So in spite of its interactive patina, the digital economy continues the industrial practice of preventing real people from participating in the growth economy – at least as its beneficiaries. We still get to work and we still end up living and socializing on a landscape that feels much more like business than pleasure. There’s just no money.

In fact, the digital landscape so effectively monopolizes economic activity that most people have almost nothing left to be extracted. That’s why in order to maintain some semblance of growth, Internet companies had to find a way to extract something other than cash from its users. Something measurable, countable, and attractive enough to shareholder to justify their real cash investment in the companies’ stock.

That’s right: Likes.

Social media originally appeared to be an alternative to the marketplace ethos of the dotcom era. After the dotcom boom and bust, fledgling social platforms such as Friendster, Blogger, and Myspace seemed to be offering a return to the more peer-to-peer sensibility of the early Internet. But the alternative value systems they created – likes, views, reblogs, favorites, and so on – became a new kind of currency. It’s more than a mere trend in marketing styles away from broadcast advertising toward peer-to-peer social influence. It amounts to a shift in the way we value everything from entertainment and culture to consumer goods and the stock market. Likes are a new way to stoke the growth furnace.

Likes themselves are a metric of worth - and not just for teenagers gauging their social status. Real companies are valued in terms of the Likes they can generate. Brands from soft drinks to automobiles check their social media traffic for upticks on a daily if not hourly basis. A multitude of sweepstakes ask consumers to do nothing more than Like or retweet an ad for a chance of winning cash and other prizes. According to research conducted mostly by social media companies, these “social” recommendations - particularly from trusted friend - mean a whole lot more than plain advertisements.

The economy of Likes is most important to the social media companies themselves. At the time of its billion-dollar purchase by Facebook, Instagram had raised $57.5 million, was valued at $500 million, and had generated $0 in revenue. It did, however, boast 49.6 million Likes per day, which has grown to 1.2 billion in the ensuing year and a half. Likewise, Tumblr netted negative $13 million the year it was purchased by Yahoo for a billion dollars. What it lost in earnings it made up for in social traffic of 900 posts/second. Snapchat, a social media app with no revenue, turned down a $3 billion offer from Facebook - all for its users’ 400 million daily, dissolving pings.

Whether or not all that social activity will someday generate true, sustainable profits is still left to be seen. What we do know is that the Likes, Follows, Favorites, and re-posts are not as immediately valuable to the people and things being Liked as they are to the companies who mine these big data troves for trends. In fact, social media companies such as Facebook now occasionally surprise Wall Street analysts by reporting revenue vastly in excess of their expectations. That’s because the analysts are still thinking in terms of advertising dollars. The real revenue stream has much less to do with display ads than it does with the data that social media companies can glean from everyone’s friending and Liking - information social media companies sell to big data market research firms such as Datalogix and Acxiom.

But if social media companies are going to maintain their growth, they must continue to generate more and more Likes out of us humans. That’s why whatever social media platform we may subscribe to – take your pick – must, by its very nature, slowly take up more and more of our time and energy. They can’t take any more of our money, but they can take more and more of our attention and data.

Users are only slowly coming to grips with the fact that they are not Facebook’s customers, but its product. Many Americans reacted in horror to the news that Facebook was conducting psychological experiments on its users. But if they’d had any real awareness of how the company earns revenue, they shouldn’t have been surprised at all. The company was simply attempting to show that the kinds of emotional contagion that occur in real life also happen online. Their social scientists proved that if people see a bunch of happy posts, they are more likely to make happy posts, themselves. The controversy over the invasion of privacy or psychological manipulation may really just be displaced anxiety: we are just scared to see human emotion and action so successfully simulated and stimulated by machines. For what was really going on here? Facebook was proving that it had recreated human relationships but in the completely controlled setting of an online platform.

It’s back to the medieval bazaar, except without the unpredictability of real human contact. It’s a race for Likes - a value system rigged from the beginning to reward one’s volume of friendships over their quality. The more we depend on these numbers, the less truly social we become. While industrial age processes simply removed human beings from the equation, these digital processes seek to simulate humanity through artificial social media. As digital consumers, we are no longer engaging with humans, but with metrics. Life becomes one big power law distribution.

