I was very excited to write an essay for a special edition of Wired Italia. See Here
Here’s the version I submitted prior to translation:
Imagine going to a restaurant a few years from now. You didn’t make a reservation, but it was almost as if they knew you were coming. The staff greets you by name and seats you right away at your preferred table. The waiter offers up a special dish just for you and of course you can’t resist the nutrient infused water designed for your specific health needs. Perhaps you drink one more glass of wine than you should and indulge in your favorite dessert even though you had committed to losing weight. When the bill comes, it’s all a little expensive and more than you had budgeted on. But what can you do? The restaurant did a great job.
The restaurant anticipated what you like because they did, in fact, know you very well. Cameras recorded nonverbal clues during a previous meal, other sensors picked up your voice and comments at each stage of that meal and were not limited to just the restaurant or the food. Your chair recorded your weight before and after and noted your shifting position along the way. The exact duration of each meal element was recorded and managed. The restaurant glassware spoke volumes. What, how much, and how fast you drank was just the tip of the iceberg. Back at the dishwashing station, the plongeur ran a quick swab around the rim of your glass and tossed the tip into an analyzer. From a tiny drop of saliva, a minute amount of unique DNA was retrieved and fully sequenced. Your trip to the bathroom produced a trove of data. Although the restaurant would be quick to assure you that no human reviews the sounds and images of your bathroom performance, the machines never sleep. Your sound and motion was thoroughly digitized, analyzed, and recorded. “Intelligent” plumbing performed further tests.
Although you didn’t make a reservation, your cell phone gave away your location and put a name and e-mail to your face. The data service used by the restaurant told the maître d’ early on that you were likely to arrive. As you approached, geolocation from your phone sent an alert. By the time you stepped inside, the staff could greet you by name with complete confidence. The menu and pricing were triangulated from the restaurant’s stock of ingredients and your personal data.
The menu choices and the prices were tailored to you. The first glass of wine was discounted just enough so that you couldn’t resist it. When it came to the second, what the heck, you only live once. In collaboration with the data service, a detailed psychograph of your desires and propensities allowed the restaurant to almost perfectly anticipate what you would buy and how much you would be willing to pay.
Working with its data partner, the restaurant gave you exactly what you wanted and collected some extra revenue for that. The restaurateur does a steady business, nothing to complain about. He can’t seem to get ahead, though. That’s because every time receipts go up, lo and behold, the data partner increases its charges by a related amount. Over the course of a year, there’s a lot of money going out the door to the data partner. But there’s no way the restaurant can compete without data. Lots of other restaurants are willing to give a great experience based on big data.
The “restaurant of the future” doesn’t exist. It’s hard to imagine that anyone is taking DNA from restaurant glasses today, and restroom surveillance at least for now seems beyond the pale. Although companies are, in fact, working behind the scenes to develop psychographic profiles of consumers on a mass scale, most of this work is at a crude stage. Most restaurants don’t use big data to tailor menus to each customer. At least not yet.
But the technologies and analytics described in the restaurant story actually do exist, even if only in early versions. The forward march of technology is relentless. The price of sensors, computing power, connectivity, and data storage falls dramatically year after year. The capability of wringing actionable intelligence from mountains of data increases month by month. And the systems and business processes that allow product customization and dynamic pricing tailored to each individual customer improve every day. The techniques are far from perfect but change is arriving at the speed of data.
You can already experience this online. Researchers at Northeastern University in Boston found companies such as Staples, Home Depot, travel websites like Orbitz and Cheaptickets, rental car companies and hotels tailor prices to individual customers. Economists call this price discrimination, the practice of charging different prices for the same product and in the United States it is entirely legal. And price discrimination is proliferating throughout the global economy. Airlines have long been at the forefront of this trend. It’s a good bet that the person sitting next to you on a flight from New York to Rome paid a different price for the same service.
Price discrimination is occurring offline too. With the rise of advanced data analytics, grocery and pharmacy loyalty programs are getting more sophisticated. The American supermarket chain Safeway uses data based on a customer’s shopping history to offer personalized deals and coupons. “There’s going to come a point where our shelf pricing is pretty irrelevant because we can be so personalized in what we offer people,” Safeway CEO Steve Burd told the Associated Press in 2013. In the same article, Euan White, an executive with a firm that helps supermarkets use consumer data to increase profitability, said: “The price offered to the individual customer is really between the retailer and customer now. Loyalty programs just for you are an easy way to move to personalized pricing where each one of us, on our own, must negotiate with the seller.”
So why are firms moving away from the mass market to a market of one? They call it 121 selling. Companies are racing to capture the consumer surplus, the extra money a consumer would be willing to pay for a product if he or she was asked. Think of potato chips, for example. A potato-chip maker wants to sell to a million people. One-third of those people just want potato chips and are not fussy about flavor or price. They’re in a hurry and will take what they can get. The next third are willing to pay extra but only if the chips taste good and are of excellent quality. The last third of customers don’t care so much about taste but will not pay more than two dollars for a party-sized bag of chips. If the potato-chip manufacturer could pick these groups off one by one, it could offer the first group inferior-quality potato chips for three dollars and the second group better chips for the same price. The third group could get inferior chips discounted to two dollars. None of that is possible if the seller can’t separate the market into segments. If the seller can’t negotiate with each customer and wants to capture the entire 1 million sales, it must sell high-quality potato chips to everybody and price them at two dollars a bag. In a mass market all three groups of consumers fare better than if they had been segmented into separate groups, and the producer doesn’t get any excess profit. However, if the seller slices up the market into those three groups the seller can get more of the surplus, leaving consumers worse off.
