Investors Guide To IOT Part 1 - Understanding The Ecosystem

February 2016

A A A

The IOT Ecosystem

Just about every technology company now says they’re an IoT player.This of course happens with every new technology shift. In most cases, a company will have some products or services that touch the IoT industry. However, many are just repackaging and renaming existing products or services. To truly understand whether a company is an innovator in this space or just putting IoT lipstick on their legacy pig, one must understand the overall IoT ecosystem, as well as its parts.

I created the following image in an attempt to organize IoT technologies and processes into a collection of categories, which I call layers.

There are 7 layers  that make up the IoT stack. I’ve grouped the  technologies and processes into four domains: Fog Computing, Cloud Computing, Big Data, and Business Value. Let’s start at the bottom.

Fog Computing

Fog computing, often referred to as edge computing, refers to the technologies and processes that occur outside of our clouds and datacenters, and are distributed across the user base. The user base can be made up of humans, machines, or objects with mobile devices, GPS technologies, sensors, or other technologies that can store and process data at its source.

Last year I wrote “Forget Big Data – Small Data is Driving the Internet of Things”. Small data refers to the data that is processed in the “fog” at the device level. Small data is often used to determine when the status or condition of an attribute changes. For example, when an application detects that a person has entered a location within five miles of a retail establishment, the application may push an offer or a message to that person’s device. A package containing perishable items may trigger an alert that the current temperature condition of the container is becoming too warm. A plant manager might get an alert from the assembly line that a part in one of the robotic machines is starting to fail and should be replaced before the entire machine is non-functional.

All these examples are applications that happen in the “fog” and do not ever need to transmit data back to a virtual or physical datacenter for processing. Fog computing is made up of: “Things,” IP enabled devices that can store, sense, and analyze data; and connectivity, a wide variety of technologies for connecting, communicating, securing, and translating packets of data in real time from the variety of “Things.”

Global Infrastructure

The next set of technologies that make up the Internet of Things constitute its global infrastructure. Many IoT applications require multiple datacenters dispersed globally that must be able to scale on demand.  Certainly some companies will use their internal datacenters to build IoT applications, but with data requirements surging into petabyte or exabyte numbers, it quickly becomes unfeasible to spend both human and compute capital to try scaling these applications in real time. IoT is a classic use case for cloud computing and the pay-as-you-go-model.

The big three public cloud providers–AWS, Google, and Microsoft–are all launching robust IoT capabilities to simplify and accelerate the application development process for building IoT applications. Private and hybrid cloud solutions will also play hard in this space due to data locale rules in various countries. A growing number of stand-alone IoT platforms that can integrate with any major cloud provider have also emerged.

Big Data

Small data that is processed on devices in the “fog” tells you “what” is happening. Big data is data from those devices that is collected in real-time, near real-time, or batch, and brought into the virtual or physical datacenter. There, it is ingested, scrubbed, aggregated, and made available for analysis to understand the “why” questions. For example, the retailer who sent the offer to the person who came into the vicinity of the store, may perform a variety of analyses to determine what that person’s buying potential is, their loyalty to the retailer, how effective various offers have been on their buying behavior, and many other use cases.

Packaging and shipping companies may mine data to understand how geography, weather, drivers, packaging, road conditions, and vehicle type influence the possibility of an event being triggered. This allows them to make people. process, or technology changes to reduce the frequency of unwanted events.

Many big data vendors already excel at ingesting and storing data to run deep analytics and machine learning algorithms to gain insights from huge quantities of data. But a new generation of these solutions is emerging to deal with data size and frequency that’s  growing at a scale we’ve never seen before, presenting processing challenges to existing solutions.

Falling into the big data category is a plethora of tools around data visualization, self service reporting/mining, machine learning, and many other functions. Many of the providers have been around for years, but not all of the legacy solutions were built for the scale that IoT demands. Take note, this is a very active space.

Business Value

This category delivers the true value of IoT. When you abstract all the underlying technologies and make it simple to build applications, great value can be derived from being able to react to real-time or near real-time events. This layer of applications and process changes can: save companies millions in preventive maintenance; increase revenue by optimizing business processes, throughput, and speed to market ; or develop a new business model to create a first-mover opportunity.

Smart cities are a great example of how to leverage IoT applications to drive value. Many applications make up a smart city and each one by itself is not transformational. But when a collection of applications is orchestrated together, real business value can be achieved. For example, some cities with traffic issues, leverage a collection of sensors, small data, and big data to detect traffic patterns, derive alternate routes, track parking space vacancies, control traffic lights, and alert passengers with real-time information, so they can make better decisions. It takes process design to bring together all the various technology components in a way that enables intelligent decisions and empowers people with information that generates real business value.

We can apply this to virtually  all industries. We’ve seen agriculture leverage drones, smart tractors, weather and soil sensing technologies, and a variety of other innovative smart devices. Farmers can then bring all that information together to produce greater crop yields, while reducing  watering and fertilizing costs, resulting in greater profitability.

Every industry has a compelling IoT story. We already know how to do cloud computing, big data, and application development. What is new here is the emergence of real-time data in the “fog,” which allows us to write new applications that drive far more intelligent decisions to produce much higher value in real time.

Summary

The technologies that make up the Internet of Things have all been around for years. What’s new are the improvements in bandwidth and connectivity, coupled with the lower cost of computing thanks to cloud computing and the pay-as-you-go model that gives companies the ability to make better business decisions in real time. Companies that embrace the IoT and bring solutions to market will have a huge leg up on their competition. In some cases, these companies are actually creating new business opportunities and becoming first market movers.

In Part 2 of this series I will discuss who some of these companies are and which technologies make up the layers of IoT. Stay tuned.

 

This article was written by Mike Kavis from Forbes. This reprint is supplied by BNY Mellon under license from NewsCred, Inc.  

BNY Mellon is the corporate brand of The Bank of New York Mellon Corporation and may be used as a generic term to reference the corporation as a whole and/or its various subsidiaries generally.  This material does not constitute a recommendation by BNY Mellon of any kind.  The information herein is not intended to provide tax, legal, investment, accounting, financial or other professional advice on any matter, and should not be used or relied upon as such.  The views expressed within this material are those of the contributors and not necessarily those of BNY Mellon.  BNY Mellon has not independently verified the information contained in this material and makes no representation as to the accuracy, completeness, timeliness, merchantability or fitness for a specific purpose of the information provided in this material.  BNY Mellon assumes no direct or consequential liability for any errors in or reliance upon this material.