You would think a book titled “Big Data: A revolution that will transform how we live, work, and think” by 2 of the world’s most-respected data experts would knock your socks off…or so I hoped.  While the first part of the book was as interesting as a TED Talk, the second part seemed more like two cheerleaders with pompoms. I was disappointed in the lack of constructive challenges and methodology facing deploying big data. Nonetheless, the book had good points.

Big Data: A revolution that will transform how we live, work, and think

The book highlights 3 major shifts that they consider revolutionary:

1- With far more data than ever before in the history of mankind, we can say that N = ALL for some datasets rather than use the traditional sampling for qualitative characteristics. It’s like a movie emerging from a series of photographs; you can see the story developing instead of snap shots.

2- With vastly more data we can loosen our grip on “exactitude” because with less error from sampling, we can accept more measurement error. According to the authors, we are obsessed with low margin of errors because of our “small data” world. It reminded me of throwing pennies in a large jar when your wallet is full of dollars, versus being poor in college and digging into the couch for those precious pennies.

3- With data up the wazoo, we’ll switch our mindset from focusing on the “causality of why”, to the “correlation of what” for occurrences. This was the hardest for me to believe. I personally think we would do both. However Chris Anderson explains it in details in his “The End of Theory” article.

Other interesting highlights:

  • The use of large amount of data (Big data) is not a new phenomenon. It has been used for centuries. However the information technology and digital age have made it possible to collect and analyze data on a scale not seen in the past.
  • The value of data isn’t just in its initial usage and shouldn’t be discarded. Data can be reused over and over in unlimited ways directly or in combination with other information to provide new findings for different industries. The example used is new and old photography.  When a photographer uses a normal camera, they must decide where to focus the lens. However using plenoptic cameras allows photographers to change the focus of a picture after the picture has been taken.
  • New business models are being created to take advantage of data opportunities that do not fit into existing organizations. For example UPS spun off its data analytics division because it could provide better value accessing data from competitors, than being part of the parent company.

As of the “dark side” of big data, the authors touch on personal privacy and misuse of data for profiling. They explained that with traditional legal system and outdated technical processes, it would be harder to protect personal privacy. As of the profiling/propensity –  penalizing people based on what they are predicted to do, not what the have done, they went on some weird tangent of science fiction discussion. They warned against falling victims for the “dictatorship of data,” and shutting off our reasoned judgment. They predicted a new class of experts called the “algorithmists” would manage big data the way accountants and auditors did 100 years ago.

Personally I was hoping for more discussions on issues facing datacenters today to ensure data integrity, or how technology is adapting to managing big data.

In summary, the book was a good read because it highlighted how far we have come with data and provided some interesting hypothesis. It is worth recommending to someone who is interested in learning about big data…, or if you want to piss off someone who is struggling with managing big data.

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