Extracting Insights in Real-Time
The IoT market will have a total potential economic impact of $3.9 trillion to $11.1 trillion a year by 20251
By 2020, more than five million smart sensors and other IoT devices will be in use around the world, and will generate at least 507.5 zettabytes of data2
It is estimated that there will be 47 billion connected devices by 20213
What makes real-time analytics at the edge become possible or even preferred, not just now but in years to come, is the possibility of conducting artificial intelligence and machine learning (ML). AI and ML are becoming increasingly more complex, versatile and sophisticated to enable real-time analysis at the edge, on the hub and, ultimately, in the cloud to get more value from all of the IoT data collected. Data can be sent to the cloud, where machine learning and AI can be trained to watch for patterns and gain insights from large data sets over time.
SOURCE: UNTAPPED DATA – GETTING MORE FROM IOT DATA AT THE EDGE AND IN THE CLOUD BY CHRISTOPHER BERGEY, WESTERN DIGITAL (WESTERN DIGITAL BLOG, 5/6/18)
HOW TO EXTRACT VALUE FROM EDGE DATA
For many IoT applications, it has become critical for data to be screened and analyzed where it is generated - from sensors in a car, surveillance cameras, drones, personal devices, robots, gateways, etc. – and even transformed there. The ability to deliver real-time analytics at the network’s edge can improve operational efficiencies, provide safer driving, create more secure environments, foresee upcoming maintenance, identify customer buying behaviors, and enable a world of opportunities.
Network latency is a challenge. It takes too long to store and forward data, whose value exists now. Edge storage manages data captures and provides the compute capabilities that aggregate and analyze that data in real time, to deliver immediate and actionable insights at the device level.
THE VALUE OF EDGE ANALYSIS
Artificial intelligence (AI), machine learning (ML), image, voice and gesture recognition, and other technologies deployed onto edge devices interact with captured data in real time to deliver valuable insights. The full digestion of analytics can be transferred to the cloud where it can be used to further train AI models for machine learning or archived for future use. The ability to access this information in real time, ultimately, creates a more efficient and effective business, operation, or environment, enabling greater opportunities for monetization of the IoT application.
Western Digital Offers an Extensive Portfolio of Products from Devices to Systems and Platforms Designed to Enable the Power and Possibilities of Data Driven by IoT and Edge Computing
In ABI Research’s view, one of the most significant trends in the Internet of Things (or the connected world—the Internet of Everything—as a whole) is the shifting balance between edge computing and cloud computing. The early days of the IoT and its conceptual precursor, M2M, have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level.
SOURCE: EDGE ANALYTICS IN IOT, ABI RESEARCH, 2015
Forward Looking Statements
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1. Source: Unlocking the potential of the Internet of Things by James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, Jacques Bughin, and Dan Aharon (McKinsey Global Institute, June 2015)
2. Source: Edge computing: A cheat sheet by Mary Shacklett (TechRepublic, June 21, 2017)
3. Source: IoT numbers vary drastically: devices and spending in 2020 by Dennis Knacke (We Speak IoT, 10/6/17)
4. Source: Gartner Says 8.4 Billion Connected “Things” Will Be in Use in 2017, Up 31 Percent From 2016, Press Release (Gartner, 2/7/17)
5. Source: Cisco Visual Networking Index: Forecast and Methodology, 2016–2021 (Cisco, 9/15/17)