The MIT Review’s selection of “10 Breakthrough Technologies of 2013” included Deep Learning and as a segue to another current area of mobile cloud excitement: big data from cheap phones.
The title: “Big Data From Cheap Phones” would seem to imply somewhat of a recognition of our concept of the edge of a mobile cloud-centric universe “folding in” on itself.
This selection by MIT barely touches on the implications of the evolution that we forecast for the mobile cloud. (See our study: “The Future Of The Mobile Cloud”.) It does however point out the role that devices, from smart to dumb, play in the world of big data, highlighting, for example, the rich RAN data that exists as network providers keep track of the devices on their networks.
An even more potent example has recently emerged of a business based on mobile-derived “hyperdata” using local-based information. We refer to Premise.
Premise’s founder David Soloff, began with a smartphone application, using the smartphone as a data collection device. The company seeks to use a “global network to track macroeconomic and human development trends in real time.”
In this case the phone with its photo, GPS and messaging capabilities becomes a hyperdata collection device among his army of global independent users for, among other things, a price-inflation indexing data set. The company claims to maintain local teams in 25 cities (supplemented by online shoppers in 30 countries) and collects millions of data points per day from its local sources.
With big data processing in the cloud, this leads to a business of providing big data to a global market of local or national entities based on information collected by smartphones. The company maintains “live indices” of price changes in the markets it monitors and offers maps analyzing potential food crisis points.
The company collects raw data for baskets of pricing information to track inflation around the world. It also tracks issues such as competitive dynamics and food security.
A fascinating element of the Premise business model is utilization of the mobile cloud to vastly facilitate the ability of small edge users to collect discrete raw data points in order to gain “big data” advantages focused on local market issues.
Field data acquisition using mobile devices is gaining some traction commercially. One example is the Orbit product from Schneider Electric. Orbit is an app for field workers to gather information, take pictures and transmit the information, including audio regarding the status of equipment. This mobile app currently runs on Windows 7, Windows 8, and iPad. A smartphone app is also reportedly in the works. The app is hosted in the Azure cloud.
It should be noted that, while one might assume, according to industry hype, that M2M installations are doing such monitoring, anyone familiar with the power or energy industries knows that that day has not arrived and human onsite data gathering is still necessary. The smartphone makes the data gathering extremely cost effective, with its multiplicity of capabilities including LBS, camera, barcode reading, voice recording – with its ubiquity and low marginal cost. We expect this approach to creep into businesses in a virtual stealth mode on a wide scale. Every one of these apps is mobile cloud-based.