight many vulnerable people, as they might not want to report their trauma to official sources.
Offline experiments are time consuming and costly. The digital space enables cheap and fast randomized experiments. Gaps in understanding can be filled by testing. Gaps always exist.
Big data has limitations. The numbers measure what can be gathered, not necessarily what is wanted or important. Models created from the data does not indicate the reason why the model works. Knowing why models work may not be that important. But with this limitation, there is no indication of insights that can be gained and ways to improve understanding of the topics. There are data sources in which do facilitate lying rather than honesty. When there is no incentive to tell the truth, people make themselves appear better. Online presence is not always anonymous, and can cater to an audience.
Caveats?
The book is well written with plenty of examples and provides a general understanding of the power and complexity of big data. There are many topics in this book which are very sensitive, as in very private and personal. As such, the book may not be appropriate for minors.
Although big data
does open up more opportunities to consider how people think, what matters is how
the data is interpreted. There are a
variety of interpretations of the data, of which there can be many misleading
interpretations. Big data does offer
lots to think about, but not how to think about what it
brings up.
