The rate of data is accelerating rapidly.

The amount of data that we’re producing, handling, and exchanging has increased at an exponential rate. 

The extraordinary amount of data requires an extraordinary solution!

We recently sat down with Josh Cooper and Greg Caltabiano to discuss AtomBeam’s solution to the exponential increase in the amount of data being created and stored. 

90% of the world’s data has been created in the last two years.

The volume of data is increasing at an astonishing rate, and as you can imagine, we’re expecting to see it double every two years.

This tremendous amount of data comes from two sources, one being the smartphone revolution, and the second being the IoT (Internet of Things).

We live in a world where there are roughly 8 billion people. There are over 6 billion smartphones, all of which use applications that send and receive data from the cloud. All of this data needs to be created, stored, transmitted and processed.” 

“IoT has also exploded in size, and data is being used in just about everything you can imagine, including:”

  • Smart thermostats and smart doorbells
  • Cars and trucks
  • Airplanes
  • Trains
  • Computer

In Greg’s words, “data would not be created at this rate if there wasn’t a good use for it, and this is where artificial intelligence and machine learning comes in”.

The AtomBeam Solution

How are we handling all of this data?

How is our data processing and data communications infrastructure handling this?

Fortunately, we’ve had a lot of technical innovation with our groundbreaking technology.

Josh states “in fact, the basic idea that underlies the whole technology is an entirely straightforward one, mainly abbreviation”.

We match patterns in your data to generate patterns in your messaging.

Those patterns are then used to abbreviate common and repetitive messages so the amount of data sent is less than it would’ve been.

This is where machine learning comes into play. We’ve created a new paradigm in computing which allows us to solve the data problem with better performance than traditional approaches.

We’re just getting started – bringing that paradigm into an even wider range of real-world data problems.