Big Data Assets series – Geo-locating Data
It’s actually not about having a bunch of GPS traces or any other way of geo-location data as a separate source, it’s rather geo-locating all available data sources to increase their value.
Knowing “where” a given piece of data has been created is going to be the key of future applications, where the context of the user generating data becomes more relevant. So far the “when” question has been successfully addressed by the inclusion of the well-known time-stamps, which is not a hard one, as most of the systems registering the single data pieces runs a clock-based CPU.
The great Spanish philisopher José Ortega y Gasset coined in 1914 the famous phrase “I am I and my circumstance”, indicating that the human being cannot be detached from the world… Applied to the data, the real value comes with the contextual information attached to a piece of data.
Something that started in the social media world as a way of overcoming the pain of manually entering the place name, where an user generated media content was produced (like a picture, a video, etc), became more and more popular with the advent of GPS-enable devices. You could for example not only tag your picture with “Madrid, Spain”, but also provide the coordinates of this Gran Vía Street corner you saw Tom Cruise surrounded by crazy Spanish fans after the “Mission: Impossible II” Spanish movie premiere.
Seeing the value added and the potential, almost all social networks (Twitter, Facebook, G+, Flickr) embraced the geo-location of their posts, twits, comments, updates and any way of user generated content.
New ways of making business relying on this capability emerged and conquered peu-à-peu every single corner of our lives: services locators like (cab finder, etc), on-site recommendation engines (finding the most appropriate locations in the surroundings for a given user based on her preferences), location-based advertising, and an a very long etcetera.
But what do they have in common? Leveraging information about the particular circumstances of an user –the when and where-, they act as a filter to remove all the information that’s likely to be not- relevant for the user. As simple as that!
But Big Data is intended to take this relevance based filtering to the next level and to exploit the usage of geo-located information in additional ways.
(This post is part of the Big Data Assets series, have a look!)