Will MasterCard Sell Big Data?

MasterCard Inc, the world’s second-largest credit card association, sees business booming from selling data to retailers, banks and governments on spending patterns found in the payments it processes, a top executive told Reuters.

MasterCard, which handles payments for 2 billion cardholders and tens of millions of merchants, uses that information to generate real-time data on consumer trends, available more quickly that regular government statistics.

“It is an incredibly fast growing area for us,” Ann Cairns, who heads MasterCard’s business outside North America, said in an interview, stressing that the company respects cardholder privacy, using anonymous data rather than personal information.

MasterCard does not give figures for its information services products but “other revenues”, which include the sale of data, grew 22 percent in the first quarter of 2014 to $341 million, outpacing the growth of total revenue dominated by payments processing, which rose 14 percent to $2.177 billion.

Cairns said clients for the data include retailers, banks and governments, with MasterCard tailoring it to their needs.

“Retailers are fantastic at using the data they have available about how people shop in their store, how their inventory turns over, but what they don’t know is what happens outside their store,” she said. “The data we’ve got is ubiquitous across the whole market. We can help retailers see what they need to do to capture more sales.”

Cairns, 57, a statistician by training who joined MasterCard in 2011 after helping manage the disposal of Lehman Brothers assets in Europe, revels in the insights real-time card data can provide, such as London’s popularity as the world’s top travel destination and a rise in spending on experiences such as eating out or going on holiday rather than shopping in stores.

MasterCard has recorded a spike in spending in Brazil on groceries and a drop in spending on luxury goods as the price of food has risen ahead of the World Cup, she said, the kind of insight valued by companies such as Nike and Adidas that are hoping to sell $300 soccer boots during the competition.

While MasterCard expands in “big data”, Cairns sees no slowdown in its traditional business of processing payments, with plenty of potential for growth as 85 percent of consumer transactions are still made by cash or check.

“Moving money and doing it safely and securely is so deeply cared about by so many people around the world that it will be a business that has fantastic value now and for years to come,” said Cairns, who previously worked at Citigroup and ABN Amro.

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Will Computer Obtain Common Sense?

Even though it may appear PCs are getting dumbed down as we see constant images of cats playing the piano or dogs playing in the snow, one computer is doing the same and getting smarter and smarter.

A computer cluster running the so-called the Never Ending Image Learner at Carnegie Mellon University runs 24 hours a day, 7 days a week searching the Internet for images, studying them on its own and building a visual database. The process, scientists say, is giving the computer an increasing amount of common sense.

“Images are the best way to learn visual properties,” said Abhinav Gupta, assistant research professor in Carnegie Mellon’s Robotics Institute. “Images also include a lot of common sense information about the world. People learn this by themselves and, with [this program], we hope that computers will do so as well.”

The computers have been running the program since late July, analyzing some three million images. The system has identified 1,500 types of objects in half a million images and 1,200 types of scenes in hundreds of thousands of images, according to the university.

The program has connected the dots to learn 2,500 associations from thousands of instances.

Thanks to advances in computer vision that enable software to identify and label objects found in images and recognize colors, materials and positioning, the Carnegie Mellon cluster is better understanding the visual world with each image it analyzes.

The program also is set up to enable a computer to make common sense associations, like buildings are vertical instead of lying on their sides, people eat food, and cars are found on roads. All the things that people take for granted, the computers now are learning without being told.

“People don’t always know how or what to teach computers,” said Abhinav Shrivastava, a robotics Ph.D. student at CMU and a lead researcher on the program. “But humans are good at telling computers when they are wrong.”

He noted, for instance, that a human might need to tell the computer that pink isn’t just the name of a singer but also is the name of a color.

While previous computer scientists have tried to “teach” computers about different real-world associations, compiling structured data for them, the job has always been far too vast to tackle successfully. CMU noted that Facebook alone has more than 200 billion images.

The only way for computers to scan enough images to understand the visual world is to let them do it on their own.

“What we have learned in the last five to 10 years of computer vision research is that the more data you have, the better computer vision becomes,” Gupta said.

CMU’s computer learning program is supported by Google and the Office of Naval Research.

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