Episode 6: Making Sensor Data Valuable and Actionable at Scale
Series: Making Sense of Sensor Data
Phil Ressler: We are headed to a world of trillions of sensors. It’s already in the hundreds of millions and sensors are getting progressively cheaper. They’re getting smarter.
Elizabeth Shonnard: You start to get to look at scale in a different way than just traditionally scaling a cloud platform.
The value of data in general comes from what it’s used for. It’s about making it classifiable and actionable.
Phil Ressler: The role of artificial intelligence and the role of machine learning are going to be progressively invasive. But none of that matters, if you’re not able to cope with the scale and the velocity of data. So we start there.
Elizabeth Shonnard: This is where Edge processing will come into play.
It’s all very configurable, so that you can handle this scale problem with all your available resources and not just sending everything constantly to the cloud.
Phil Ressler: The difference with sensor data is that it is time-series data. It’s not structured, record-oriented data.
The meaning of the data is not only in the initial data points themselves, but also in the trend line associated with seeing what that device is recording over a period of time.
Shawn Gunn: Many companies have made decisions on the analytic side but the data that feeds analytics is equally as important.
And so that’s where Sixgill sense helps to bring in very granular actionable data that can feed the power of many of these analytics platforms.
Phil Ressler: We can take contextual, record-oriented data that’s necessary to inform an automated logic, and separate that from the time-series data that is going to be directly acted on.
Shawn Gunn: We have gone into companies such as Amazon and Azure and Google Cloud, and started to look at integrating our services directly on the Edge of those clouds.
We can capture all the data that’s coming from these billions of devices as it reaches that funnel.
The service that we’re providing not only aggregates data but we do cleansing and normalization of it.
Phil Ressler: We’re going to make the system progressively more intelligent. We’re going to make it progressively more secure. We’re going to do all the things that you have to do on the back end to make it something that developers can forget about and just simply adopt and rely on.
Elizabeth Shonnard: 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. It’s going to handle all of the data problems that you’ll have if you aren’t thinking about that ahead of time.