Book review of "Big Data: A Revolution That Will Transform How We Live, Work and Think", by Kenneth Cukier and Viktor Mayer-Schonberger, John Murra

Tempini, Niccolò

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blo gs.lse.ac.ukhttp://blogs.lse.ac.uk/lsereviewofbooks/2013/05/02/book-review-big-data-a-revo lution-that-will- transform-how-we-live-work-and-think/by blog admin May 2, 2013Book Review: Big Data: A Revolution That Will Transform HowWe Live, Work, and ThinkIn Big Data: A Revolution That Will Transform How We Live, Work and Think , two of theworld’s most-respected data experts reveal the reality of a big data world and outline clear andactionable steps that will equip the reader with the tools needed for this next phase of humanevolution. Niccolo Tempini finds that rather than showing how the impact of data-driveninnovations will advance the march of humankind, the authors merely present a thin collectionof happy-ending business stories.Big Data: A Revolution That Will Transform How We Live, Work, and Think. KennethCukier and Viktor Mayer-Schonberger. Hodder. March 2013.Find this book My issue with Big Data is that it does not take big data seriouslyenough. Although the authors have pedigree (Editor at the Economist;Prof essor at Oxf ord) this is not an academic text: it belongs to thatcategory of  popular essays that attempt to stimulate debate. Anyonewho works with data (e.g. technologists, scientists, polit icians,consultants) or questions what will be borne f rom our age of  dataaf f luence may have expectations f or this book -  unf ortunately it f allsshort on providing any real answer.The book paints an impending revolution in mighty strokes. The authorsclaim the impact of  data-driven innovations will advance the march ofhumankind. What they end up presenting is a thin collection of  happy-ending business stories — f light f are prediction, book recommendation,spell-checkers and improved vehicle maintenance. It ’s too bad the book’sscientif ic champion Google Flu Trends, a tool which predicts f lu rates through search queries,has proven so f allible. Last February it f orecast almost twice the number of  cases reported bythe of f icial count of  the Center f or Disease Control.Big data will certainly af f ect many processes in a range of  industries and environments, however, this bookgestures at an inevitable social revolution in knowledge making (‘god is dead’), f or which I do not f indcoherent evidence.The book correctly points out that data is rapidly becoming the “raw material of  business”. Manyorganisations will tap into the new data af f luence, the outcome of  a long historical process that includes‘dataf ication’ (I’ll def ine later) and the dif f usion of  technologies that have tremendously reduced the costsinvolved in data production, storage and processing.So, where’s the revolution? The book argues f or three rather simplistic shif ts.The f irst shif t – the new world is characterised by “f ar more data”. The authors say that just as a movieemerges f rom a series of  photographs, increasing amounts of  data are as important because quantitativechanges bring about qualitative changes. The technical equivalent in big data is the ability to survey a wholepopulation instead of  just sampling random portions of  it.The second shif t is that “looking at vastly more data also permits us to loosen up our desire f orexactitude”. Apparently, in big data, “with less error f rom sampling we can accept more measurement error”.According to the authors, science is obsessed with sampling and measurement error as a consequence ofcoping in a ‘small data’ world.It would be amazing if  the problems of  sampling and measurement error really disappeared when you’re“stuf f ed silly with data”. But context matters, as Microsof t researcher Kate Crawf ord cogently argues in herblog. It is easy to treat samples as n=all as data get closer to f ull coverage, yet researchers still need toaccount f or the representativeness of  their sample. Consider how the digital divide – some people are onthe Internet, others are not — af f ects the data available to researchers.While a missed prediction does not cause much damage if  it  is about book recommendations on Amazon, asimilar error when doing policy making through big data is potentially more serious. Crawf ord reminds usthat Google Flu Trends f ailed because of  measurement error. In big data, data are proxies of  events, notthe events themselves. Google Flu Trends cannot distinguish with certainty people who have the f lu f rompeople who are just searching about it. Google may tune “its predictions on hundreds of  millions ofmathematical modelling exercises using billion of  data points”, but volume is not enough. What matters isthe nature of  the data points and Google has apples mixed with oranges.The third and most radical shif t implies “we won’t have to be f ixated on causality [...] the idea ofunderstanding the reasons behind all that happens.” This is a straw man argument. The tradit ional image ofscience the authors discuss (f ixated with causality, paranoid about exactitude) conf lates principles withpractices. Correlational thinking has been driving a lot of  processes and institutional behaviours in the realworld. Nevertheless, “Felix, qui potuit rerum cognoscere causas” (Fortunate who was able to the know thecauses of  things) – which happens to be the motto of  the LSE – is still bedrock in Western polit ical lif e andphilosophy. The authors cannot dismiss causation so cavalierly.However, it appears that they do. Big data, they say, means that the social sciences “have lost theirmonopoly on making sense of  empirical data, as big-data analysis replaces the highly skilled surveyspecialists of  the past. The new algorithmists will be experts in the areas of  computer science,mathematics, and statistics; and they would act as reviewers of  big data analyses and predictions.” This isan odd claim given that the social sciences are thriving precisely because expert narratives are a necessarycomponent of  how data becomes operational. This book is a shining example that big data speaks thenarrative experts give it. What close observers know is that even at the most granular level of  practice,analytic understanding is necessary when managers attempt to implement these systems in the world.The book is blinded by its strongest assumption: that quantitative analysis is devoid of  qualitativeassessment. For the authors, to datafy is merely to put a phenomenon “in a quantif ied f ormat so it can betabulated and analysed.” Their argument, that “mathematics gave new meaning to data – it could now beanalysed, not just recorded and retrieved”, implies that analysis begins only af ter phenomena get reducedto quantif iable f ormats. Human judgement is just an inconvenience of  a ‘small data’ world that has no role inthe process of  making data. This is why they warn that in the impending world of  big data, “there will be aspecial need to carve out a place f or the human”.It is hard to see how imagination and practical context will suddenly cease to play a f undamental role ininnovation. But innovation could def initely be jeopardised if  big data systems are not recognized f or whatthey are – tools f or optimising resource management. Big data may not be an instrument of  discovery;while certainly it is a way of  managing entit ies that are already known. Big data promises to be f inanciallyvaluable – because it is primarily a managerial resource (e.g. pricing f ares, f inding books, moving spareparts, etc.).In the world according to Cukier and Mayer-Schönberger, all the challenges of  knowledge-making are aboutto evaporate. With big data af f luence – sampling, exactitude, and the pursuit of  causality will no longer beissues. The most pressing question is the problem of  data valuation. Now there is a problem the authorsare willing to discuss seriously: how can data be transf ormed into a stable f inancial asset when most of  itsutility as a predictive resource is not predictable?So eager are the authors to mark the potential value of  big data f or organisations (data can only be anasset to a corporation) that they overlook the impact of  these systems on other social actors. So what ifbig data environments reconf igure social inequalit ies? While the cit izen will earn new responsibilit ies (likeprivacy management), only corporate entit ies will be able to systematically generate, own and exploit bigdata sets.Big data is serious. There will be winners and there will be losers. What the public need is a book thatexplains the stakes so that they can be active participants in this revolution, rather than be passiverecipients of  corporate competit ion.———————————————————————————————–Niccolò Tempini is a PhD Candidate in Inf ormation Systems at the London School of  Economics andPolit ical Science. You can f ollow Niccolò on Twitter @tmpncl. Read more reviews by Niccolò.

Book Review: Big Data: A Revolution That Will Transform How We Live, Work, and Think

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