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Best Business Books 2013: Digitization

Three Harbingers of Change

(originally published by Booz & Company)

Viktor Mayer-Schönberger and Kenneth Cukier
Big Data: A Revolution That Will Transform How We Live, Work, and Think
(Houghton Mifflin Harcourt, 2013)

Marina Gorbis
The Nature of the Future: Dispatches from the Socialstructed World
(Free Press, 2013)

Henry Jenkins, Sam Ford, and Joshua Green
Spreadable Media: Creating Value and Meaning in a Networked Culture
(New York University Press, 2013)

Whether you invest, build, teach, research, regulate, investigate, heal, entertain, or sell, major changes in how you do what you do are looming. “Big data,” much in the media spotlight recently—particularly for the revelations of the National Security Agency’s (NSA’s) surveillance of “metadata”—is probably already changing how you do your work. But socialstructing and spreadable media, two new terms that signal similarly momentous shifts, may still be unfamiliar. This year’s best business books on digitization can equip you to better understand all three phenomena and the changes that they will enable and engender.

Tsunamis of Data

We humans and our machines are generating towering tsunamis of data. The Sloan Digital Sky Survey collected more data in its first few weeks than had been collected throughout the history of astronomy. A single lab can now sequence more DNA in a day than was sequenced in the decade-long, multinational effort required to decode the human genome. Google processes thousands of times the quantity of text in the Library of Congress every day. Its executive chairman, Eric Schmidt, claims we are generating more information every two days than in all of human history up to 2003.

Storing, processing, and making sense of these trillions of bits of data used to be impossible. But today it’s stupendously inexpensive to store data, it’s far easier to process it, and there is a library of sophisticated algorithms for making sense of it. Most tellingly, businesses (and other organizations and individuals as well) are recognizing a growing number of novel ways to apply it.

Thus, Target can deduce when specific women have just become pregnant—or are likely to become pregnant—from the patterns in their purchases. Google Flu Trends competes with the Centers for Disease Control and Prevention in predicting influenza outbreaks by tracking billions of Web searches for flu symptoms and related subjects with a half billion different algorithms. The SecDev Group can identify the location of probable cease-fire violations in geopolitical conflicts within 15 minutes. High-frequency traders can buy and sell stocks in microseconds, based on ultrafast analysis of all the stocks traded a microsecond in the past, a practice said to account for more than half of all stock trades and flash crashes. Scientists at HP Labs can successfully forecast the box-office success of films by looking at the rate at which relevant tweets are posted. The list of profitable applications of big data is far longer than this—and growing fast.

Viktor Mayer-Schönberger, a professor at Oxford University, and Kenneth Cukier, an editor of the Economist, plumb this phenomenon in their book, Big Data: A Revolution That Will Transform How We Live, Work, and Think. They base their bold subtitle on three assertions.

First, big data is qualitatively different from sampled data, yielding insights that are possible only when the size of your sample is close to the totality of the observed population. The really, really big picture can reveal details that were invisible with less than near-total sample sizes. Look through the right algorithmic lens and you can see things in big data that you can see only with big data.

Second, big data enables valuable forecasting of a wide swath of phenomena through the use of correlation, even though causation may be unknown. “Society will need to shed some of its obsession for causality in exchange for simple correlation,” suggest the authors, “not knowing why but only what.” Correlations that can be acted upon profitably are good enough to justify the use of big data.

Third, big data is messy and imprecise: “We don’t give up on exactitude entirely,” the authors write, “we only give up our devotion to it.” Theoretic understanding and precision might not be needed to profit from knowing just in time about the right messy, unexplained correlations.

Much of big data starts out as a side effect of human activity, without much intrinsic perceived value. This is so-called data exhaust: the amount of time our cursors hover over icons on Web pages, the daily price of butter in a million grocery stores, or the locations of legions of mobile phones minute by minute. The authors of Big Data regard this as data ore: a store of potential value that can be transformed into tangible value (only) through the extraction of useful knowledge and its application, whether that is to sell more units of a commercial product or to more effectively deal with natural disasters.

Mayer-Schönberger and Cukier aren’t uncritical cheerleaders for big data. They know that processing some of this data ore—the data connected to what we previously thought of as privacy or anonymity—may have toxic results. Consider AOL’s release of anonymized data about millions of its users’ behaviors for legitimate social science research in 2006. By applying big-data analysis, a journalist was able to lift the veil of anonymity and identify specific people—akin to the way Iranian revolutionaries pieced together shredded documents when they invaded the U.S. embassy in 1979. So although the NSA’s leaked PRISM program did not purport to collect the contents of citizens’ communications, the metadata gathered could inevitably reveal an enormous amount about them.

If “dataveillance” on this scale doesn’t give you the willies, consider that algorithms similar to those used to analyze flu trends could be used to predict which individuals are likely to commit crimes. A society that stops crimes before they occur? There was a movie about that flavor of police state called Minority Report. The authors of Big Data caution that the dangers of pervasive data-veillance are as real as the opportunities they foresee.

