[00:00:10] When you’re taking in data from a wide variety of data types that data is going to look different depending what it comes from if you talk to people in the marketplace. They have ecosystems that are generating sensor data and are not collecting it in any holistic way or they are collecting data and putting it into siloed applications. They don’t have a holistic view of what that operating ecosystem is like.
[00:00:32] Say I have a farm and I have a bunch of temperature humidity sensors everywhere. If I want to add weather data into the stream and be able to trigger rules on a combination of those two types of data you’re not only handling that translation layer from the sensor data into the platform. But then you’re handling how to normalize that data with other types of stream data at the same time.
[00:00:58] There is a challenge in collecting the data in a uniform way, making it available to a variety of applications in a uniform way, being able to unify the storage, the normalization, the classification, get a common understanding and prepare the data, do the pre-analytics necessary so that true analytics can efficiently draw conclusions from that data. How do we do that? Well, Sixgill sense is sensor data agnostic we need to understand the data that’s being delivered by a sensor the first time so that we can map what it is.
[00:01:32] And after that the system remembers that and is smart enough to be able to differentiate the various data types and normalize and classify unify them.
[00:01:40] Sixgill helps companies meet the challenge of IoT and connecting diverse sensors. Because we’ve built out this sensor data agnostic platform from the start we take in all of that data we connect these sensors in a scalable maintainable way and we make it accessible for business cases.
[00:02:01] There’s an opportunity to take in data build a record find the exception events determine what’s actionable. Automate the responses and do that at a scale that can function at the billions of levels and the trillions and level that we’re going to have over the next 15 or 20 years.
[00:02:19] The opportunities presented primarily by the continued explosion of sensors are really twofold, one is determining where you can put the sensors and what information is required and in order to support what decisions. On the other side of the equation a whole slew of interesting challenges. Who owns the data. How does it get distributed? Can you sell it? It creates an immense number of opportunities. There are all sorts of interesting challenges associated with them as well.
[00:02:42] We are headed to a world of trillions of sensors. Smarter sensors getting progressively cheaper can generate lots of data and with the entire world essentially automating breakneck speed. If you wait until it is an overwhelming proposition then you can’t ever catch up.