Big Data Assets series – Machine-2-Machine Data
We all are used to the Man-2-Machine communication, actually because almost all of us carry it in form of smartphone in our pockets.
But in the so called internet of things, the machine 2 machine interaction is the cornerstone. The idea of vehicles sending and receiving messages from other vehicles to warn about an accident or describe a traffic congestion a couple of miles away is no longer something we need to wait decades to see!
Technologies like smart metering are already not only changing the way we consume energy to lower costs and protect the environment, but also tracking the consumption patterns of households all over the world. For example, the so called TV pickups can be analyzed to create models that predict and handle such situations.
Another emerging trend is the building of tracking systems relying on RFID technology, Radio Frequency IDentification chips that enable, if properly equiped, the geo-localization of chips over the time. A part from the logistic companies, where this technology belongs to the daily routine, RFID is nowadays conquering our lives… the so called E-passport or biometric passport, the micro-chip implants to track for example sheeps or pets, etc… or books from libraries. More sophisticated uses have been pursued by retailers, to track the shopping cart movements in a store, which generates insights to optimize something as crucial as the placement of the goods in a shop.
An evolution of RFID can be found again in our pockets: NFC, Near Field Communication, a two ways devices communication, on which for example the e-wallet and smartphone payment capability rely. The beauty of NFC is its simplicity, ideal for contactless payments, to pass pictures from a device to another one, or to complete the pairing and set up processes to enable richer and more sophisticated ways of exchanging information, like Bluetooth or WiFi.
What these M2M implementation technologies have in common is exactly what makes them so interesting for us: the enablement of non-stop time-based data gathering.
But what could you do with this data? Well let me tell you one example…
Let’s say your start-up idea is digitalizing the ordering process in restaurants.
Your company provides tablets where people can check the menu and submit their orders (of course, it has already been invented).
Let’s say you collect this data centrally for all the restaurants in the city… so you could know what’s been ordered when and where.
Let’s say you can break down the dishes to components (like salmo, or beans, or even the different spices).
You can create a model to explain the taste in the city, identify new restoration opportunities, create collective filtering based recommendations -what’s hot today, dish of the month, happy hour specials, etc-…
In other words, you could have a weapon the restaurant managers would kill for.
And it was just an example… don’t you see the possibilities?
(This post is part of the Big Data Assets series, discover the other ones!)