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How to determine the right balance of cloud and edge for your analytics project

The cloud continues to be the foundation for IIoT, but the emergence of edge computing now gives companies an additional option to modernize their infrastructure and operations. While the cloud offers the benefits of reduced IT costs and scalability, the edge is another option that offers faster response time and increased data security.

However, despite its benefits, the emergence of the edge has presented new challenges for industrial companies looking to upgrade their computing systems, forcing them to determine which combination of cloud and edge is needed for any successful analytics projects they may take on. While both cloud and the edge are important components of a full analytics-based project, there are three key considerations for how to best determine what balance of these technologies is right for your setting.

Cost factors in system upgrades

More often than not, costs can skyrocket as organizations look to upgrade their systems by implementing real-time analytics and introducing historical analysis in the cloud — especially as the bandwidth required to move data from the edge to the cloud can get pricey. To make matters more complicated, companies cannot predict their cloud costs if they don’t know precisely how much data they’ll need to move to the cloud. With edge computing, you can address the costs that seemed unpredictable with cloud computing alone.

With edge models, your costs are transparent. This unparalleled insight into expenditures makes it easier to see the direct value of the technology, immediately providing insights that will help your organization improve productivity and efficiency while also increasing worker safety and product quality.

Analytics and real-time capabilities make an impact

If your organization doesn’t have the need for real-time analytics, cloud computing is a fine option. However, some processes benefit tremendously from real-time computing and are too critical to move to the cloud. By moving this computing to the edge, organizations can not only address latency concerns, but can also cut down on costs — as edge computing is often at a lower price points than alternative systems.

A substantial amount of applications can be supported at the edge. These systems collect data for real-time analytics which help to improve plant floor operations, such as predictive device failure, keeping critical processes up and running.

Security needs with increased modernization

When adopting digitized technologies, increased security is something to consider — especially when computing is pushed away from the network. While cloud systems do offer security, the addition of edge computing is often preferred for organizations that have major security concerns.

As a security measure, the edge preforms pre-processing so that only necessary data is sent to the cloud — keeping sensitive data closer to the network. The edge also provides analytics with instant feedback from localized storage points across multiple sensors, which in turn reduces the number of access points that are susceptible to hacks.

Finding the right balance between edge and cloud is the key to success. With right combination of edge and cloud, organizations can see real ROI and often decreased costs. That said, appropriate tools and computing types will ensure that your plant’s data is accurate, your costs stay down and your operations are protected.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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