This is just where the winners of the industrial want to keep things. Trust your “friends” and trust the numbers. Experts be damned. Reviews by Consumer Reports, where real scientists in expensive laboratories conduct meticulous experiments on products, are to be ignored in favor of free “peer” recommendations from strangers. Professional is just another way of saying elitist, anyway. Who needs a real review when you just see how many people “like” something, instead? Market extremes otherwise dampened by expertise instead spin wildly out of control, while real-world professional experts, journalists, editors, and reviewers lose their jobs. This stuff is all written voluntarily, for free, as an increasing number of job categories are challenged by the Internet ethos of free and open exchange between all those people on the skinny, profitless expanse of the power law curve.

Social media companies grow at the expense of their users.

As if responding to this obvious critique of his Long Tail theory, Chris Anderson followed up with a book called Free, arguing that we should all give away our labor and products. In his view, creative professionals in particular should welcome the opportunity to give away their books, music and other products because they build up demand for other services such as live performances and lectures. “Those $20,000 speaker fees soon add up.” he explains. Of course, the only venues capable of paying those fees are corporations looking for speakers to validate their practices and reinforce the cycle of dehumanization - not schools and communities seeking help in resisting those forces.

Musicians, meanwhile, are supposed to sign “360 deals”, named for the idea that they are agreeing to let a single corporation manage all of their recordings, performances, merchandise, product placements and residuals. Albums may not generate much income, but a sold-out concert can. Only now, that concert needs to be supported by a steady flow of online media and new releases. According to industry expert Bob Lefsetz, musicians have to give up the quaint notion of sitting around in a studio for a year developing an album. The album won’t make money, anyway. Musicians’ new job is to develop a constant flow of new content to an audience that will forget them in a few months if they don’t. It’s all singles, and all designed to get and stay on the iTunes list and Pandora rotation - which themselves don’t generate even a fraction of the revenue musicians used to collect off album sales. That money only comes from going on the road, and only if you are a superstar.

For the rest of us, the math of “free” doesn’t quite add up, unless we happen to own the platform. Those of us who wrote for HuffingtonPost for years did so because we felt we were contributing to a progressive community platform. No, we were not paid in dollars, but there was a sense of solidarity in supporting a new kind of journalism, and a mutually reinforcing credibility when we all participated. When Ariana Huffington went on to sell the entire enterprise to AOL for $315 million, she did not cut her 9000 unpaid writers in on the winnings. Not even a token point or two was thrown to us. It was as if by receiving exposure on the website’s pages, we were already the beneficiaries of Ariana’s largess.

To be fair, there is an economy of sorts underlying all this free labor. It’s an economy of Likes - the word popularized by Facebook for clicks of approval or friendship on their social network. Likes, follows, tweets, retweets, favorites, and so on, are the new currency of the social media universe.

In other words, the best way to make money off one’s reputational currency is to sell it - either to brands who want to become “friends” with the fans of a particular celebrity or, better, to market researchers who want to gather data about a particular demographic. It’s not the people or their work that matter, but the data their activities create.

In a landscape dominated by social media, everything begins to matter less for what it is than how many Likes it can generate - because more Likes means more data to sell. Musicians don’t sell records, they sell the social networks that they were able to build through their music. The music, movies, and TV shows that entertainers create matter less to their careers than the volume of social media activity they can drum up around them. Rock videos and TV series are cast based on the number of followers a star can bring along with her. Artists and entertainers are no longer performing for human audiences so much as the big data computers. Nursing one’s Twitter or Instagram following is compulsory. Instead of taking acting lessons, the aspiring star must stir up social media attention, and keep feeding users more content in order to draw out more Likes from them. Given the way attention works online, this means resorting to the least-common-denominator antics: wardrobe malfunctions, sex tapes, and other, usually degrading sensationalism.

