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Big Data: A Revolution That Will Transform How We Live, Work, and Think is a book concerning the advancements, uses, and promises of big data. Viktor Mayer-Schönberger, Professor of Internet Governance and Regulation at the Oxford Internet Institute, and Kenneth Cukier, data editor at The Economist, argue that big data analytics is a revolutionary tool, used mainly in business, science, research, media industries, and social life. The central idea of the book, as mentioned in the title, is that humanity has entered a new phase of digital revolution where every aspect of human lives can be transformed in data that can be stored. Letting the data speak can lead to unprecedented insights into our actions and their consequences. The volume of data generated, stored, and analyzed in 2013 is incomprehensible: 1,200 exabytes stored information - less than 2 % is non-digital The book reveals the future possibilities of building on the analysis of vast amounts of data, by presenting brilliant examples drawn from world-renowned companies such as Google, Walmart, United Parcel Service Inc., Apple, Microsoft and the work of some of the foremost scientists such as Michele Banko and Eric Brill (researchers at Microsoft), Daniel Kahneman (Nobel laureate), Luis von Ahn (creator of Duolingo, Captcha and ReCaptcha) and several others of their ilk. Mayer-Schönberger and Cukier in this book provide a variety of interesting and useful insights into the power of data analytics. The central position about big data in this book underlines the notion that technology is not neutral. The way we collect data, the means and tools we analyze it, and what we do with the results are all shaped by our worldview. In each of the ten chapters of the book, the authors address the importance of sampling vast amounts of data, the significance of datafication, the value of data, the risks of data, and the tools required to control a data-driven society, by offering proposals to prevent or eliminate the negative consequences. See related video....click here Big Data represents three shifts in the way society is organized The book starts with an exploration of what the authors call the three main shifts in how we analyze information. 1. Big data makes sampling unnecessary 2. Large data sets make measurement error more tolerable 3. The search for causality is giving way for the systematic use of correlations to reveal hidden patterns MORE- Process all data, not just samples As supported by the authors collecting, organizing and then understanding the data has traditionally been difficult for at least two glaringly obvious reasons: 1. The majority of the world’s information has tended to be analog rather than digital. 2. Collating and then analyzing analog information is extremely expensive and time-consuming. The usual response to these challenges has been to analyze a random sample of the data and then extrapolate that rather than attempting to analyze all the data. According to the authors, sampling always blurs the details, and it’s often the fact that the really interesting things in life happen within the margin of error that inherently exists in sampling. The simple dynamic is the more data you use, the greater the quality of your predictions become. Taken to its logical conclusion, that dynamic means if you analyze all the data rather than just a sample, you’re going to come up with superior results no matter what. Using all the data makes it possible to spot connections and details that are otherwise cloaked in the vastness of the information. Viktor Mayer-Schonberger and Kenneth Cukier MESSY- Less desire for exactness The authors claim that once we start thinking in terms of big data, we have to make some adjustments to our thinking. One of these is we have to stop seeking for exact or precise measurements and instead become comfortable with the inexactitude which is inevitable in the real world of big data. The larger the scale, the more messy data always becomes at a micro level, but the more detailed the patterns become at the macro level. Simple models and a lot of data trump more elaborate models based on less data. Peter Norvig, Google’s artificial-intelligence guru Admittedly, as supported by the authors, treating data as imperfect in order to make better forecasts sounds somewhat counterintuitive at first but it is nevertheless true. Ultimately, big data may require us to change, to become more comfortable with disorder and uncertainty. Viktor Mayer-Schonberger and Kenneth Cukier CORRELATION - Move from causes to patterns It’s human nature to try and figure out the reasons why something is happening, but that turns out to be unhelpful in a big data world. Of far more value is identifying the correlations which already exist in the data and developing predictions based on those correlations, which also forecast their likelihood of success in the future. Predictions based on correlations lie at the heart of big data. Viktor Mayer-Schonberger and Kenneth Cukier To illustrate the aspect of data correlations the authors present the case of Amazon.com. When it first opened for business in 1995, the company employed around a dozen book critics to write reviews and suggest other books the reader might like. Then, a few years later, a software engineer suggested a better way to run Amazon’s recommendation system would be to instead base recommendations on purchase patterns – what other people had bought once they bought the initial item. When these machine-generated recommendations were tested against those made by human editors, they worked so well the decision was quickly made to disband the editorial team. Today, it is estimated about one-third of Amazon’s total sales are driven by its recommendation system which is pattern-based. Knowing why might be pleasant, but it’s unimportant for stimulating sales. Knowing what, however, drives clicks. Viktor Mayer-Schonberger and Kenneth Cukier Real-world data is messy, contradictory and often there are multi-faceted correlations involved. However, the tools needed to identify and unravel any correlations and relationships are becoming more and more sophisticated. Ultimately, in the age of big data, these new types of analyses will lead to a wave of novel insights and helpful predictions. We will see links we never saw before. We will grasp complex technical and social dynamics that have long escaped our comprehension despite our best efforts. But most important, these non-causal analyses will aid our understanding of the world by primarily asking what rather than why. Viktor Mayer-Schonberger and Kenneth Cukier Big data changes the nature of business, markets, and society Big data is poised to reshape the way we live, work, and think. The possession of knowledge, which once meant an understanding of the past, is coming to mean an ability to predict the future. Ultimately, big data marks the moment when the ‘information society’ finally fulfills the promise implied by its name. The data takes center stage. Viktor Mayer-Schonberger and Kenneth Cukier DATAFICATION - Datafication will become common As the benefits of big data become better recognized and valued, more and more real-world phenomena are going to be datafied or transformed into useful data which can be