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FogHorn 2020 Predictions: How Edge Computing Will Transform Current and Upcoming Industrial and Commercial IoT Deployments in 2020

VMblog Predictions 2020 

Industry executives and experts share their predictions for 2020.  Read them in this 12th annual series exclusive.

By Ramya Ravichandar, VP of Products, FogHorn

How Edge Computing Will Transform Current and Upcoming Industrial and Commercial IoT Deployments in 2020

Over the past year, edge computing has grown in adoption and popularity for IoT deployments to deliver the real-time responsiveness necessary for critical use cases in industries like manufacturing, oil/gas, and smart buildings. Indeed, Gartner predicts 75% of enterprise-generated data will be created and processed outside the traditional, centralized data center or cloud by 2022.

Following the broader understanding and wider adoption by industrial and commercial organizations of edge technology in 2019, edge intelligence functionalities will continue to evolve in 2020, with the goal to move data-driven projects from proof-of-concept to effective full deployments.

Three predictions to keep an eye on in 2020 are:

Organizations will move IoT projects from proof-of-concept to proof-of-value deployments

IDC anticipates there will be over 41 billion connected Internet of Things (IoT) devices, generating over 79 zettabytes of data, by 2025. This trend will be driven by the expanding quantity and variety of streaming data channels, moving beyond audio, image, and video sensors to also include acoustic, acceleration, vibration, and others. Training data for artificial intelligence use cases and machine learning model creation also play a significant role here.

 During proof-of-concept (POC) deployments in the last few years, many organizations have confirmed the benefits that IoT can bring to a wide variety of industries - and IoT spending is expected to reach $1.1 trillion by 2025, according to IDC. For example, smart city initiatives are replacing existing equipment with IoT-enabled, embedded sensors that capture a wide range of data. Armed with IoT data, cities can improve public safety and security, energy efficiency, traffic management and respond to ever-changing environmental and weather conditions.

 However, one of the main challenges in the industry today is figuring out how to capture, organize, process, and deploy large amounts of complex data more effectively. Although organizations are able to implement IoT solutions and gather data, there are still a few roadblocks impacting wide-spread adoption, including skill gaps and cloud costs. Indeed, only 26% of organizations that implemented IoT felt their project was successful.

In 2020 and beyond, we will see organizations move IoT and IIoT projects from proof-of-concept to full deployments with the goal to increase overall operational efficiency. To move beyond initial POC benefits, organizations will focus on innovative new opportunities, such as edge computing, to drive significant ROI, deliver enhanced operational productivity, and achieve the final proof-of-value phase.

In addition to data quantity, organizations must improve data quality to drive actionable insights 

While many see connectivity limitations, security risks, and data bias issues, including data quantity, as roadblocks to IoT success, data quality also plays a critical role in delivering effective IoT projects. Organizations can only make the right data-driven decisions if the data used is correct and suitable for the use case at hand. Edge computing plays an essential role in evaluating and delivering heightened data quality, as edge-enabled solutions can perform real-time analysis of disparate data streams and identify only the most valuable insights for further processing and AI training.

Looking ahead, data processing and enrichment at the edge will contribute to IoT success by identifying and addressing false and inaccurate machine learning models that lead to dangerous machine failures, declining operational productivity, and significant cost issues.

Edge-enabled solutions will power a more sustainable future

In 2020, we will see an increase in edge computing deployments driving green tech use cases to minimize carbon footprint. Transport organizations will start deploying edge computing to detect abnormal regen and idling events in real-time to save billions of pounds of CO2 emissions per year. Additionally, oil and gas organizations will deploy edge technologies to monitor flare stack health to understand emissions output. Through sensor fusion technology, edge solutions will help identify issues with compressor health and alert operators about potential regulatory violations. Also, steel manufacturers will look to edge computing to save millions of tons of CO2 emissions by identifying defective parts produced in steel manufacturing as early as possible in the process to reduce scrap and increase yield.

For these organizations, edge solution will deploy real-time measurement data and machine learning models to determine product quality and directly impact sustainability initiatives.


About the Author

Ramya Ravichandar 

Ramya Ravichandar, Vice President of Products at FogHorn, brings a rare combination of technical expertise in real time analytics, machine learning and AI, combined with valuable experience in Industrial IoT. She is a seasoned product leader who previously headed Cisco's Streaming Analytics platform for IoT.  Ramya has a PhD in Computer Science from Virginia Tech.
Published Tuesday, December 03, 2019 8:05 AM by David Marshall
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