What exactly is “IoT”? Internet of Things, yes; but what does that mean?
Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?
Being able to connect things together is “table stakes” at the intelligence augmentation game. What you are able to do with this network is the real story.
IoT is an enabler of THREE high-level objectives:
(1) DO MORE: Whether machines producing more (high throughput) because of less breakdowns or a weekend athlete burning more calories because she is able to keep her heart rate in the fat-burning zone, we are accomplishing more.
(2) HIGHER QUALITY: By monitoring environmental pollution, cities restrict automobile access into city center for better health outcomes over time.
(3) BETTER USER EXPERIENCE: In the near future, mass customization will allow me to find eyeglasses with perfect fit at low cost based on video inputs at connected additive manufacturing facilities.
All three objectives can be met because Network of Sensors & Devices exist and will grow. But Networking of Sensors & Devices does NOT capture what has to happen so that we can DO MORE at HIGHER QUALITY & BETTER UX! The information that rattles around the network has to be consumed properly, insights generated and decisions made.
A functional description of IoT is a network that “processes” information in the network towards our three objectives. One can see that “processing” is enabled by a multitude of technologies: IP networking, wireless, chipsets, protocols, security, cloud computing, database technologies, analysis software, visualization and so on. Subsuming this under “IT or Information Technology” and keeping it aside for the moment, the “higher-level” processing involved is Applied Data Science!
Applied Data Science is a tautology. Data Science IS the applied aspects of many pure sciences (see “What exactly is Data Science?” for details). Beyond the network of sensors & devices and base IT technologies partially listed in the last paragraph, what is unique and new in IOT is Data Science applications –Data Science applied with the focus on information extraction, insights generation and prescriptive decisions. There is no identifying name for this *applied* aspect of Data Science but I have been referring to it as “Engineering” Data Science. We use “engineering” in the sense of the applied aspect of any science (Engineering is the applied aspect of Physics, for example).
IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science)
Each component is critically important to IoT; major advances in all three in recent years have made IoT and its promise real. Engineering Data Science (EDS) is the youngest and the least mature of the three. Immediate next steps in EDS evolution seem clear to me (more in Next Stage in IoT revolution – “Continuous Learning”).
When IoT is defined as “(Network of Sensors & Devices) + IT + (Engineering Data Science)”, it seems to pervade ALL industries from my vantage point! What do I mean by that?
Let us look at the largest 5 (excluding IT) sectors of S&P 500: Consumer Discretionary, Energy, Financials, Health Care & Industrials.
I have mentioned only a few instances in the right hand column but you can add many more. They all require some data generating mechanism, IT connectivity and decision making software. What this shows me is that IoT has already pervaded and will totally engulf all the businesses! As such, I tend to look at IoT as a technology framework that underpins ALL businesses and industries of the 21st century.
Now, does the exact same IoT serve all these sectors or are there nuanced variations? As I mentioned, EDS is the youngest and fastest evolving portion of IoT today – let us focus where the changes are most rapid.
I have partitioned applied Data Science into three: Industry, Business & Social Data Science.
As you can see, each application area calls for refinements and adaptations to its verticals. Specialization for each vertical notwithstanding, the three “types” of Data Science are best seen as a unified whole, which we are calling “Engineering Data Science or EDS”. Rapid progress in Engineering Data Science is required to achieve our three goals of “DO MORE at HIGHER QUALITY & BETTER UX” with IoT.
IoT is not JUST what GE, Siemens, ABB or Hitachi do! It is a technology framework for all business and industrial technologies going forward. IoT is just a metaphor for this powerful, all-encompassing technology framework.
PG Madhavan, Ph.D. - “Data Science Player+Coach with deep & balanced track record in Machine Learning algorithms, products & business”