They won’t have to rely only on data based on what we did; through biometric analysis companies will know how we felt about a product or service. That is, get inside our head.

In his book, 21 Lessons for the 21st Century, Israeli historian, Yuval Noah Harari, writes about the coming intersection of biometric data and algorithms as engineers develop software that detects human emotions based on movements of eye and facial muscles.

Harari writes: “Add a good camera to the television and such software will know which scenes made us laugh, which scenes made us cry and which scenes bored us. Next, connect the algorithm to biometric sensors and the algorithm will know how each frame has influenced our heart rate, our blood pressure and our brain activity.”

As Harari notes, companies could use this technology to know our emotions better than we do.

Then there’s the Internet of Things. As billions of devices are connected, companies will know when you turn on lights, wash clothes and how long your bread is toasted. Fully or partially self-driving vehicles in the next decade or two will take data-capture to a new level.

But do not read this blog as another Big Brother warning or whinge about privacy invasion. Data collection trends are unstoppable. Used sensibly, the potential of real-time data is safer roads, smarter energy use, lower costs and greater convenience.

My concerns is the allocation of data value between business and consumers. We give our data to Siri and Google Maps and these applications make our life easier. We let retailers use our data through membership programs because we get a discount or other benefit.

What happens when this trade-off is too slanted in industry’s favour? As business collects more data, the benefits we receive could have diminishing marginal utility. Google Maps, for example, becomes less valuable because so much of the developed world has been mapped.

Or the small discount you get from being part of a retail database is not worth the bombardment of tailored marketing offers that swamp your email. The streaming TV service is great until you realise it is analysing and making money off your facial expressions.

At some point, consumers will ask if they are getting sufficient value from their data.

Or, if part of our job in the New Machine Age is to feed data to software algorithms, should that data provision be a paid job?


This idea is not as crazy as it sounds. Eric A. Posner and E. Glen Weyl in Radical Markets proposed that data provision should be treated as paid labour – an idea that would create “data workers” and, perhaps, help labour markets transition to the New Machine Age.

Surely one’s voice data and facial expression is their intellectual property. Allowing Netflix, Amazon or another retailer to scan your biometric data – information they might commercialise through marketing or product development – could attract a small copyright payment.

If this idea took hold, “data workers” could receive a small passive income stream from data collectors that contributed to a form of universal basic income. Who knows?

As I see it, there are four problems with this idea. First, we don’t know how much our data is worth. If you provide thousands of data points for industry each day, is that worth 1 cent, 10 cents, $1 or more. Is the data worthless on its own and only valuable in a “crowd”.

Should providing data to personal assistants or mapping tools be a paid job?

Should providing data to personal assistants or mapping tools be a paid job?Credit:AP

Second, we don’t measure data-collection volumes. How much data do you provide industry daily and how much data does a company keep on you and for how long? Data volume and price are needed to gauge value.

Third, what is the best mechanism to collect payment? Companies paying a tiny data fee to millions of customers each month makes no sense. Nor does companies deducting an amount off a good’s price to account for data value. Industry would game the system.

Governments could introduce a data tax and redistribute the proceedings to communities through benefit programs or spending initiatives. But that too is fraught with problems.

The fourth problem is industry resistance. Paying for customer data is hugely disruptive to their business models. Right now they benefit from the greatest asset there is – personal information – without paying for it or showing what they provide in return.

I don’t know the answer to these issues. What is obvious is that data has great value – why else would there be a trillion-dollar cybercrime industry? And why are companies falling over themselves to collect and analyse your data.

So I ask again, why do we do give away such a valuable asset to big business?

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Tony Featherstone writes on Personal Finance specialising in Superannuation & SMSFs, Specialist Investments.

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