Cultural judgments aside, this online social climbing leads to a strangely circular career path: creators must develop social media networks in order to “make it.” But then once they’ve made it, the main thing they have to sell is not whatever talent they’ve come with, but the social media network they have amassed. Yes, a famous rock star can still make money on a tour, just as a TV star gets paid for appearing on a sitcom. But these jobs are really just fodder for the bigger prize of becoming a media property, oneself.

There is a certain, if limited, empowerment in all this. A large factor in making it as a performer or even a journalist was always the ability to generate advertising revenue. In traditional media, the advertiser could dictate to TV networks and newspaper editors. If a show didn’t somehow serve the advertiser it was pulled. Likewise, the entire notion of “unbiased” journalism emerged only after national brands demanded neutral backdrops for their advertisements, so they wouldn’t be accused of backing one side. By working their own social networks, creators no longer have “the man” looking over their shoulder. The new solution is to become “the man.”

“You are your own media company,” Oliver Luckett, founder of the first real social media talent and marketing  agency, The Audience, explained to me when I pressed him on it. “100%. That is every single person’s goal in this.” Working with online celebrities from Ian Somerhalder and Steve Aoki to Russell Brand and Pitbull - people with multiple millions of followers and Likes - Luckett uses a social data analysis platform to match his clients’ social networks with the right brands. So if 10% of a TV star’s million followers have also engaged with a particular shampoo or automobile brand on social media, Luckett is armed with data that can win his client a new social media endorsement. Likes for sale.

Pop stars like Jay-Z take it to a new level, distributing free music apps that log users’ contacts, geolocation and even phone records, all to scrape more user data, which is in turn sold to advertisers and market researchers. It’s as if no matter what business you’re in, profit ultimately rests on your ability to glean and sell the data associated with your transactions. Even on e-commerce sites, in many cases the profitability of retail transactions pales in comparison with that of the big data they leave in their wake. Creating relationships with consumers is really just about engendering enough trust to get them to share their data assets with you. Artists, publishers, newspapers, entertainers and cultural producers of all sorts will have to be tuned to, if not entirely geared toward, reaching easily identified social audiences. This is not a soft science, like determining a printed magazine’s audience in the old days. It’s hard data on engagement. As an author, my books will be less valuable as objects for sale (people won’t be paying for things like books anymore, anyway) but as the publishing tool through which I accumulate followers on social networks, whom I then sell to brands. So my books had better be brand friendly, and my audiences pre-selected for their data-richness. And even then, I’ll have to make it to the very head of the long tail to be of interest. Even social media deserves a better role in our lives and businesses than this.

The unsustainable endgame is an economy based entirely on marketing and advertising. In its currently inflated state, the entirety of advertising, marketing, public relations, and associated research still only accounts for less than 5% of GDP, by the very most generous estimates. Furthermore, unscrupulous website owners have now learned to use robotic ad-viewing programs to juice their revenue from pay-per-click advertising. Most of these bot programs run secretly on the computers of everyday users in the form malware, a kind of mini-virus that co-opts a computer’s processing power. Bots now comprise an estimated 25% of all online video ad viewers, and 10% of all static display ads. In 2015, advertisers are projected to lose $6.3 billion in pay-per-click fees to these imaginary viewers. Consider the irony: malware robots watch ads, monitored by automated tracking software that tailors each advertising message to suit the mal-bots’ automated habits, in a human-free feedback loop of ever-narrowing “personalization.” Nothing of value is created, but billions of dollars change hands.

Eventually, social branding has to run out of fodder. As more and more markets lose all revenue potential except what they can make as social media marketing platforms, who is left to buy all this marketing and consumer data? Consumer goods like soap and potato chips may have been able to keep mainstream broadcast television alive with advertising, but they cannot support the multi-billion-dollar valuations of Silicon Valley and the future of the entire digital economy. Besides, consumers themselves are growing increasingly unwilling to play along. Many of us are actually willing to pay for the things we want - such as HBO or Netflix - rather than waste our time on free products and experiences that exist for no other purpose than to mine our data. Google’s model of giving away everything in return for our looking at their ads and sharing all of our data may be losing ground to Apple’s “walled garden” model of paid apps and fee-for-service offerings. There’s certainly room in the ecosystem for both options. We just have to hope that it’s not only the wealthy who enjoy the luxury of a choosing between them.

