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.