Adding the “Where” to the “When”
Today’s world isn’t necessarily filled with big data, rather it’s filled with a lot of data. The primary challenge to understand and making use of this copious amounts of data is creating context. Two constants exist that naturally bring context to data: time (the “when”) and location (the “where”).

Point Inside allows you to look at IOT data through the lense of location.  Sensors can tell us temperature, state (e.g., lights on/off), status (e.g., occupancy), noise, etcetera.  But the sheer mass of data becomes difficult to comprehend unless context is applied.  For instance, noise over time might be interesting.  But noise at a location at a point in time provides even more context.  It’s now possible to detect distinct noise signals (gunshots) at a location to trigger police dispatch.

Are you listening to your venue?

Point Inside delivers a system to capture your IoT data signals and bring them together at a location and display them on an indoor map.  In an airport, this may help you to predict when to dispatch security assets.  In a mall, you could compare shopper traffic flow to sales or even learn how weather impacts indoor behaviors.