So far, the Wall Street Journal, which caters to wealthy businessmen on expense accounts, is doing much better at extracting fees from its readers than the New York Times, whose paywall is derided by its largely Leftist audience as vociferously as if it were a violation of the Bill of Rights’ provision for a free press. Those who ask for an honest day’s wage for an honest day’s work online are treated as the enemies of the free and open net. In that sense, the effort to hide the humans on the other side of our purchases worked all too well.

There are a few people who manage to use their accumulated reputational currency to sustain themselves - but they do so by eschewing social superstardom and the sponsorship it brings, and turning instead to their fans. Think of this strategy as the online, digital equivalent of a local brewery, only the locality isn’t geographical, but cultural.

For example, Amanda Palmer, a musician with a small but ardent following, found herself without a record label after the company realized she was only selling about 25,000 albums - a paltry amount by industry standards. So she turned to the crowd-funding site Kickstarter, hoping to raise $100,000 from her fan base to make another record. She ended up raising $1.2 million, from a total of just 24,800 people. She was successful - and on her own terms - because she used social media to forge qualitatively strong connections with her fans instead of quantitative ones with the whole world.

Fortunately for Palmer, she enjoys doing the sorts of things required to keep that fan base feeling personally connected to her. She makes herself available to them almost 24/7, especially when on tour. Where some musicians might want to do their shows and escape to the hotel for some wind-down, for Palmer the show is just the beginning of a long night of dinner and dancing and conversation with fans in their homes, couch surfing, and more. So it’s not a strategy that can be implemented completely online, and it doesn’t leverage the net any more than it leverages the performer’s time and energy.

Again, it’s closer to the eBay model, with a seller connecting to her specific audience, rather than trying to climb the generic leaderboard. Besides, she’s not selling her social network to advertisers, so she doesn’t need a massive following. She just needs enough people to pay for her music directly. She may not get rich this way (the $1.2 million went mostly to production and fulfillment), but she can live on to sing another day.

Interestingly enough, while her own fans love and support her, Palmer has been quite vociferously attacked by those who don’t approve of her tactics. They argue that after receiving financial support from her fans, she should be excluded from participating in the sorts of barter and gift relationships she still enjoys on the road. How dare she enjoy the fruits of a “gift economy” of collaborators, meals, and lodgings, while also asking for money for her own labor? And she’s just one of many artists accused of such inconsistency, in the screechy tones of heightened outrage that only the dehumanized anonymity of the Internet seems capable of generating.

This “hybrid” approach to making culture may be messy, but it’s only upsetting when we look at it through the industrial lens of big corporations exploiting humans. As a business plan, it’s inconsistent. But Amanda Palmer is not some monopoly company, or even a superstar performer exploiting her fans; she’s one mid-list singer, trying to make a living in a winner-takes-all landscape intentionally designed to prevent her from forging real relationships or exchanging value with her listeners. Her mix of barter, money, and gift is actually much more consistent with the tangled, ambiguous nature of real human relationships.

If anything, it’s more traditional. In the pre-industrial 1800s, townsfolk would chip in to support a new blacksmith if they needed one, providing a barn, food, and tools until he could get his business going. They didn’t get shares in the company; they got a local smithy. Similarly, when we bought our oats from Joe the miller instead of Quaker the corporation, we enjoyed (or suffered) a real relationship with him. You might have bought your oats from Joe, but he bought parts for his mill from you. You weren’t simply one another’s customers, but interdependent members of a community. It was human, and it was messy.

