The driving force behind the evolution of capitalism is innovation. The discovery of coal led to the industrial revolution in Britain in the 18th century, and the competition forced other countries to adopt a similar manufacturing-based economic system. Consequently, American life changed, as well. The commodification of coal created urbanized cities and led to the development of the working class. The industrial revolution is one of the more recent examples of innovation changing the economic landscape. But the most recent, and the one we are living in right now, is the age of big data.
The internet established an unprecedented level of communication and information available to the public. Its creation revolutionized the way we see the world. Post-industrial revolution, economics was driven by oil; it became the new coal. Oil’s successor, however, is something far less materialistic but far more valuable.
The global big data industry is currently valued at $140 billion and is estimated to nearly double by the year 2025; the financial companies of yesterday trumped by the tech corporations of today. Yet, the entire industry revolves around one central principle: data commodification (the process of monetizing data). While intangible, the insights data can provide have the potential for a company to make billions.
Data, accompanied by AI and other powerful algorithms, can:
- map out consumer habits or characterize groups of people
- optimize systems by which the manager can detect any shortcomings
- predict consumer and market trends
Given its uses, data is for sure a comparative advantage to any business that utilizes it.
But the process only works with a large sample size. A single data point doesn’t provide useful information; it’s the connections and implementations of collective data that makes the process so profitable. For this reason, data will always be monopolized. A company’s data is worthless to the consumer and to companies in different industries: Microsoft’s data doesn’t mean much to John Deere.
Amazon, for example, uses consumer data coupled with market research to determine the price a product should sell. It’s also how it recommends specific products to the user. To do this, Amazon tracks your purchase history, browsing history, overall market trends, the popularity of products, and more. Prices on Amazon fluctuate as frequently as every ten minutes. Billions of data points are being extracted during that time frame and are used to maximize Amazon’s profits, all at the cost of your privacy.
Data commodification is a business model employed by many tech companies that sell IoT products. Every product has a market size and is limited in sales, but data is infinite and can be produced by the millisecond, unlike its oil predecessor. From fridges that order milk when you are out to wearable devices that track and record biometric data, everything is digitalized. All of the data generated by the smart devices in your home can easily be communicated to their creators via the cloud, and the information processed via cloud computing (Azure, for example).
A smart home assistant can listen in to private conversations, find out what you are planning to do, and when searching for something on the web, recommend sites it knows you will like. Companies can also sell your data to third party companies, something you give consent to in the company’s cryptic terms and conditions.
But, when an appliance is sending a constant stream of data back to its maker, that company has continuous relationships with the owners of its products, and can find all sorts of ways to make money from those relationships. — Adam Davidson
Data commodification has led to a new era in capitalism dubbed “surveillance capitalism” by Harvard Professor Shoshana Zuboff. The surveillance economy depends on the constant extraction of data from the lives of millions. Continuous data extraction can create a feedback loop capable of analyzing your actions, and through data analysis, nearly recreate your personality.
Tech companies have warped the rules of capitalism, as did the industrial revolution. No longer are the consumers buying a product or service from a company. It is the consumer who is the product itself: it is what we click on, what we say, and what we do that is being profited off of. Although it might seem like compensation for being able to use a specific service, TikTok for example, for free, the significance of data has far more pronounced implications than just better marketing.
The issue of data collection is one of privacy and individual rights. Consumers’ data is collectively worth billions, but consumers themselves don’t have rights over their assets. Any attempt to hide your data results in an inability to use the service or product. Even if you are willing to sacrifice the convenience of having such services, society will not. And this goes back to the underlying problem of it all: we are constantly connected to the internet and unable to disconnect.
It’s also an issue of autonomy. Companies are constantly contending for our attention; the longer we stay online, the more data can be extracted, and the more profit they can make. We are being subconsciously convinced to stay connected by being provoked with targeted ads and notifications. The saying “throw everything at the wall and see what sticks” has never resonated more. It’s just that what’s being thrown is controlled by sophisticated algorithms that have access to immense data.
“The most important thing about a technology is how it changes people.” — Jaron Lanier
The situation can be likened to the peasants in a feudal system, with the land-owners being tech companies and the land-workers being the consumers. The land-owners can profit off of the backs of peasants, almost being played like puppets on a string. The system, although archaic, works and is very profitable for the land-owners, so long as the land-owners have a firm grasp on everything.
And so begins the era of data colonization. As social media apps, online websites, and IoT devices continue to pervade our lives, tech corporations can extract more information and find new uses for them. The arrival of the surveillance economy changed the way companies view value. With data extraction being more profitable than the device sold itself, companies rush to get their products to market to gain a monopoly over the data generated from potential customers.
It’s a race to get product costs as low as possible to tap into pools of buyers of all demographics. Countries like Africa and India, where tech is more expensive and not nearly as widespread, hold an untapped abundance of data that could provide valuable insights to tech companies of other nationalities and ethnicities.
In today’s open-market economic system, companies cannot afford to keep their consumer’s data private. As more companies shift to a data-centralized business plan to make a profit, companies that don’t follow suit will lose the data colonization race: cheap data collecting IoT products will be able to flood the market and overshadow any other competitor that doesn’t mine data.
As the European powers of the 1700s colonized nations all over the world, data is driving tech companies to infiltrate homes and minds with their smart products. While far less violent, it is just as sinister.
Companies will continue to allocate resources towards finding new ways to extract data from their users without their knowledge. Part of the problem is a severe lack of legislation. Understanding the extent to which our data is mined can help legislators institute policies to maintain a certain degree of fairness.
General Data Protection Regulation (GDPR) is a landmark piece of legislation that will for sure be used as the guiding light. It’s the first of its kind, but it too has its shortcomings. GDPR places a lot of regulatory burden on small businesses that may not have the resources to meet these requirements. It is also vague in some respects. GDPR gives the user the right to delete data a company might have on them, but if this data is used to train a machine-learning algorithm, does this model also have to be deleted?
To clarify such ambiguities, the government needs to work hand-in-hand with the tech industry. The government alone is not best suited to develop new legislation but the tech industry also cannot be asked to self-regulate. A balance needs to be found, one that will require much deliberation.
Furthermore, if researchers and legislators were given the source code to the algorithms that process consumer data, they could set regulatory testing and understand potential sources of bias. Removing this barrier will create a level of accountability. But of course, source code is a matter of intellectual property. By making it public, big tech would not be able to compete against other tech companies… wait.
Reform can also be seen as more people become vocal about the harmful effects of “surveillance capitalism:” perhaps then, executive mindsets will change. But until then, the government needs to step in to protect the consumer but also businesses that use data for good. Data by itself is not harmful; it’s the way it’s extracted and used that poses dangers to society.