Sense Networks is making sense of real world location data

Intro

We know that a user’s spatio-temporal data (i.e. location based + time) can provide good insights into a users behavior. However, as we start to go beyond a particular individual and try to tie similarities across users so as to develop user “types” using various sources of spatio-temporal data the task can become exceedingly complex. This is where Sense Networks step in. They have built out a solution based on a proprietary MVE algorithm that can take in these multi-dimensional data and reduce it using distributed (Hadoop) technology to develop connections that can meaningfully be extracted from such large amounts of data.

What they offer

 Based on a presentation by Girish Rao, Product Manager from Sense Networks (at the NY Location Based Apps Meet-up), seemed like there were two key data domains that Sense Networks was focused on:

Location based data mining – Example – Cabsense NYC is an iPhone app that Sense Networks developed that identifies the best corner to stand on to catch a NYC cab. They derived this from the NYC’s Taxi and Limosine Commision’s (TLC) data from the Taxi’s GPS location (good thing most NYC cabs have GPS data).

User “type” identification – Based on data from the telcos (i.e. location based on tower, etc), Sense Networks has started to map out user profiles and as a by-product of that can help understand user preferences. Add to this financial data (if available), and then powerful profiles can be created which can clearly help segment the user populations. The advantages of this from an advertising perspective are obvious. Additionally, these profiles can also be used to make recommendations to the user. The possibilities are close to endless.

What is their approach

According to Girish Rao of RealSense Networks, his company wasn’t necessarily relying on user generated data about their preferences or locations (like foursquare.com). The key reason perhaps is what he alluded to, which was that they currently source most of their data from telecom carriers (they have user data by cell phone towers they have used, etc).  Each carrier’s data can run into the millions of users’ data and this means Sense Networks, unlike foursquare.com does not have to wait around to build a meaningful population of users to really start mining user behavior. They already have this data and with more data from the telecom companies, they could literally get meaningful data of millions of users overnight!

Conclusions

Although Girish didn’t get too much into the pricing model, it seemed more like they were eager to make this data available to the developers to leverage and they were looking to get the developer community engaged. So if you are a developer looking to leverage this data, reach out to them http://www.sensenetworks.com/

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