Digital platforms from social media to crowdfunding allow us to reclaim some of these community dynamics and apply them to our own business pursuits. Those of us who have become aware of the way some corporations exploit or hide their tactics may have a knee-jerk reaction against people who appear, at least on the surface, to be doing the same thing. But the relationships that small businesspeople are forging with their constituencies online are direct, transparent, and peer-to-peer. These are explicit, fee-for-service and social relationships.

They are relationships between real people.

Big Data The value exchange between users and social networks, or fans and giant media properties, is entirely less direct and most intentionally covert. These digital networks exploit the same underlying human social dynamics in order to simulate the sort of social groundswell that an artist like Palmer creates in real life. The Hunger Games film series, for example, encouraged fans to retweet breaking news about casting and trailers. Fans who did so were rewarded with points called “sparks,” with which they could compete for prizes, mostly in the form of online acknowledgment for their devotion to the brand. The top 100 fans had their names listed (in order) on the Hunger Games website. Of course, none of the news they were tweeting was really breaking; these were all scheduled releases, meticulously timed to create the illusion of rising excitement up to the opening weekend of the movie. The fans only knew what the marketers of the film wanted them to.
But when you’re conducting social at industrial scale, what people know about your company is much less important than what your company knows about the people. It’s a one-sided, highly controlled relationship where, invariably, the platforms and companies with which we engage learn more about us than we ever learn about them. Social marketing both creates the illusion of a natural, non-marketed groundswell of interest and, more importantly, provides marketers with a map of social connections and influences. These social graphs, as they’re called in the industry, are the fundamental building blocks for big data companies to do their analysis.

Big data is worth more than the sum of its parts. It is both the technology for solving everything from terrorism to tuberculosis, as well as the endgame of nearly every business plan in the Internet space today. Like pop stars, none of these health, entertainment, or content “plays” will make any money on their own - but the data they can glean from their users will be gold to marketers. So they hope.

The platforms themselves matter more than the content they distribute. Content is not king at all; it is fodder, or the medium through which the valuable and data-rich interactions can occur.

For instance, social media stars such as Vampire Diary’s Ian Somerhalder have larger audiences online than they do on television or radio. Somerhalder’s Twitter following alone is over four million people; his TV show is lucky to get a million viewers. It’s not simply a matter of the TV program using Somerhalder for his pre-existing audience; it’s Somerhalder the social media property using the television program to brand himself and collect a few more followers along the way. The stories, sets, and costumes of the goth romance series comprise an advertisement for Somerhalder, imbuing him with attributes that, in turn, give him traction and specificity in social media. Yes, his vampire character’s charisma reflects well on the companies and products he associates with in social marketing campaigns. But his real value, or at least his perceived value in the social media universe, is his ability to generate useful data about everyone who interacts with him.

Indeed, big data is the latest of the universal tech solutions and the endgame of nearly every digital business plan. So the thinking goes: the revenue that a smartphone, education platform, video game, or health app can’t make on its own will somehow be recouped on the back end by selling all that data collected on its users. Every startup is a “big data play.” Yet when we take into account the fact that the revenue supporting big data apps must presumably come out of that same constant 5% of the GDP associated with marketing and advertising, it becomes clear that such a payoff can’t possibly come to pass - unless, of course, ours becomes an entirely data-driven economy.

The expectations for big data are irrational. And as I’ve learned from watching the digital business landscape for as long as there’s been one, whenever we see people projecting this much hope and fear onto something, it’s because they are overestimating the technology’s ability to deliver - or underestimating human beings’ unique importance in the scheme of things. This effort to reduce people to manageable sets of numbers is nothing new to digital technology. Computing was always synonymous with census-taking and population research. Famously, one of the first applications of the IBM punchcards was to keep track of ethnic groups in Nazi Germany. Recent revelations about NSA and corporate big data “spying” on us amount to less of an aberration than a continuation of this reductive, industrial age approach to population management.