It’s an axiom of economics that the party who knows more generally does better in a given transaction. In the past sellers could only guess at a price that consumers would be willing to pay. Now companies have the tools to assess the maximum price a consumer is willing to pay and the systems to charge individual prices. Never before in history have companies had the ability to know virtually everything about you, tipping the balance of power in the marketplace in their favor. With big data and powerful analytics and no meaningful safeguards on our privacy, companies can extract large portions of the consumer surplus systematically and with precision from person to person. Companies that know how much you’re willing to pay, as well as the extent of your financial resources, will possess the unique ability to make you pay as much as possible. Perhaps even all you can pay.
Big data has ushered in a new economic order. Conceivably, a single data giant, or more likely a few data giants, will hold unprecedented power over what consumers believe, what consumers are offered, and the final price of goods and services. Data giants will act as the middlemen on virtually every transaction, matching buyers and sellers while getting paid handsomely for that role. Only a handful of companies have that capability. Google, Amazon, Facebook for instance know (or can infer) not just who you are but what you are like, not just where you are but where you are going. Companies that watch what you’re doing now will soon have a pretty good idea about what you will do next. Few other companies can compete with the intimate knowledge of a data giant. And that means the data giants are poised not only to turn the consumer economy on its head but entire industries, perhaps every industry.
With their granular, real-time knowledge data giants will tailor commerce to individual tastes and circumstances. Product customization and restrictions on transfer will restrict the resale market, making arbitrage impossible. The mass market will disappear into an immensely complex, data-driven market in which prices vary from minute to minute and individual to individual. Standard goods and services will evolve into a multidimensional tapestry of customized offerings. Fortunes on a scale never before possible will be created. And the age of consumer sovereignty and commoditized products will finally pass away. If the use of data is not limited a new age of granular monopoly will arise as data giants dictate price and quality on an individual basis, in real time, over immense populations.
But we don’t have to accept that vision of the future. We are not powerless, doomed to sit and watch global well-capitalized firms amass more information than ever before conceived and use it against us in the marketplace, or the workplace, or hold our businesses hostage. The public doesn’t have to passively accept the terms and conditions most companies thrust on us these days—if you visit a store or website or buy a product they claim the right to spy on you as much as they want. The question for people around the world is who will demand a better bargain?
The first step is to understand the nature of data. Like air and water, data surrounds us. It’s essential to our way of life, something our modern economy can scarcely do without. And like pollution, the environmental challenges posed by data are fundamentally a matter of scale. When a neighbor keeps pigs, the noxious smell of manure can seep across property lines to affect near neighbors. That was historically a small-scale problem, and legal remedies evolved over time. But what if your neighbor builds an industrial factory farm? Large-scale intensive hog farms hold half-a-million pigs, producing immense quantities of waste. The smell emanating from waste lagoons can be considerable and affect a wide region. It’s not simply a matter of one individual impinging on the rights of another. It becomes an environmental issue in which an integrated commercial organization, for its own profit, impinges on the lived of a significant population.
The same holds for data. It is now possible to collect, retain, and deeply understand not just some but all of a person’s data. That possibility opens up prospects for harms and abuses that we have never faced before. From our DNA sequence to our online browsing history, personal data paints an intimate picture of our identity. Data is no longer just part of the landscape, a boring and almost inaccessible statistical shadow cast as we go about our lives. For many practical purposes, data is our environment. Unintelligible to the human eye, the ones and zeros recorded in machine memory reveal the riveting, tragic, and sometimes salacious details of our lives. Data has become an urgent environmental concern.
Around the world, environmental philosophy permeates our society. Limits on pollution, preservation of undeveloped land, zoning restrictions, protection of species, recycling, and energy efficiency are accepted goals of modern life. Today nations acknowledge the profound environmental challenges that lie ahead: climate change, population growth, species loss, and habitat destruction. That level of awareness would never have happened without a gradual learning process, whereby increasing numbers of people learned the scientific facts and adopted shared concerns. Through the bruising process of politics, increasing support for “green” policies became consensus and then law, affecting not only our current behavior but how we think about future human progress.
In contrast, the ideas of data environmentalism are in their infancy. Twentieth-century writers like George Orwell laid out a framework for thinking about data in the context of a totalitarian government. But until now, privacy was not a commercial or economic concern. In an earlier era only major governments were capable of mass surveillance, and only governments could benefit from panoptic knowledge in a way that remotely justified its cost. But that’s no longer the case. The falling cost of acquiring and using data on a mass scale opens up commercial possibilities far exceeding the scope of government to date. The threat from the commercial use of huge quantities of data is more immediate and in important aspects, more extensive, than the potential for government abuse. Commercial entities, by their very nature, act on behalf of their owners. They have no obligation to create widespread prosperity.
Data environmentalism begins with education. The general public must learn about the amount of data collected and how exploitable that data has become. Next people have to consider the implications for companies, for workers, and for consumers and appreciate where large-scale data collection is headed. Organization and collective action will be required.
It can start now. Your personal data belongs to you and you should control it. You don’t have to wait for Google or the FTC or the European Union to do you a favor. You can invoke established legal principles right now to stop companies from abusing your privacy. Some of us are trying to do just that. That’s why my co-author D.T Mongan and I established a web tool called My User Agreement to help consumers around the world dictate their own terms and conditions in commercial dealings. We don’t expect data collection to stop but we do want to ensure that the details of our lives can’t be used unfairly.
Of course that’s just a start. Unless limited through grassroots consumer demand or top-down legal authority, all of the important consequences described in the restaurant tale are virtually certain to occur, even if the exact methods differ. Fortunately many tools are ready at hand that could limit the abuse of our personal data. All we need to do is use them.