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7 ways digital transformation and edge computing complement each other

The work of digital transformation is heavily reliant on data analysis. However, in order to make fundamental changes, organizations must frequently make significant changes in how data is collected, stored, and processed. Consider edge computing.

As enterprise edge computing applications gain traction, it is becoming clearer how they will interact with digital transformation initiatives. Edge computing can be the link that supercharges potential business outcomes in the case of many advancing capabilities, such as machine learning or IoT.

Typically, digital transformation is focused on enabling better products, services, experiences, or business models. Data is at the heart of such transformation; however, fundamental changes are often only possible when the organization can make significant changes in how data is gathered, moved, stored, or processed. Enter the cliff.

“A purpose-built edge solution may be required to drive real-time operations and closed-loop analytics, as cloud-only solutions may not be appropriate at the edge,” says Vishnu Andhare, senior consultant at technology research and advisory firm ISG. “The most significant way that edge computing supplements DT initiatives is by enabling edge-native applications that leverage cloud-native principles while taking into account the edge’s unique characteristics: standardization, connectivity, scalability, security, hyper-personalization, manageability, and cost.”
What role does edge computing play in digital transformation?
Here are some of the most common ways that digital transformation initiatives and edge computing are collaborating to create more business value today:

1. Intelligent processes and predictive maintenance
According to IDC’s McCarthy, the resulting predictive maintenance and asset optimization algorithms are improving a key metric in many organizations: overall equipment effectiveness (OEE). OEE evaluates equipment availability, performance, and quality to determine manufacturing productivity. “Factory operations teams run these algorithms on on-site edge infrastructure, which reduces latency to the cloud by minimizing data movement.”

2. Cost-cutting while providing application services anywhere
According to Rosa Guntrip, senior principal marketing manager, cloud platforms at Red Hat, data volumes continue to grow as the number of devices, applications, and people who need to connect grows. “If all data must be routed back to a central data center for processing, organizations may find themselves needing to scale up their data center infrastructure to meet rising demand, which has an impact on both CapEx and OpEx costs.” Furthermore, if all of that data must be returned to a central location, organizations must consider the costs of data backhauling (i.e. cost of bandwidth).

3. New models for customer experience and service delivery
According to Joshi of Everest Group, banking, financial services, and insurance firms are looking to edge computing to help develop new customer experiences and services that take advantage of connected devices ranging from wearables to connected vehicles. Edge can also help improve user experiences by utilizing bots and voice-enabled intelligent assistants.

4. Availability and responsiveness in real time
Retailers are deploying edge systems in stores and regional warehouses at a rapid pace. “These businesses are confronted with an increasing number of IoT systems, such as point-of-sale, digital signage, and asset tracking.”
Consumer packaged goods companies can also take advantage of the convergence of edge computing and DT to improve supply chain visibility and logistics oversight.

5. Latency-sensitive application support
“Easy-to-identify opportunities, such as streaming media and real-time collaboration, are frequently those that can have the greatest impact on your user base,” says George Burns III, senior consultant for cloud operations at SPR. The most obvious (and widely deployed) application of edge computing is streaming high-definition media, from online gaming to augmented reality (AR) applications for service technicians to real-time video streaming in next-generation sports stadiums. “This necessitates edge computing in order to achieve highly responsive applications while avoiding the need to backhaul prohibitively large amounts of data to the cloud.”
Edge computing can also help in life-or-death situations. Instead of relying on centralized cloud services, healthcare organizations can store and process data locally. As a result, clinicians can gain faster access to critical medical data, such as MRI or CT scans, or information from an ambulance or ER, allowing for faster diagnoses or treatments.

6. Better user experiences
“The distance and destination of network traffic that must travel to connect a remote worker to their corporate network resources will almost certainly have changed significantly,” Burns says. “These changes in the landscape can frequently result in a less-than-desired user experience, prompting businesses to consider a different content delivery strategy.” In many cases, incorporating edge-optimized resources can improve employee, partner, and customer experiences.

7. Distributed asset and device orchestration and security
Energy and utility companies, for example, may benefit from deploying edge capabilities to enable real-time interventions for operational efficiency. Operating an oil rig can entail managing a variety of legacy assets from various manufacturers. “Edge-orchestration platforms can assist in rapidly connecting and disconnecting heterogeneous devices (each with a different interface and communication protocol) and achieving zero-touch and zero-trust management of these devices.”
Edge computing, can perform real-time operational decisions based on local analysis of sensor data for remote operations with limited or no connectivity to the cloud, such as deep in a mine or out in an agricultural field.

Many IoT sensors in warehouses, factories, fields, and vehicles use an asynchronous data model.
“With the increase in computing, connectivity, and other functionality available in these IoT form factors, many of these devices can now perform some of their own computing operations,” says SPR’s Burns. “Field sensors that can analyze soil and moisture data can provide a more immediate response to control equipment, providing more value than sensors that rely on a cloud-based approach to perform compute actions.”

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