For decades, even without computers, direct marketers have been using the data they have gathered about us to make decisions about how to market to us. This began long before spam, when the high cost of printing and mailing physical pieces of paper motivated marketers to limit their offerings to those homes that might actually be interested. They gathered publicly available data, such as tax records, mortgage information or driver’s licenses and purchased what they could from banks, doctors, car dealers, or anyone with a wealth of lists. They stored this information on physical notecards, and then manually selected a range of cards to include in a mailing. Over time, computer records took the place of notecards, but the decisions about whom to include in a mailing or how to word specific offers was still based on human logic. Wealthy families would be sent offers for luxury goods, those with children would get toy catalogs, and so on.

As the data piled up, statisticians began categorizing people into increasingly sophisticated demographic and psychographic groups, giving rise to the first data-driven market research firms such as Claritas and Acxiom. With upwards of seventy different categories in which to put us, researchers could arm marketers with psychological profiles of their target audiences, helping them to match their pitches to the particular social aspirations of their customers. But they soon realized that their data offered more possibilities than this: it could predict our future choices. Using more sophisticated computers and methodology, researchers began connecting seemingly unrelated data points, and became capable of determining who among us was about to go to college, who was probably trying to get pregnant, and who was likely to have a particular health problem. More than merely knowing our likely receptiveness to a pitch, they became capable of predicting, with alarming accuracy, what we human beings were going to do next. They had no idea why such a prediction might be true, and didn’t really care. This was the beginning of what we now call big data.

What makes big data different is that it depends on correlations that make no outward human sense. That’s the truly creepy part. Privacy is the red herring. Most people are still concerned about surveillance on the actual, specific things they are doing. That’s understandable enough. People don’t want the government knowing what they’ve said to friends over the phone about how they take deductions on their tax forms. They don’t want Tiffany’s receipts for gifts to a mistress showing up in divorce court. And they don’t want anyone knowing about the time they streamed that fetish video because they happened to feel curious one night. So when both the NSA and corporations assure consumers that “no one is listening to your conversations” and “no one is reading your email,” at least we know our content is supposedly private. But, again, content is the least of it. As anyone working with big data knows, the content of our phone calls and emails means nothing in comparison with the meta-data around it. What time you make a phone call, its duration, the location from which you initiated it, the places you went while you talked, and so on, all mean so much more to the computers attempting to understand who we are and what we are about to do next. Facebook can derive data from how long your cursor hovers over a particular part of a web page. Think of how many more data points there are in that single act than there are in the price of your car or the subject of your phone call.

The more data points that statisticians have about you, the more data points they have to compare with those of all the other people out there. Hundreds of millions of people, each with tens of thousands of data points. Nobody cares what any particular data point says about you - only what they say about you in comparison with everyone else.

For example, it may turn out that people who have cars with two doors, own cats, open a weather app between 10am and noon, and have gone on a travel site in the last fifteen minutes are 70% likely to purchase a pair of skis in the next three months. Do data analysts know or care why that data set is true? No. But it’s extremely valuable to the companies selling skis. Combine this with the ability of the web to keep track of individual users, and you get a true one-to-one marketing solution. Instead of buying ads that every visitor to a website sees, advertisers can limit their ad “spend” to the browsers of their target consumers. It’s the same technology that lets marketers hit us with ads for products we may have recently browsed on e-commerce sites - only now, instead of using our browsing histories, they use our big data profiles.

The same sorts of data can be used to predict the probability of almost anything - from whether a voter is likely to change political parties to whether an adolescent is likely to change sexual orientation. It has nothing to do with what they say in their emails about politics or sex, and everything to do with the seemingly innocuous data. Big data has been shown capable of predicting when a person is about to get the flu based on their changes in messaging frequency, spelling auto-corrections, and movement tracked by GPS.

This is powerful stuff, and not just for businesses hoping to cash in on the future before it happens. Large data sets help doctors track viruses through populations or determine genetic propensities for certain diseases. The World Bank has used big data to identify people without access to financial services who would benefit from a micro loan. If seemingly unrelated pieces of data could help determine the causes of cancer, wouldn’t you want to know them? A non-profit organization called DataKind partners civil society groups with data scientists working pro bono to explore everything from the corrupting influence of campaign finance to vaccine storage breakdowns. They use big data to evaluate micro loan recipients, child well-being indicators, and farmer education. Applied appropriately and openly, the ability to predict people’s situations, needs, and responses makes for better policy and resource management.