analyzed by computer. Daily activities that were invisible before (think reading a novel) leave a pattern now (think popular highlights in your Amazon Kindle). So, what exactly will be datafied in the future? Real-world measures – Time, distance, area, volume, and weight can now be measured and tracked with increasing levels of accuracy and precision. Words – Google and other companies are making headway in turning printed books into datafied text which can be searched, indexed and processed by machines. Location – With the launch of the satellite-based Global Positioning System (GPS) in 1978 and the today GPS modules embed in digital devices is feasible to match what is happening to a precise and measurable location. Interactions – Social media is generating a rich firehose of raw information which will be integrated into a wide variety of products and technologies in the future. The authors claim that when we look at the big picture, it’s clear pretty much everything will be datafied at some point in the future. Seeing the world as information, as oceans of data that can be explored at ever greater breadth and depth, offers us a perspective on reality that we did not have before. It is a mental outlook that may penetrate all areas of life. Viktor Mayer-Schonberger and Kenneth Cukier VALUE - Data will become a key asset In the sixth chapter, the authors address the importance of the various uses, reuses and option value of data. In a big data world, those who know more have a competitive advantage. The great thing about data, as supported by the authors, is once we have it in digital format, we have it forever. We can then do lots of different things with it: · Reuse it in a different way · Merge it with other data · Find novel or “exhaust” uses With big data, the value of data is changing. Data’s value shifts from its primary use to its potential future uses. Viktor Mayer-Schonberger and Kenneth Cukier IMPLICATIONS - Expertise will give way to analysis Mayer-Schönberger and Cukier discuss the importance of the value chain big data general has and looks something like this: · Data holders who have control over large collections of information try and extract value from the data but sometimes they lack the requisite skills and expertise. In that case, they often license their data to third parties who extract the value and pay licensing fees. · Data specialists are the firms who have the expertise and the technology required to carry out complex analysis of the data. Specialists capture and retain part of the value they create by taking the raw data and uncovering correlations. · The third group commonly found in the big data value chain are companies and individuals who have a big-data mindset and who can thereby see opportunities before others do. These entities might not have access to the data, or the skills required to act on their insights but by being astute first-movers, they spot opportunities to capture value. At present, those who have the big data mindset are coming up with the most innovative ideas are capturing the bulk of the value created in the value chain. Today, in big data’s early stages, the ideas and the skills seem to hold the greatest worth. But eventually, most value will be in the data itself. Viktor Mayer-Schonberger and Kenneth Cukier According to the author's smart leaders across industries are already seeing big data as a management revolution, one that brings new challenges and vast new opportunities. Companies that figure out how to combine domain expertise with data science will, according to current thinking, establish market leadership. On the other hand, they argue that corporate managers will also face challenges both technical and essential, ranging from understanding what kind of data the company needs and how it can access and analyze it to capture value. They also noted that data intermediaries, who collect data from multiple sources, aggregate it and develop innovation, will also flourish in the future. As value migrates from the expertise to the data itself, there will be many intermediaries who will profit handsomely. Big data has lots of positives but also a dark side as well As big data becomes more and more widespread, several key issues will arise which society will need to address. What’s the downside of big data? Big data raises at least three obvious issues as they are developed in the following chapters. RISKS 1. How to maintain privacy? Big data paralyzes privacy. It contains loads of personal information and allows some quite intimate details about a person’s life to be deduced. Big data is a whole new ball game when it comes to privacy and civil rights. The profitability the use of data presents raises ominous risks that threaten to undermine the control presented and mentioned by the authors in this book. Datafication risks arise when crossing the lines of consumer analysis to an invasion of privacy. CONTROL 2. Can we punish on propensity? Big data makes individualized predictions of future behavior based on patterns and probabilities a reality rather than a work of fiction. The authors see the threat of penalties based on propensity and wonder how that will be handled. Stopping a serious crime before it happens sounds enticing but wouldn’t that be a perilous path to take? The controversy over profiling will pale into insignificance in an era of big data predictions of future behavior. Schonberger and Cukier in their book make two main suggestions on how the data-driven companies to minimize such risks: firstly, by auditing their information and secondly, by avoiding breeches of confidentiality. NEXT 3. Will we fall victim to the data? The tenth chapter of this book analyzes and presents the importance of some vital characteristics of big data such as accuracy, exactitude, cleanliness, rigor of data. But there are times when relying solely on the data brings unintended consequences. The solutions to all these potential problems with big data are still in the process of being figured out but will probably incorporate these elements: · Instead of getting people to consent to making their data available, data users are going to need to be held accountable for what they do – mainly because secondary uses of data are going to be so pervasive. · Society’s understanding of the concept of justice will need to be updated – and the law will need to state explicitly people should be held responsible for their actual behaviors and not their propensities. · Algorithm auditing will be needed – a new class of professionals will be needed who will be trained in being able to analyze the integrity of data sources, the choice of analytical tools used, and the soundness of the results generated. According to the authors’ analysis, as data becomes the fuel for business growth in the future, it is likely the same kind of antitrust rules that have been used in the past will be repurposed and applied to big data. This book is a useful introduction to the culture of big data. For those who are related mainly to business analytics, marketing research, uncertainty in decision making, price discovery, marketing management, social engineering, and policymaking and wish to investigate more thoroughly, there is an index and extensive endnotes and a detailed bibliography. Get your copy from Amazon HERE!!