In May 2017, The Economist published an article entitled, “The world’s most valuable resource is no longer oil, but data.” A jolting concept but one that had already surfaced back in 2006.
The quote, “Data is the new oil.” is credited to a mathematician, Clive Humby, who highlighted the fact that, although inherently valuable, data needs processing, just as oil needs refining before its true value can be unlocked.
Since Humby planted that thought, data capture and manipulation have evolved rapidly and have continued to spark debates and conversations amongst economists and tech geeks around the world. Most have agreed on the following comparisons:
· Data is an essential resource that powers the information economy in much the same way that oil-fueled the industrial economy.
· Oil is a finite and scarce resource. Data availability is infinite and isn’t just abundant but is also a cumulative resource.
· Oil requires huge amounts of resources to be transported to where it is needed. Data, on the other hand, can be replicated and moved around the world instantaneously, at very low cost, through fibre-optic networks.
· Data becomes more useful the more it is used, rather than its energy being lost as heat or light, as when oil is processed. Once processed, data reveals further applications. For example, medical data collected from a pool of patients can help a doctor diagnose and treat an individual patient. Thereafter, it can be anonymised and fed into machine learning systems to generate broader insights that can benefit more people.
· The life cycle of oil is defined by process: extraction, refining, distribution. The life cycle of data is defined by relationships: with other data, within the context and with itself via feedback mechanisms.
The value of data was never really apparent when the World Wide Web surfaced in 1989. Then, the web was originally conceived and developed to meet the demand for instant information-sharing between scientists in universities and institutes around the world. The miracle that is Google was launched almost a decade later in 1998. Social media platforms like Facebook followed in 2004.
By the time other social media platforms like Instagram were launched in 2010, eCommerce platforms like Amazon and eBay (which launched around 1995), were already refining their cyber business models and cottoning on to the idea of data collection and analytics.
The term “data science” was only coined in 2001, and by 2010 was acknowledged as a burgeoning profession and academic discipline.
The data genie is out of the bottle...
The difference between data and information.
The “Silicon Valley business model” is a stroke of genius. Provide services for free, in exchange for a customer’s data. That data is then used to create a profile of that customer, which is sold on to retailers as opportunities to send targeted and very niche advertising.
Google is classified as an Internet or web browsing company, but in essence, it is a company that makes its money from advertising.
We now know that to dovetail this business model, social media need to ensure “eyeball time” on their platforms – ie: keep the person scrolling for as long as possible on one platform. Our addiction (we’re all digital addicts) has therefore been cunningly engineered. It’s no coincidence that the most talented designers who design casino slot machines are being poached by tech companies to design apps and operating systems. These are people who understand the lure and effect that flashing lights, strobing colour and haptics have on our brains. The more we stay glued and engaged on a platform, the more data we give away. And we now also know that it is not only the primary tech company we cede our data to but multiple third parties that buy that information. What we don’t know is how long that value chain has become.
The Battle Lines are Drawn
FAANG is an acronym for the market's five most popular and best-performing tech stocks, namely Facebook, Apple, Amazon, Netflix and Alphabet's Google. Unsurprisingly, these are the very same companies at the epicenter of the storm brewing around issues of data privacy and ownership.
The U.S. Department of justice (DoJ) has embarked on an investigation into the big tech companies. Its mandate is to “address ‘widespread concerns’ around social media, search engines and online retail services and whether their actions have harmed consumers”. This has come about after pressure from European regulators and lawmakers (who have more stringent regulations around data privacy) who felt that America was not only falling behind, but also that the American tech giants were using their customer information to stifle competition.
In The Economist’s feature, the connection between data and anti-competitive conduct by the tech companies was explained:
The “Silicon Valley business model” is a stroke of genius.
“The giants’ surveillance systems span the entire economy: Google can see what people search for, Facebook what they share, Amazon what they buy. They own app stores and operating systems and rent out computing power to startups. They have a “God’s eye view” of activities in their own markets and beyond. They can see when a new product or service gains traction, allowing them to copy it or simply buy the upstart before it becomes too great a threat. Many think Facebook’s $22bn purchase in 2014 of WhatsApp, a messaging app with fewer than 60 employees, falls into this category of “shoot-out acquisitions” that eliminate potential rivals. By providing barriers to entry and early-warning systems, data can stifle competition”.
