In an age when every company is a technology business, digital transformation becomes imperative for enterprises to remain competitive. The first wave of digital transformation focused on moving workloads to the cloud. Enterprises undergoing large-scale digital transformations centralized data processing and storage by migrating entire operations to public or private cloud services. The recent influx of connected devices and the resulting data explosion is putting a strain on this model, and now companies are looking toward the next wave of transformation that will move them closer to the edge and their customers.
Today, the explosion of IoT devices, autonomous vehicles, robotic systems, and other digital platforms has resulted in a deluge of data and a vast expansion at the edge of the network. This is putting a strain on the network and requires new ways of processing, analyzing, and acting on this data in real time. This intersection of new technologies and the massive amounts of data they produce put us at an inflection point when we consider the architectures that will help us manage the global economy.
Enter the next wave of digital transformation. Call it Digital Transformation 2.0. It is focused on empowering the edge, aimed at deploying an enterprise’s business logic as close as possible to the customers where rapid, real-time data analysis can have life-altering impacts. Doing this requires an adjustment in mindset from traditional IT development models to a more cooperative DevOps approach. Bandwidth, latency, geography, and security are critical considerations for applications today. This calls for a new approach to IT that moves workloads out to the edges, closer to the customers where action takes place.
Edge computing will take on greater importance across industries as the number of connected devices and applications increases. Today’s applications in banking, healthcare analytics, robotics, and autonomous vehicles require reliable network connectivity and real-time access with low latency. In many cases, centralized cloud computing may not be able to keep up with the processing demands of these applications simply because of the latency introduced by network distance from the edge. This results in inefficiencies and intolerable lag times. A stock broker who needs to make quick decisions based on market fluctuations, or an emergency room doctor looking for test results before performing a procedure, can’t afford to wait for an answer when the stakes are high.
In this 2.0 world, the cloud and edge work together in new ways. The cloud will take on a more strategic role while operational computing – deploying insights generated in the cloud – will shift to the edge to avoid latency penalties. In one model, AI in the cloud might develop algorithms based on massive volumes of data, while edge computing applies those algorithms to data streams as they are gathered and processed.
For instance, self-driving vehicles must be able to collect, analyze, and act on a variety of data in real time. They are essentially mobile computers and data centers on wheels. Consider an autonomous bus that travels a well-defined route along Geary Boulevard in the Union Square district in downtown San Francisco. There are plenty of exceptions to any ideal conditions that require rapid analysis and response: changing signal lights, pedestrians stepping into the street, stalled vehicles blocking lanes, a fender bender that holds up traffic. Waiting for the data to travel to the cloud and back could have disastrous results. Moving the logic and compute power to the edge – as close as possible to the bus – makes it possible for the autonomous system to use the data to make the right adjustments in real time and avoid collision—or worse.
These kinds of rapid analysis and response applications are becoming more prevalent across industries. Manufacturing facilities that rely on specialized robotics need to gain quicker insight to data generated by machines and respond to equipment malfunctions or quality control issues. In the retail industry, edge computing can be used to analyze store-specific point-of-sale data and regional trends to inform everything from inventory to in-store displays.
Streaming media and online gaming, where user experience is core to the business, are also moving more computing resources to the edge. If a game bogs down during peak usage due to lack of bandwidth or unstable network connectivity, users will abandon the platform. Gaming engineers constantly need to make changes to improve game play and fix issues in real time. This requires engineering and deploying in rapid succession.
Given the number of application areas that need to move logic closer to the customer, the edge becomes more important in the next phase of digital transformation. This means that enterprises have new decisions to make around the question of build or buy. Building out the edge requires large capital investments that many companies just aren’t able to make. Just as organizations needed to evaluate whether to build their own cloud operations or migrate to managed cloud services such as Amazon Web Services or Microsoft Azure, companies will need to decide whose edge computing network will provide the best solution for their needs.
In the next phase of digital transformation, enterprises will look to edge computing to help close the latency gap between their data and their application. While the cloud continues to play a vital role as a central data repository and processing center, apps that depend on rapid response times will move to the edge. The challenge is understanding how to combine computing, storage, and networking capabilities into a complete solution from the cloud to the edge. This is why selecting the right partner for this next phase of digital transformation is all the more critical.
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