Praescient Analytics, normally a defense contractor, is putting its big data capabilities to work fighting human trafficking. The groups uses data gathered from online classified ads, social media keywords, and law enforcement and NGO sex trafficking information to map and predict where law enforcement and non-profit resources should be focused. Using big data, Praescient used the same technology they employed to track terrorists in Afghanistan to digitally map, predict, and triangulate the activity of a child prostitution ring at the 2014 Superbowl. That’s a human-centric approach to big data.

For marketers looking for an edge, however, mere prediction isn’t enough - and this is where they tend to get in the most trouble. Big data is simply a set of probabilities. Usually, it’s hard for analysts to get more than about 80% certainty about a future human choice. So, for example, big data analysis may reveal that 80% of the people who share three particular data points are about to go on a diet. That’s a pretty good indication of where to direct their ads for diet products. But what about the other 20%, who may have chosen to do something other than go on a diet? They get sent messages along with everyone else, aimed at convincing them that they need to think about their weight. Feeling fat today? If they weren’t already on the path to considering a diet, now they will be. And it’s not even human beings making the decisions about who to send which ads; it’s algorithms programmed to extract the most purchases out of consumers by exploiting their data sets. The algorithms use trial and error to see what works, iterating again and again until that 80% probability goes up to 90%. In the process, human novelty is reduced, and new possible choices are minimized as consumers are trained to conform to their statistical profiles. It’s a digitally complexified version of the one-size-fits-all values of industrialism.

On the surface, this increase in customers looks like growth. But it’s a limited, zero-sum game, where consumers’ possible behaviors are reduced to things people have done before. Moreover, the reduction in new possibilities cuts both ways. While big data may be a great tool in a larger arsenal of research methodology, it can also serve to limit a company’s innovative potential. Many of the companies I’ve visited have been cutting back on expensive, unpredictable research and development (R&D), and spending these resources on big data analysis. Why ideate in an open-ended fashion, they argue, when they’ve already got the data on what consumers are going to want next quarter? It’s virtually risk free.

What they don’t get is that using big data to develop new products is like looking in the rearview mirror to drive forward. All data is necessarily history. There’s the rub. Big data doesn’t tell us what a person could do. It tells us what a person will likely do, based on the past actions of other people. Big data is a very complicated map of history, through which analysts forecast likely outcomes. What it doesn’t take into account is novelty. New outcomes are never predicted by big data. It can’t do that, because it can’t see anything that hasn’t already happened. It can see the likelihood of another shoe bomber, but a mall bomber? Hasn’t happened yet, so it can’t be predicted to happen again.

As a result, companies depending on big data must necessarily reduce the spontaneity of their customers. They need to render consumers less lifelike and unique, and more consistent with some behavioral pattern that’s already proven to be easily exploited. That’s not a great relationship to have with one’s customers: hoping that they get less interesting.

Even if we can predict many future needs based on big data analysis of previous consumer choices, this leads to a limited, consumer-driven innovation cycle. What about the invention of new products? Game changers? These don’t come from refining our analysis of existing consumer trends, but from stoking the human ingenuity of our innovators. Without an internal source of innovation, a company loses any competitive advantage over its peers. It is only as good as the data science firm it has hired - which may be the very same one that its competitors are using. In any event, everyone’s buying data from the same brokers, and using essentially the same analytics techniques. The only long term winners in this scheme are the big data firms, themselves.

Paranoia just feeds the system. Becoming more suspicious of the data miners - as we do with each new leak of government spying or social media manipulation - only increases the value of data already being sold. The more restrictive we are with what we share, the more valuable it becomes, and the bigger the market that can be made. We might just as easily go the other way - give away so much data that the data brokers have nothing left to sell. At least that would put them all in the same boat as the rest of us.