Understanding Machine to Machine Analytics

Machine to Machine Analytics is a growing industry, with some estimates pegging it to grow to as much as fifteen billion dollars in the next 5 years. What exactly is machine-to-machine analytics? And what changes or impact will it have on the data mining and analytics industry?

Machine-to-machine analytics refers to analytics on data generated by machines – for example, utility sensors, fitness trackers, aeroplane machine sensors, smartphone location trackers and so on. There is a tremendous amount of data being generated by these machines, and there are multiple technologies and tools being developed to store, manage, process, and analyse data generated by what is called “The internet of things”

How is it different from the data that we are used to dealing with? Very often, the data used for business analytics is “human-generated data”: for example, a customer goes into a store and buys 5 products. The transaction database logs the choices made – the products selected, the prices of the products selected by the customer etc. Similarly, when collecting data on default rates, or survey responses or direct mail response rates, coupon redemption rates, plan upgrades etc, all the data that is being collected is human generated.

Machine-generated data, in contrast, is data that is produced entirely by machines – for example, RFID data, weather satellite data, or medical device data. Some of this data may be of human observation – for example, fitness trackers, or web server data, but it is still generated entirely by machines.

What is new with machine-generated data? Machines have been generating data since the earliest machines were built during the industrial revolution, but what has changed in the past couple of years is both the level of remote control of machines that they can now be started, stopped, and upgraded remotely, as well as the level of interaction with other machines and humans which is now much more prevalent and pervasive.

Of course, this is still data, and data needs to be processed and analysed to help business work with data driven decision-making processes. There are some common elements with traditional data analytics, but also differences as well when it comes to dealing with machine to machine data

The common challenges include making sense of complex data patterns and interactions, and using historical data to make robust future predictions. As we collect more and more data, at a faster rate, the data analysis process will require more complex and intensive analytical algorithms to be applied to the data to generate business value

But there are also some very new and specific challenges that are coming up when dealing with machine-to-machine data. One challenge is around the volume of data – Big Data. Machines generate vast amounts of data – for example, airline engine data by some estimates is 2.5 billion terabytes of data a year. So a lot of Big Data processing and storage challenges will be applicable to data generated by machines. Another challenge with machine-to-machine data is that very often the analytics insights and recommendations that are required from this type of data tend to be real-time or almost real-time. For example, using a smartphone’s GPS data to provide customized location-based discount offers to customers. Or managing and providing real-time solutions to server performance issues.

Currently, the data that is being generated and managed by Big Data systems are mostly from Clickstream data and text data, followed by machine-to-machine data. But very soon in the near future, machine-to-machine will be the largest source of data being generated and processed and will need specialized skills to manage and mine this data for insights and business strategy. Machine-to-machine analytics has the potential to revolutionize our way of life, with the capability of personalizing all our customer experiences to a much larger degree. Businesses need to be investing in machine-to-machine storage and analytics systems and resources to retain competitive advantage.

Interesting Articles:
Big Data Inno-Lytics
Are we all prepared for the ‘Internet of Things’?
Big Data, Analytics and Mobile: The Future of Digital Tech.

Interested in learning about other Analytics and Big Data tools and techniques? Click on our course links and explore more.
Jigsaw’s Data Science with SAS Course – click here.
Jigsaw’s Data Science with R Course – click here.
Jigsaw’s Big Data Course – click here.

Related Articles

} }
Request Callback