Mobile Big Data: Useful or Simply An Invasion of Privacy? | DMA

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Mobile Big Data: Useful or Simply An Invasion of Privacy?

Imagine if your transport app could not just tell you the time of the next bus, but whether it was full or not? What if you knew the bar you are about to head to was packed to the gills? A UK-based company is hoping to address the problem by tracking mobile signals through pubic WiFi networks. By using a Big Data approach, they can identify exactly where people are. The company, Presence Orb, is installing its software in an increasing number of public WiFi installations. It monitors the signals from phones searching for hotspots and from this, can bring additional contextual information, to see how many people are there.

It’s not the first time that Presence Orb have attempted such a service. Last year theyactivated recycling bins in the City of London to anonymously track the movement, speed and direction of mobile users in the area. This data would have been made available to help advertisers refine their offering . Although the only data taken was the mac address, and therefore anonymous, there were accusations of Minority Report-type activity. The service was withdrawn as a result.

By aggregating the connection data, the new system provides a greater level of anonymity. It is arguably therefore less intrusive than cookies that track users from websites. That aside, the fact remains that privacy is as much about user perception as the activity itself. The introduction of the cookies regulations, for example, highlighted this kind of tracking activity to the public at large. It also gives options to opt-out to some extent – although if you want to know how hard it is to avoid cookies, then read this article about a woman who tried to hide her pregnancy from the internet. The problem with WiFi based tracking is that users are likely unaware of the activity and have no choice to opt-out. Mobile is a personal unshared device and even when data is taken anonymously and aggregated, brands need to be very sensitive to how users perceive it.

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