By Srinivas Rao, Sr. Director, System Engineering, Dell Technologies
Edge and Cloud environments are often seen as competing platforms on the surface but they are synergistic in nature. They both complement each other to help organizations understand and act on data from all areas of their business. By harnessing the strength of both and distributing the intelligent analysis in the most appropriate way, businesses can reduce costs and maximize their productivity and efficiency.
The edge delivers immediacy and action
Edge plays a critical role in how organizations leverage new data sources to drive specific and differentiated business outcomes. 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 basis 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.
In the case of a retail setting, the edge can play a vital role where real-time insights are needed. For instance, when a customer scans items through the self-checkout, pricing can be downloaded onto the device to accelerate the transaction. Also, customers can generate real-time coupons when they scan items. Localized analytics capabilities can identify what is being purchased and can generate coupons for related products. Moreover, local analytics can help retailers prevent loss by mitigating scanning errors and other mishaps at checkout.
Acting on the 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.
The cloud gives data a second life
While data used in edge use cases loses value once analyzed and processed, it could regain value when transferred to a private or public cloud. 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 collected at the edge, business leaders can realize its long-term value.
Keeping in mind the same retail setting, in-store trends can be analyzed by taking store data and comparing it over time using advanced analytics activities like inventory, pricing, as well as improving employee scheduling. Also, a 360-degree customer profile could be created by comparing in-store aggregate data and matching it with other data sets such as loyalty programs to enable more personal engagement methods. There is no need for immediacy or real-time action in certain scenarios but accumulating data over time can help businesses reap benefits in the long run.
Using edge and cloud resources in tandem requires consistency
Organizations are realizing the need for distributed architecture to meet their business requirements and one inhibitor of the same is the complexity across edge and cloud environments. Each environment supports many applications and has its own management capabilities and operations. Hence, it becomes extremely critical to establish consistency across private clouds, public clouds, and edge environments.
Establishing consistency across environments can help balance environmental constraints of the edge with the latency and data gravity of the cloud, leverage the appropriate environment to meet data and application needs, and let business needs determine where applications reside. It also reduces management complexity and enhances security across every environment.
Looking at this from the lens of the same retail setting, when there are physical and digital touchpoints that complement one another, customers engagement could be established across different mediums to create an omni-channel engagement. This can even lead to predictive purchasing where analytics is applied to data around both in-store and online activity, as well as other consumer data, to determine future buying patterns and consumer intent to optimize the store accordingly.
To conclude, the key to unlocking the power of both edge and cloud-based platforms is by identifying and segregating which business-driven task or operation is best suited to either environment. Time-critical operational tasks sit better in real-time processing capabilities of the edge layer and continuous improvement analysis and overall asset performance analysis etc. which are not time-critical sit better at cloud-based environments. Hence, by leveraging the benefits of both, businesses can maintain optimal operational efficiencies and drive productivity.