Traditional methods for processing data require large data transfers between (a) end users, IoT sensors, POS devices, “edge” data centers, and others; and (b) centralized systems, such as cloud or on-premise infrastructure. The latency inherent in these transfers delays both organizations and end users from acting on data-driven insights, often at a competitive disadvantage. Edge computing optimizes these data exchanges by shifting more computing power to edge technologies themselves, reducing their dependency on centralized systems. This can be critical in sensitive manufacturing operations, for example, or when end users are customers for whom timely results are critical.
Scaling centralized resources like data centers and even cloud infrastructure can be costly and often exceeds an organizations’ incremental needs. Scaling compute resources at the edge allows an organization to expand its network and computing power without large investments in centralized infrastructure. An organization can optimize how its computing power is distributed as it scales as well, preventing bandwidth issues common when scaling traditional infrastructure.
Traditional infrastructure becomes more vulnerable as organizations add distributed devices or allow end users to access resources through unapproved and potentially compromised networks. Bad actors are increasingly seeking out these vulnerabilities within organizations of all sizes as well. Edge computing reduces these vulnerabilities by limiting the amount of business-critical data exposed when transferring between edge and centralized systems. Higher compute power at the edge means organizations can push more sophisticated security functions to edge devices as well.
Organizations can localize essential functions to edge devices—such as data collection, processing, or UI features—ensuring connection loss doesn’t disrupt critical functions in those remote environments. This way, IoT sensors can continue collecting, storing, and processing data on a limited basis in a way that nonetheless allows operations to continue until their connection is restored. End users can access critical resources even as they wait for their connections to be restored as well. Data center downtime needn’t shut down entire networks a result.
Edge computing allows organizations to qualify, categorize, and optimize the distribution and computing of data. That means non-critical data can remain at the edge for processing, while only business-critical data is processed, isolated, and transferred between edge resources and core infrastructure. Minimizing the transfer of non-critical data in this way can drive real cost savings for organizations. Accelerating computational power at the edge can drive greater operational efficiencies and business results, increasing the efficacy and ROI of edge investments as well.
Compute capabilities at the edge needn’t fall into a single category. Organizations can adopt a wide variety of edge devices and systems—from unique end-user dashboards to differing IoT devices, each with unique functions. Organizations can scale their adoption of edge systems both quickly and incrementally, optimizing their acquisition of new edge capabilities as they grow. This is especially useful when expanding into new markets or taking advantage of new data sources in a timely way, without the cost and time associated with scaling centralized IT resources.
With edge computing, end users needn’t conform to centralized systems that are optimized for the organization, and not for them. When employee- or customer-facing systems are at the edge, organizations can optimize features and functionalities for those end users instead. For example, employees can benefit from unique, purpose-built dashboards they can augment through edge-based self-service functionalities. Users who are customers can do more to personalize their experiences, or organizations can provide a wider variety of edge capabilities to a diverse customer base as well.
Depending on the use case, edge computing can allow users to control what personal data is shared with centralized systems or remains at the edge. Employees who use edge computational resources for private or confidential functions can benefit from advanced capabilities without sharing that data with centralized systems, let alone cybercriminals. Customers who wish to avoid sharing personal data with an organization can nonetheless enjoy advanced functionality at the edge as well.
