Smart switches, thermostats, household appliances and other devices are becoming more intuitive by the day, as they ingest ever-increasing amounts of data. This information is used to empower the device – and any system driving it – to learn more about the role it is performing and adjust automatically in order to perform even better.

According to Statista, there will be in excess of 30 billion connected devices in the world by the end of 2020. With the sheer volume of data now in the world, it is impossible to collect all of this information into one central repository, analyse it efficiently and then push recommendations back to the device.

In 2020 and beyond, technology will evolve to execute the compute function on the devices themselves - a.k.a. at ‘the edge’, rather than doing it centrally.

By computing at the edge, these devices will improve functional efficiencies by learning to adjust in real-time rather than being slowed down by the transfer of information to and from a central system.

Devices Becoming Smarter

It was not until a few years ago that traditional devices became smarter and new smart devices started emerging such as – “Alexa, how’s the weather today?” – voice assistants. Now, these devices are becoming even more intelligent with the ability to adjust on the fly.

For example, Google’s Nest uses motion sensors to determine when a person is home and when they are not.

This adds a lot of functionality to a household, such as the ability to set air-conditioning to switch on at the most opportune time before an individual arrives home. This saves power by not running the air-conditioning unit when nobody is home, but enhances living conditions by heating or cooling the house ahead of it being occupied.

While the resident can program Nest just like any other thermostat, and tell it when to heat or cool the home, what makes Nest different from the “non-smart” thermostats is that it uses the data it collects to automatically adjust the program to warm or cool the home knowing when the resident leaves and returns. This eliminates the need for the resident to constantly adjust the thermostat based on when they are home, or waste electricity consumption if they forgot to adjust.

Centralised Analysis Becoming Harder

Traditionally, analysing data and deriving intelligence from it has been done centrally. Data warehouses, the workhorse of business intelligence, are central repositories into which data is ETL’d – Extracted from operational systems, Transformed into the appropriate format, and Loaded into the data warehouse.

However, data warehouses are now losing their place in the spotlight for various reasons. For one, data warehouses can only store structured data; whereas, the bulk of data these days is unstructured.

Another reason is the sheer volume of data; it has become so vast that it is not economically feasible to store all of the data – whether that is an organisation, utilities company, data centre - in a single data warehouse. To lower costs, companies offload non-current, historical data into cheaper repositories like Hadoop.

This bifurcation creates yet another silo for data.

Given these evolutions, it is now impossible to collect all of the information generated across multiple systems and devices, right across their various locations, into one central repository. Nor can the information be effectively analysed for intelligence, and then used to make recommendations back to the device for optimal performance.

Edge Computing as the Solution

What’s missing is the fact that the technology has yet to evolve in order to execute the compute function on the device itself, rather than doing it centrally. Known as edge computing, this concept involves the devices having to evolve their capability to not only generate data, but also use it for analysis or computational purposes.

Thus, they derive intelligence to meet their needs and become self-sufficient.

However, such independence does not mean that they function autonomously. These devices are still connected to a central system and transmit the information that is needed for the central system to analyse across all of the devices.

As a result, there is a duality of computation in which some analysis happens at the edges - to the extent needed for local operation - and, at the same time, data is transmitted to a central analytical system to perform the holistic analysis across all of the enterprise systems.

Reducing the data at the sources and transmitting only the required information to a central system is nothing new. Data virtualisation has been doing this for decades.

Data virtualisation is a style of data integration that is real-time and accomplishes the task without replicating the data into yet another repository. When a request for a data set comes in, data virtualisation runs the queries in the relevant source systems, extracts the results, and only delivers them to data consumers, who use reporting tools to analyse the results.

So, edge computing is akin to data virtualisation in the manner that it runs the queries at the edges and transmits only the information needed by the central system.

Benefits of Edge Computing

The biggest benefit of edge computing is time savings. Over the past several years, two aspects of the technology have evolved much faster than the others—storage and compute.

Today’s mobile phones have more memory and compute power than the desktops from 30 years ago. Yet, one aspect of the technology that has not evolved as fast is the bandwidth to transmit data, as it still takes minutes or hours for data to move from one location to another.

With devices moving farther and farther away to the cloud and across continents, it becomes imperative to transmit the smallest amount of data as possible in order to improve overall efficiency.

By delegating compute to the edge, these devices will learn and adjust in real-time rather than being slowed down by the transfer of information to and from a central system.

Being Smarter…on the Edge

As technology evolves, it creates a new problem that requires a new solution. With the advent of smart devices, data volume has exploded and reduced the efficacy of centralised computation and analysis.

Edge computing solves this problem by making smart devices even smarter by helping them to process their own data and then transmit only the data that is required for centralised computation. As a result, they improve the efficiency of not only the edge devices, but also that of the centralised analytical systems.

Given the promise that edge computing offers, it is poised to take off as one of most important technology trends in 2020 and beyond.

www.statista.com/statist…worldwide/