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Episode 3: Dealing With The Sensor Data Explosion

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Episode 3: Dealing With The Sensor Data Explosion
Series: Making Sense of Sensor Data

Phil Ressler: 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.

You talk to people in the marketplace. They have ecosystems that are generating sensor data and they’re not collecting it, in any holistic way.

Or, they are collecting data and putting it into siloed applications, and they don’t have a holistic view of what that operating ecosystem is like.

Elizabeth Shonnard: 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 the translation layer from the sensor data into the platform, but then you’re also handling how to normalize that data with other types of stream data at the same time.

Phil Ressler: 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 and 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, essentially just the first time so that we can map what it is. And then after that the system remembers that and is smart enough to be able to differentiate the various data types and normalize, classify, and unify them.

Elizabeth Shonnard: 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.

Phil Ressler: 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 level, and at the trillions level that we’re going to have over the next 15 or 20 years.

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 at breakneck speed, if you wait until it is an overwhelming proposition, then you can’t ever catch up.