2Â°) Precision does not matter. The less data one use the more qualified and precise they must be. Working with big data causes less precision but the large volume helps to get results no once could have expected before. We need to learn to trade precision for trends. That also proves that big data does not fill all. We’re in the field of probabilities, not exact prÃ©vision, but probabilities that are maximized through the volume of data that’s processed.
With Big Data we’ll have to believe even if we don’t understand
3Â°) We’re shifting from the world of causality to the world of correlation. This point is very debated and I imagine lots of experts won’t agree with that. In this post titled “big data bullshit” (unfortunately only available in French), Christian Faure tells us there’s nothing new here, causality being the highest level of correlation. I also recommend this post (still in french) that explains that the revolution the switch from deductive thinking (start with an hypothesis, deduct the consequences and check if it works in real life) to inductive thinking (start with an observed situation and try to generalize it). On the other hand, what everyone should agree on is that deduction and induction does not apply to the same needs and, more than the switch from one to another, what’s disruptive is that big data does not come with explanation. Big Data does not tell why something happened or is likely to happen. It says that A impacts B, that “we can predict that”, but it won’t explain the process that makes that a change in A causes a change in B. Period. That’s a major disruption for our cartesian minds. We’ll need to learn to believe without understanding.
Why should we pay that much attention to Big Data. First because technology now makes it possible. Then because raw material, data, gets bigger everyday, a phenomenon called datification. Of course there the data we know, continuously generated by our information systems. But the digitization our our lives makes that anything that happens in real life generates data. From our digital assistants that will even record our vital signals to medial reports, including traffic on the roads : the list of available data has no limit but our imagination. Now we can use all these data to understand how things work, what impacts what, on what to focus and act as a priority.
Data is the exhaust of a living world
If any action in the world generates data (and it’s actually the case : police or firemen intervention, public parks watering, attendance to a class, browsing a website, natural phenomenons like wind speed, water temperature etc…), data is the exhaust of our activity, exhausts than can be analyzed to understand our environment and embrace its complexity in the same way we analyze sediments to understand what earth looked like thousands of years ago to guess what is going to happen in the future. The difference is that Big Data is real time : it’s about here and now.
Of course that’s not that easy. One need to find the right data, the relevant ones and process them properly. That’s the theme of another part of the book, dealing with the new jobs big data makes necessary and the value chain of this industry. There are those who own data, those who processes it and those who use it. They are part of a moving value chain and it’s still hard to guess who’ll drive the most value from it in the future. May be it’s going to be data owners, not those who process it or those who invent new cases and data-based business models. Anyway, the jobs related to exploiting data are numerous and will lead to emergence of lots of new activities. There are already lots of examples in the book and we cannot imagine what will happen the day we’ll really understand what we have in our hands.
Private individuals see their privacy threatened and are excluded from the value chain
And what about private individuals ? That’s a point that’s often overlooked or never fully dealt with. The book tackles – of course – data privacy concerns and the risk of a society that would try people on a predictive way. Law should guarantee that Minority Report will remain fiction because it threats the foundations of our social contract : people must be help responsible for what they do, not for things we predict they may do. Globally speaking privacy should be reinforced and those wo use data held responsible for any use beyond the primary reason data were collected for. Unsurprisingly, current regulations do not meet these new challenges.
The book also tackles the place of private individuals in the value chain, a matter that’s too often overlooked. In fact we’re facing a major shift : for the very first time in economics, the one who produces the raw material that’s almost stolen from him is excluded from the sharing of the value that’s built with. It’s even a double punishment : people get no share of the value generated with their data but their data can even be used against them (bank, insurance, police) or to make them spend more. According to the authors it’s unthinkable that, one day, people will negotiate individually the use of their data for a fee (the system would be unmanageable) but we can imagine that, tomorrow, data brokers do it for them on a collective scale.
It’s impossible to tackle the entire matter in 200 pages but I think this book is pretty useful to get the big picture and make people of the challenges so anyone could have an overall understanding of what’s happening, where we’re heading to and to what extent it will transform our lives. It’s a very good start to acquire a kind of big data literacy. Then it’s up to anyone to go deeper into any specific field.
It’s already quite hard to refuse the interconnected world we’re leaving in but, tomorrow, it will be impossible to avoid the world of data. As a player or as a subject. For the better (services, health, crime, decision making) and the worse (privacy, behaviors normalization, exclusion of those who don’t fit in an “ideal and harmless” model.
You can order Big Data: A Revolution That Will Transform How We Live, Work, and Think on Amazon.