Big Data is fuelling the AI revolution
Written and accurate as at: Jun 13, 2018 Current Stats & Facts
Big Data is fuelling the AI revolution
Strava is an app that athletes use to track, share and compare their exertions. The US company, for its part, can analyse the data generated by its users. For no particular reason, Strava recently hosted a ‘heat map’ of three trillion location points it had collected from one billion activities. What could go wrong? Just that it created one of the largest security breaches of recent years.
People used the data trails of folks exercising in remote areas to deduce the location of secret military bases and covert military activities across the world. The error of Strava’s military users was to fail to ‘opt out’ of the default setting that shared their data.
The Strava saga highlights contentious issues surrounding ‘Big Data’, a term that describes the volunteered and observed information collected from internet users and connected objects. The issues include privacy, ownership, the privileges and responsibilities of data-gatherers and the ignorance surrounding Big Data’s characteristics.
From an overarching perspective, the economic traits of Big Data, privacy issues and the lack of clear property rights around this core ingredient of the artificial-intelligence revolution prevent the growth of markets that would steer the data in an efficient and timely way to where it could generate the most benefit for the economy and society. The challenge for policymakers is to define ownership and encourage data markets while minimising the risks that Big Data can pose.
It must be said that the privately owned data-gatherers are conscious of their responsibilities to protect the data that is only valuable because these businesses found a way to commercialise it. The commercial imperative is such, however, that the more data that is gathered the more incentives the accumulators have to profit from that information. While artificial intelligence embeds itself further into everyday living, demand for data will only grow, and its controversial aspects and market failures will demand resolution. But it won’t be easy. Big Data is different.
Question of ownership
The data-generated wealth of Big Tech has prompted many people to describe Big Data as the ‘new oil’. That comparison, however, doesn’t withstand scrutiny.
Oil, like any commodity, is indistinguishable across producers. Oil’s supply is finite and it is single use. Oil’s worth comes from the difficulty and expense of finding and extracting the substance from the earth. Ownership is usually clear-cut. These features mean that oil is easy to price and trade.
Data, on the other hand, is not a commodity because each dataset is unique. Its supply is infinite. Data is easy to gather, simple to copy and can be reused and multipurposed. The ownership of data is contentious. Privacy concerns restrict data’s change of ownership and possible uses, and install security obligations on holders. Buyers are uncertain as to the worth of any dataset. These features make data difficult to price and trade.
One of the most pressing controversies surrounding Big Data is that Big Tech is generating massive gains from Big Data it collects and, by default, controls, and not providing enough in return to users or society.
Europe’s response is to make laws that keep the ownership and control of Big Data with the subjects. Australia intends to pass a similar law.
Consumer-slanted data-ownership laws could help people place a value on their data while easing concerns about privacy violations. Such laws, however, usually prove problematic. The laws only cover a portion of the data collected. Compliance can be costly. People are often too complacent to reclaim their data ownership. And even with portable data, entrants to markets will find it hard to dislodge incumbent companies cemented in stranglehold positions by network effects.
As the nature of Big Data embeds it with complexities surrounding privacy and ownership among other issues, society will take a while to work out how to gain the most out of Big Data and minimise Strava-style hiccups.
By Michael Collins, Investment Specialist, Magellan Asset Management