Let me start with a piece of trivia. As of today, research suggests there are already more connected “things” than human beings on earth. Not surprising, really, considering that each of us possesses multiple devices to simply stay connected. Think of these countless everyday physical objects being connected to the internet. Some studies predict that the number of connected devices will touch 50 billion by 2020. Times are changing, and so are “things”.
As a technology enthusiast, what fascinates me more than the numbers, is the rapid and relentless evolution in the world of Internet of Things (IoT) and Artificial Intelligence (AI). Right now, the Edge is where all the excitement is. We are seeing a shift from cloud computing to computation and data processing on IoT devices. From smart phones and surveillance cameras to connected cars and drones, embedded solutions can capture, aggregate, transform and preserve data across IoT devices.
Now, what is Edge Analytics? It is a method of analysing data at the edge, near the source or capture device where data is collected. The power of Artificial Intelligence and Machine Learning is what makes real-time analytics at the edge a reality. Edge Analytics has the potential to be the real game changer, as organisations leverage the value extracted from this data deluge to make important, real-time business decisions. The future, I believe, belongs to surprisingly smaller, yet amazingly powerful and agile behind-the-scenes solutions.
Time is of the essence
Traditionally, IoT applications simply collected data from “things” and sent them elsewhere – say to the Cloud - for analysis. However, as “things” got smarter, they were able to run the computation on-site, making the process incredibly faster and more effective. Given the explosion of data generated today, there is a growing need to identify relevant methods for storing and analysing this data to extract critical business insights.
The location where data is stored and analysed has become extremely important to extract maximum value. Analytical tools need to work with real-time data, and the best place to do that is where the real-time data sits – right at the Edge of the network where IoT connects the physical world to the Cloud.
For enterprises to fully leverage Edge devices, reliable and resilient storage as well as powerful analytical tools need to reside within them. This reduces latency and augments agility.
Essentially, there are three crucial functions that define Edge devices (such as mobile phones, surveillance cameras, connected automobiles, and so on): Computation, storage and communication. However, the true value of Edge Analytics lies in its seamless integration between IoT and Cloud. Enterprises need the power and flexibility of Cloud services to run complex analytics on the data processed and aggregated from IoT devices, to support real-time decisions and actions in the physical world.
Context is crucial
Not too long ago, raw data used to sit at the Edge, while metadata was sent to the Cloud. However, increasingly, we have realised that context complements content. To facilitate informed real-time decisions, raw content needs to be readily transformed into meaningful information.
Let’s take the example of smart cities. Without an interconnection-oriented approach that encourages seamless flow of data between IoT devices and the Cloud, how could smart parking services and garbage recycling work? Within a decade, experts predict that majority of the population would be using connected health monitors – to track heart rate, blood pressure and other vital signs – as a preventive measure to beat lifestyle diseases. Similarly, self-driving cars will become a truly viable and safe transport option, with Edge Analytics in the driver’s seat.
To create a more efficient and effective work environment, content needs to meet context and transform passive choices into informed decisions. I believe, companies that underrate the importance of context pay a heavy price. Data in isolation is of little value; it cannot aid enterprises to gain quick and actionable insights to immediate environmental stimuli. It’s only when technologies, such as AI, and Machine Learning (ML), are pushed onto Edge devices that they interact with real-time data to give businesses the all-important context.
The way we live, the way we work, and everything we do is changing. Only those who can foresee the change and constantly adapt to it will survive. Today, we are seeing more AI-driven applications in our everyday world. AI is the buzzword. However, it’s still in its infancy.
As I see it, IoT is the technology to watch out for. And Edge Analytics is the key to exploring its full potential. It is poised to become a world of intelligence, by leveraging the data-driven Edge. In the coming years, we expect an increased adoption of AI in Edge devices, offering speed, agility and cost-effective solutions for new-age enterprises looking for real-time intelligence in today’s connected world.
Edge Analytics has a magical element, which can revolutionise almost every industry – from manufacturing and retail to healthcare and logistics.
FORWARD-LOOKING STATEMENTS: This article contains forward-looking statements, including statements relating to expectations for storage products, the market for storage products, product development efforts, and the capacities, capabilities and applications of Western Digital products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, supply chain and logistics issues, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances.
- By Balaji Sivakumar, Director of Product Marketing - Western Digital
The thoughts and opinions shared here are of the author.
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