Harnessing Collective Action

Big data involves computers and networks slicing and dicing the artifacts of human and machine behavior in new ways, while socialstructing concerns a similar form of reuse through rearrangement. Socialstructing (the word, in its various forms, was coined by Marina Gorbis, the executive director of the Institute for the Future [IFTF], a venerable nonprofit think tank located in Silicon Valley) is a way to use connective and computational technologies to bring people together so they can restructure old ways of doing things and invent new ones.

The word socialstructing might or might not enter the public vocabulary the way big data has, but the phenomenon is already a significant enabler of collective action. The power of collective action—the force that brought us agriculture, cities, science, capitalism, and democracy, as well as slavery, fascism, and organized warfare—is determined in part by how human beings do or do not collaborate. Now that the Internet has lowered nearly to zero the transaction costs for large numbers of people to communicate, coordinate, and engage in collective action, a wide variety of socialstructed institutions are emerging in diverse fields: citizen science (Foldit), collaborative consumption (Airbnb), crowdsourcing (Genomera), crowdfunding (Kickstarter), co-working (the League of Extraordinary Coworking Spaces), microventure funding (, and peer-to-peer online learning (P2PU).

Socialstructing provides a name for a trend that some of us have been watching emerge for more than a decade. I’ve written about “technologies of cooperation.” Yochai Benkler described a new nonmarket form of economic production. Clay Shirky focused on how digital networks lower the barriers to coordinating collective action. Rachel Botsman extended the trend into what is now being called “the sharing economy.” Michael Nielsen described how this then-unnamed phenomenon was changing the way science is done. Now Marina Gorbis has pulled these strands together in The Nature of the Future: Dispatches from the Socialstructed World, by pointing out how diverse signals of social production are transforming a wide variety of institutions. (Disclaimer: I was a contractor for IFTF, and the book cites my work on online co-learning.)

One of IFTF’s forecasting tools is the systematic search for faint signals of change that might not make headlines today, but might portend systemic change in the future. Here, Gorbis details the signals of socialstructing in the production of scientific knowledge, in medical and pharmaceutical research, in finance, in education, and in governance—arenas that affect most people’s lives. Some of the author’s examples are eye-opening and compellingly credible, particularly the chapters on citizen science, sharing economies, and online peer learning. I was less convinced that the real changes Gorbis identifies in the fields of finance and governance will soon transform some of the biggest and most powerful bureaucratic institutions in the world, but certainly these fields are ripe for disruption.

Citizen science isn’t for the future—significant science is being conducted by communities of amateurs right now. Players of the online game Foldit have already identified important structural information about the protein protease, which is key to understanding HIV and the immune system. Hundreds of thousands of Galaxy Zoo participants have helped astronomers identify hundreds of millions of galaxies., a citizen-science biology organization, brought the price of an essential DNA sequencing machine down from US$10,000 to $600. Professional scientists aren’t going to disappear, but they are being aided and abetted by millions of citizens with powerful personal computers, broadband connections, and socialstructing platforms.

Healthcare is ripe for socialstructing. It is already enabling patients to not only take a more active role in their disease treatment, but also conduct their own research. For instance, by following established procedures in their own experiments and pooling their medical data, patients with ALS (Lou Gehrig’s disease) on PatientsLikeMe, an online community of 120,000, made an educated guess that lithium did not provide relief as had been rumored—18 months before professional medical journals confirmed that finding.

What some call using social capital, enlisting the help of others to accomplish tasks outside formal institutions, is also augmented by digital media. A sharing economy has given birth to dozens of services such as Airbnb (people rent out rooms in their homes), Lyft (people put a big pink mustache on their cars and provide rides to other Lyft members, at a fraction of the cost of a taxi), and NeighborGoods (people lend and borrow everyday items). The sharing economy definitely has legs, but it is uncertain whether it will become as powerful as citizen science and patient communities or whether its growth might be truncated by corporations, such as Hyatt or Hertz, defending their turf by acquiring these services.

The social institutions of education at all levels are under enormous pressure to change as classroom models based on the era of factories and mass production break under the demands of 21st-century knowledge economies. At the same time, learning, monopolized by schools for thousands of years, is morphing because of digital texts and online learning communities. Khan Academy, MOOCs (massive open online courses), well-funded “edupreneur” startups such as Udacity and Coursera, how-to videos on YouTube, platforms for peer learning such as P2PU and Skillshare: These seem less like isolated signals than a cultural shift at this point. As a participant and explorer in the field of online social learning myself, I can testify that something big is afoot. But whether and how these emerging “socialstructures” will change the ancient, inherently conservative institutions of public and private education is not yet clear.

