Why monetization of transaction data is not always worth the risk

Why monetization of transaction data is not always worth the risk

Why monetization of transaction data is not always worth the risk
Big data is big business. Take, for example, the type of data that credit card companies have at their disposal. Access to Visa’s database would essentially provide a real-time window on the entire economy.

This raises interesting questions about how, when and if certain data should be used. Visa makes billions and billions from its core business and while it could doubtless make more money from its data, it’s probably not worth the risk.

Tammer Kamel founder and CEO of Quandl, a technology company that provides a plethora of data sets to financial institutions, said Visa is rightly paranoid about the sort of headlines that imprudent monetization of transaction data could generate.

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“Imagine a headline saying: ‘Visa selling your information to greedy hedge funds on Wall Street.’ They know there is money to be made by extracting insights from the data, but the risk/return profile is scary. Sure they can maybe make an extra $50 million, $100 million or even half a billion dollars, but that’s still small compared to their core revenue and going through that process puts their reputation at risk. This is something these guys are wrestling with right now.”

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Quandl has over 100,000 users, including lots of data scientists from banks and hedge funds, and over the years Kamel has gathered plenty of insight into the data business. He also pointed out that card providers and payment processors are paid a lot of money by major retail outfits like Walmart. If card providers were to turn around and start letting the outside world infer how sales were going at Walmart that would be a huge betrayal.

“Wall Street would love to see all the credit card transactions that are happening at Walmart and work out how Walmart is doing. But if Visa ever released that, Walmart would be livid,” he said.

Quandl provides well-structured data, the sort analysts like, via its API, so it’s available decoupled from other software or hardware such as Bloomberg terminals. This means users can easily run their own machine learning analytics on it. Quandl’s data sets feature everything from alcohol consumption per head of capita in Albania to building control permissions across the US.

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“We have a big data set that is essentially all the building permit data in the US, broken down by regions and cities and counties, and the sort of building permits people are applying for – is it residential, is it commercial, are they putting in elevators, are they putting in pools.

“A nice, big, hairy data set, and if you analyse it you can learn all kinds of interesting things about what’s happening in the economy right now, because of course construction is a very important part of the economy.

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