It's no surprise, then, that the threats to “break up the tech companies” have been brewing for some time now. Some economists are comparing this discussion to the same argument made in banking a decade ago, when regulators missed the opportunity to break up the Wall Street giants, who were just shrugging off the multibillion-dollar fines thrown at them
History is repeating itself already. In July Facebook was fined $5 billion for privacy violations by the US Federal Trade Commission. While this was the largest civil penalty in the regulator’s history, it seemed that Facebook simply dipped into its capacious pocket and sniffed at the fine. A few days before the Facebook ruling, the FTC also slapped Equifax (the credit reporting agency) with a fine of $800 million. Across the Atlantic the UK Information Commissioner fined British Airways £187 million and the US Marriot hotel group £99 million, both for failing to secure customer data.
Ownership and Digital Property Rights
But while regulators are flexing their collective muscles on data privacy, there is a pincer movement afoot to provide consumers with their own armory against tech companies: the right for individual consumers to sue a tech company when they breach data security policies.
In California, amendment SB 561, allows consumers to sue major tech companies under the California Consumer Privacy Act (CCPA), which was passed unanimously by the state’s legislature in June 2018. California was the first state in America to give residents legal authority over their online data. Under the CCPA, companies are required to disclose data collection and use practices, as well as give consumers the right to opt-out of the selling or sharing of their personal information. Consumers also have the right to request their data be deleted.
The giants’ surveillance systems span the entire economy...
This year, California Governor, Gavin Newsom, is going one step further. He and state lawmakers have started work on an ambitious and contentious policy: viewing the data we all create as “labour” and putting a financial value to that process, which they are calling a “data dividend”. A “data dividend” would be a payment that businesses would make to the state or to consumers if their personal data are sold.
“I think most people recognize that there is something unsustainable going on here, where we are concentrating more and more power and wealth in a few companies, and it is not normal power. It is power over the nature of our democracy, over our families, over our personal identities,” says computer scientist and author Jaron Lanier, who has been assisting state lawmakers to discuss potential models for a data dividend.
“The concept shouldn’t be misunderstood as ‘anti-tech,’” he added. “A ‘data dividend’ could allow the governor and lawmakers to rethink the economy and measure the invisible labour of people, whether it’s posting content to social media platforms or contributing personal information useful in coding new tech tools”.
The concept of ‘data as labour’ brings the argument back to the beneficiation of data.
Are we, the consumers, the labour force creating the data, or are the tech companies the ones putting the real effort and resources into converting the data into valuable information? They have, after all, provided a service which we have “willingly” agreed to, and are using.
If we started from scratch, and had to pay to join these online platforms, with a guarantee of data privacy, rather than a freemium model that involved a barter exchange, which would we choose? It’s a Catch 22 and borderline rhetorical question, since the horse has bolted. The data genie is out of the bottle and the battle lines we’re working with, started out as blurred.
It is going to be a very complicated and protracted war. But a war that must be fought.
Data brokers and data scientists: booming industry and new careers
- Data brokers are companies which collect information from public records, online activity, and purchase history and re-sell it to other companies for marketing purposes. The more companies know about consumers and their online activities, the more targeted (and successful) they can make their advertisements. But data needs to be transformed into useful information, so brokers need data scientists.
- A data scientist knows how to extract meaning from and interpret data, which requires understanding and navigating the data landscape: everything from statistics and machine learning to pre-empting human behaviour. Data scientists are increasingly sought after because they spend all their time collecting, cleaning, and munging data, because data is never clean. But it is always valuable.
Pull quote box: How to ensure more personal data privacy.
More and more people are taking measures to prevent their data being taken and traded. Here are some of the more popular platforms being used:
· Messaging service: Signal – They guarantee that all your calls and messages are end-to-end encrypted and painstakingly engineered to keep your communication safe.
· VPN: virtual private network - Allows you to create a secure connection to another network over the Internet. VPNs can be used to access region-restricted websites, shield your browsing activity from prying eyes on public Wi-Fi, and more.