Extract from “My Digital Footprint”, this is from the Chapter 5 “Web 2.0 and Mobile Web 2.0”
Mobile Web 2.0 – value lies in getting data out from a device
We have discussed Mobile Web 2.0 and Web 2.0 extensively above. We have seen that Web 2.0 could be viewed as harnessing collective intelligence and, by extension, Mobile Web 2.0 could be viewed as harnessing collective intelligence from mobile devices. This is depicted in Figure 15.
Figure 15 Moving focus to getting more data off a device than on to it
The ability to get data out of or off a mobile device lends itself to the unique advantage a mobile device has.
We explore this idea in greater detail in subsequent chapters.
Considering this concept, that there is more value in getting data off a mobile, let’s consider that sensors (acceleration, temperature, noise level) can easily be placed in or attached to mobiles. Further, a user can send information from their device, by voice, IM (Instant Message) or text, to a centralised service point. Both sensors and people can provide vital data during a disaster-relief operation or outbreak of a disease, for example, could not be gathered. It is known that aid agencies are building systems that use handsets to sense, monitor and even predict population movements, environmental hazards and public-health threats. InSTEDD (Innovative Support to Emergencies, Diseases and Disasters), a non-profit group based in California USA, focuses on the use of mobile-gathered data to improve developing countries’ ability to respond in emergencies. Funded with seed money from Google’s philanthropic arm, it has released a suite of open-source software to share, aggregate and analyse data from mobile phones. It is being used in anger in Cambodia, where health-workers can send an SMS, of observations and diagnoses, to a central number.
FootPath, a system developed by Path Intelligence (UK), aggregates and analyses signals picked up from mobiles as people move through a particular area. The data can be used to optimise the flow of pedestrians through high density areas, such as railway stations and airports. Data can determine, for example, whether customers visit a specific shop. This will be linked to marketing at some point to close the loop.
Dr Alex Pentland, at MIT, describes ‘X-raying entire organisations, cities and countries’ by collecting data in the two ways described: passive (no user intervention) and active (user interaction). Dr Pentland’s algorithms can already cluster information from thousands of mobiles and divide people into ‘tribes’ of like-minded folk. He calls this ‘reality mining’, something I explore later as the ‘rainbow of trust’. Dr Pentland’s company, Sense Networks, is working with Vodafone and other collaborators to build an early-warning system for modelling and predicting the spread of tuberculosis in South Africa.
There is more value in getting data off the mobile!
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