Why the future of business requires edge and cloud technologies to work together
People often think of edge and cloud environments as discrete technologies. While they do offer unique value, it’s becoming clear that edge and cloud are intrinsically intertwined – they complement one another to help organizations understand and act on data from all areas of their business.
However, those getting started with edge need a clear and actionable strategy that dictates:
◉ What technology you invest in
◉ Where you place that technology
◉ How you will move data across these environments
Let’s talk about a strategy that will help you balance the advantages and constraints of both edge and cloud technologies to transform the way you do business.
The edge delivers immediacy and action
The first component of this strategy is edge computing. Many businesses have a significant operational technology footprint – the machines that are extremely industry-specific like an MRI machine or conveyor belt, as well as those that can be found in almost any building such as HVACs and elevators. These resources have typically remained unconnected and therefore offer little insight into the business from the activities they perform. The edge provides a unique opportunity to take what was once offline and bring it online, allowing organizations to understand and act on data from their physical business activities.
Acting on this data in the real world requires immediacy, which is why edge environments are best suited for applications that require accelerated response times. When you pre-process data and perform light analytics locally, you can uncover real-time insights – such as an overheating asset in your manufacturing plant or an unusual motion detected – and act on them within milliseconds.
Let’s walk through what that might look like in a retail setting.
Edge example: Leveraging real-time insights in retail
◉ Self-service checkout: When a customer scans their items through the self-checkout, pricing can be downloaded onto the device, accelerating the transaction.
◉ Real-time coupons: As the customers scans items, localized analytics capabilities can identify what is being purchased and generate coupons for related products.
◉ Loss prevention: Local analytics can help retailers prevent loss by mitigating scanning errors and other mishaps at checkout.
In each of the examples above there is a need for immediate action on this information and therefore a need for edge computing. A delay of just a few seconds could prove costly and hurt the shopper experience.
The cloud gives data a second life
As I noted above for many edge use cases the value of data drops off rapidly, but that very same data can have a second life when transferred to a private or public cloud. For instance, AI and deeper analytics would likely be cost prohibitive to do in each store. The cloud complements edge computing by providing massive scale to keep up with data growth. It also offers native access to services that enable the use of advanced analytics, artificial intelligence, and machine learning. By applying these services to the data, you have collected at the edge, you can realize long-term value.
Cloud example: Cloud computing in retail
In-store trend analysis: by taking store data and comparing it over time using advanced analytics activities like inventory, pricing, even scheduling employees can be improved.
Build a 360-degree customer profile: By comparing in-store aggregate data and matching it with other data sets such as loyalty programs to enable more personal engagement methods.
None of these efforts require immediacy, in fact they usually benefit from accumulating data over time, so it makes more sense for the seller to move this data to the cloud once it is processed at the edge.
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