’Michael – we are bigger than US Steel“.
Over the holiday season, I said this to my friend Jeremy Geelan when I was comparing the Mobile industry to the IoT.
The term Internet of Things was coined by the British technologist Kevin Ashton in 1999, to describe a system where the Internet is connected to the physical world via ubiquitous sensors. Languishing depths of academia(at least here in Europe …) – IoT has it’s netscape moment early in 2014 when Google acquired Nest
Mobile is huge and has dominated the Tech landscape for the last decade.
But the Internet of Things(IoT) will be bigger.
Here are some numbers. Souce (adapted from David Wood blog )
By 2020, we are expected to have 50 billion connected devices
To put in context:
- The first commercial citywide cellular network was launched in Japan by NTT in 1979.
- The milestone of 1 billion mobile phone connections was reached in 2002.
- The 2 billion mobile phone connections milestone was reached in 2005.
- The 3 billion mobile phone connections milestone was reached in 2007.
- The 4 billion mobile phone connections milestone was reached in February 2009.
- We reached 7.2 billion active mobile connections 2014
So, 50 billion by 2020 is a massive number by a factor, and no one doubts that number any more.
But IoT is much more than the number of connections – it’s all about the Data and the intelligence that can be gleaned from the Data.
As more objects are becoming embedded with sensors and gain the ability to communicate, new business models emerge.
IoT also creates new pathways for information to travel – especially across an Organization’s bounday and across it’s value chain and in engaging with their customers.
This Data and the Intelligence gleaned from it – will fundamentally transform organizations creating a new kind of ‘Predictive Organization’ which has Predictive analytics / Machine Learning at it’s core i.e. Algorithms that will learn from experience.
Machine learning is the study of algorithms and systems that improve their performance with experience. There are broadly two ways for algorithms to learn: Supervised learning(where the algorithm is trained in advance using labelled data sets) and unsuprevised learning (with no prior learning – ex with methods like Clustering etc).
Machine Learning algorithms take the billions of Data points as inputs and extract actionable insights from ther data. So, the Predictive Organization starts with the prediction process and then creates a feedback loop through measuring and managing. Crucially, this tales place across the boundary of the Enterprise
I believe there are twelve unique characterictics of IoT based Predictive analytics/machine learning
1) Time Series Data: Processing sensor data.
2) Beyond sensing: Using Data for improving lives and businesses.
3) Managing IoT Data.
4) The Predictive Organization: Rethinking the edges of the Enterprise: Supply Chain and CRM impact
5) Decisions at the ‘Edge’
6) Real time processing.
7) Cognitive computing – Image processing and beyond.
8) Managing Massive Geographic scale.
9) Cloud and Virtualization.
10) Integration with Hardware.
11) Rethinking existing Machine Learning Algorithms for the IoT world.
12) Co-relating IoT data to social data – the Datalogix model for IoT
Indeed one could argue that IoT leads to the creation of new types of organization – for instance based on the sharing economy based on converging the digital and the physical world.
I will be launching a newsletter starting in Jan 2015 to cover these ideas in detail.
You can sign up for the newsletter at futuretext IoT Machine Learning – Predictive Analytics – newsletter