The Silent Roar: Predicting the IoT in 2020

By Brian Russell

With the Internet of Things growing at an ever-fast rate, the coming year will afford many new opportunities to collect and process data at the edge.

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Take in this moment. We’re almost 100 years since the roaring 1920s, and technological advances have made great strides in transforming business culture and outcomes. Back then, manufacturing and production lines were king and queen of buying and selling. These days, the digitally connected world called the Internet of Things (IoT) is one of the main drivers in the marketplace.

The Roaring Twenties were marked by many pivotal moments, like the first Macy’s Thanksgiving Day Parade, prohibition, jazz, Amelia Earhart flying across the Atlantic Ocean and the big Wall Street crash of 1929. The rise and fall of the decade’s milestones and morale affected people throughout the decades following. In terms of technological advancements, the ’20s brought radio, silent movies and Henry Ford’s automobile industry to the masses.

The most recent years of the 21st century have shown significant improvements in technologies as visionary companies seek new ways to help quench the ever-increasing thirst for data. The next 100 years might be too formidable to predict, but it’s safe to assume the upcoming year of 2020 will be pioneered by IoT – connected devices computing and analyzing data on the edge as the unheard, silent roar changing the future.

Prominence of the Edge in 2020
As cloud strategies mature, many companies have now completed their migration to the cloud for targeted workloads and are continuing to look for other ways to improve their performance and competitiveness.

Reducing cloud spending while maintaining, if not improving, the effectiveness of their digital environment has become a focus for many organizations. A number of factors have made the total cost of ownership for cloud services higher than some decision makers have expected. Although some may point to pricing strategies of the public cloud providers, the ease with which cloud services can be created and consumed brings challenges to organizations without effective governance mechanisms in place.

Latency is a significant challenge in certain contexts for which real-time or near-real-time responses are required. The higher network latency that comes with geographically dispersed sites within the public cloud infrastructure presents a challenge for those use cases.

Because of persistent needs for low latency and to reduce cloud spendings, we’ll continue to see more use cases in which both hardware and software technologies are used in imaginative new ways for ingested data to be analyzed at the edge, or at least processed to reduce the volume of data that is sent to the cloud. For example, we should continue to see tighter integration of hardware, such as GPUs and wireless cards that support various wireless protocols with software stacks capable of ingesting, processing, storing and displaying data all within a single edge-computing appliance.

Better Data-Management Platforms
For years, many organizations have continued to collect vast amounts of data in data warehouses, data lakes, data silos and the like, but have been challenged to extract meaningful insights from the wealth of data they possess. The raw data produced by the sensors and devices deployed in IoT use cases only adds to the abundance and is of limited value, unless and until it can be deciphered.

While there are a multitude of software packages available, both open-source and commercial, it requires a deep technical understanding to bring the right tools together to create a pipeline to ingest, transform, store, analyze and visualize the data to realize a business’s intended outcome. What we’ve seen to mitigate this problem is a progression of data-management capabilities. Pre-integrated software stacks that bring together specific tools to effectively process the data are the most basic solution after disparate tools.

Many times, these stacks of software components can be combined with a single installation and provide the ability to transform the data. The next step in the progression is engaging data-management platforms offered by a multitude of vendors. These platforms often take the same tools and utilities found in the pre-integrated stacks but provide a layer of abstraction that makes achieving the intended outcome a simpler process.

In 2020, we should see the level of abstraction continue to increase for a better, more effective user experience. Self-service features that can continue to encourage the rise of the citizen data scientist should continue to develop. In addition, the proliferation of off-the-shelf software packages created to help discover specific business insights will continue to make these platforms much more powerful tools for organizations looking to unlock the value in their data.

Pathways to Production
There are various requirements that must be met when deploying any solution to solve a specific business problem. Once an initial architecture has been designed to address the problem, a proof-of-concept (POC) is initiated to determine if the intended outcome can be achieved through the prescribed method. Additionally, a proof-of-value (POV) is also performed to ensure that the solution can be implemented at a cost less than the expected savings or additional revenue that it is expected to produce.

Many organizations have successfully proven the concept for particular use cases through pilot testing, yet they struggle to roll out the solution at scale across their enterprise in a cost-effective manner and will consequently be looking for technology and services that overcome this issue of scale. When it comes to managing devices for a large-scale IoT solution, software packages are now being offered to provide effective and secure means to manage devices. The capabilities of these device managers need to improve in order to provide better manageability, such as more accurate and detailed inventories and more frequent over-the-air updates.

A Tell of the Times
Decision makers can approach the challenge to digitally transform their business either with excitement for the opportunities or fraught with uncertainty. The coming year will afford many new opportunities to collect and process data at the edge, to employ more effective platforms to manage their data, and that will allow them to scale the pilot projects from which they’ve realized value.

As an Internet of Things (IoT) solutions architect at Atos North America, Brian Russell partners with clients to identify and achieve business outcomes of greater efficiency and potentially new revenue streams through the innovative use of technology. Brian has more than 25 years of experience with architecture, implementation and support within mission-critical environments and now focuses on achieving outcomes for customers leveraging the Internet of Things. He is responsible for providing technical solutions using the latest connected products and offerings that enable the client’s digital transformation, as well as contributing to the development of Atos’s service portfolio by identifying new IoT-related offerings and products.