Probably Gorbis’s weakest argument for significant structural change is the chapter on the potential for socialstructed government. The signals she points out, however, are fascinating and hopeful. For example, Stanford professor James Fishkin has perfected and tested “deliberative democracy” in Texas and Mongolia, California and Brazil, by bringing together groups of citizens of all political stripes together, polling them on specific issues, enabling them to learn from and interrogate experts who have different viewpoints, encouraging discussion, then polling them again—a rigorous experiment in innovative governance that shows how people can together learn to make better decisions about issues. Another example is the rewriting of Iceland’s constitution in 2011 and 2012. The world’s oldest democracy enlisted citizens to propose new clauses and to deliberate online. These are indeed signals worth paying attention to. But do they portend real political change?

Like big data, socialstructing brings dangers as well as opportunities. The greatest danger, the author of The Nature of the Future argues, may be new boundaries between those who are economically and educationally equipped to take advantage of socialstructed institutions and those who are not. Any changes in the way people organize social ties, political institutions, established work patterns, and measures of value are unpredictable, but it can be forecast with certainty that some people will always take advantage of any imbalances revealed by new technologies to further their own interests at others’ expense. I’m with Gorbis when she writes, “If we are not careful, the new curve may also bring with it new disparities. What direction this nascent curve takes is up to us. We are not passive bystanders in the unfolding of the future; we have some responsibility for and agency in shaping the kind of future we want to live in.”

Spread or Dead

Spreadable media is what results when socialstructing meets entertainment, advertising, and journalism. Consider the way that the circulation of media—for example, the millions of links, likes, tags, comments, blogs, tweets, and emails that can quickly make a video viral—has become a cultural force, and even a new form of economic production.

In Spreadable Media: Creating Value and Meaning in a Networked Culture, media theorist Henry Jenkins, formerly of MIT and now at USC, and his coauthors, digital strategists Sam Ford and Joshua Green, make a convincing case that fan involvement in the re-creation and circulation of media content is not just an interesting side effect of many-to-many multimedia networks and smartphone video editing apps, but a significant force for empowerment and exploitation in and of itself. “What we are calling spreadability,” explain the authors, “starts from an assumption that circulation constitutes one of the key forces shaping the media environment.”

Just as big data forces us to reconsider our privileging of causality over correlation, spreadability is forcing major culture creators, such as entertainment companies, to reconsider how much control of their content they should cede in order to see it more widely distributed. Armies of fans of anime—Japanese animated cartoons—voluntarily subtitle and recirculate their favorite videos in multiple languages, providing valuable exposure to the animators. Independent video makers generated more than $10 million worth of publicity for Mentos candy as a side effect of posting popular videos of people dropping Mentos into Diet Coke to create a geyser. The underground circulation of professional wrestling videos revealed hitherto unidentified “surplus audiences,” which prompted World Wrestling Entertainment to launch a new cable channel devoted to past matches and to sell DVDs of classic matches.

Jenkins and his coauthors also cite example after example of fans who produce cultural value for nonmonetary rewards, such as social recognition by their peers. For instance, fans of the Harry Potter books and films created Dumbledore’s Army, a worldwide online community that effects real change in the physical world. Its members sent airplanes full of medical supplies to Haiti after the devastating earthquake of 2010 (spreadability multiplied by socialstructing).

Spreadable Media debunks the notion of “influencers” that was popularized by Malcolm Gladwell. Citing research by network scientist Duncan Watts and others, its authors argue that networks and communities of co-influencers are more important than keystone individuals: “Any new system must respect the importance of surplus audiences and the role active audience members play as grassroots intermediaries shaping the experience of other audience members.” They also cite the importance of “produsers,” a word coined by Axel Bruns to define those who combine the functions of producers and users of media. “Produsers,” write the authors, “play curatorial and promotional roles, selecting and promoting content and creating metadata, improving the prospects of the material being found by future users.”

Spreadable Media is convincing in its argument that “successful creators understand the strategic and technical aspects they need to master in order to create content more likely to spread, and they think about what motivates participants to share information and to build relationships with the communities shaping its circulation.” Toward that end, the book provides detailed advice to content producers, such as using “transmedia touchpoints” to listen to what fan publics are telling them about their products, rather than using social media as just another channel for broadcast promotion. If you are in the music, movie, television, or game business, this book is a must-read.

Taken together, the signals and lenses described in the three best business books on digitization this year provide us with a clearer understanding of the positive and negative social, economic, and political changes that socialstructing, spreadable media, and big data could create in the near future. We are already seeing tectonic shifts in politics that are being caused at least in part by socialstructing. The influence of spreadable media can be seen in transmedia and mass-media products, which include hashtags and other spreadability affordances emanating from entertainment companies. And most wide-reaching of all, big data is influencing more and more aspects of life—surveillance and sales, public health and financial markets, politics and science—which is why Big Data is my choice as the Top Shelf selection for digitization.

Author profile:

  • Howard Rheingold has been exploring digital culture for 30 years. His books include Net Smart: How to Thrive Online (MIT Press, 2012), Smart Mobs: The Next Social Revolution (Perseus, 2002), and Tools for Thought: The History and Future of Mind-Expanding Technology (2nd ed., MIT Press, 2000). He has taught at Stanford University and the University of California at Berkeley.
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