Digital transformation and edge computing: 7 ways they fit together

Digital transformation work depends heavily on data analysis. But to make fundamental changes, organizations must often make substantive shifts in how data is gathered, stored, or processed. Enter edge computing
25 readers like this
Two employees analyzing data and reports

As emerging edge computing applications for the enterprise gain momentum, it’s becoming clearer how they converge with digital transformation initiatives. In the case of many advancing capabilities – such as machine learning or IoT ­– edge computing can be the link that supercharges potential business outcomes.

For example, an organization might want to collect IoT data from sensors or devices in the field and process them using artificial intelligence (AI) in the cloud. “While this works in small deployments used for proof of concept and pilot projects, it lacks the ability to scale,” says Dave McCarthy, research director within IDC’s worldwide infrastructure practice focusing on edge strategies. “At some point, the amount of generated data overwhelms networks, resulting in unacceptable response times.”

[ Get a shareable primer: How to explain edge computing in plain English.] 

Digital transformation is typically focused on the enablement of better products, services, experience, or business models. At the heart of such transformation is data; but often fundamental changes are only possible when the organization can make substantive shifts in how data is gathered, moved, stored, or processed. Enter the edge.

“This may necessitate a purpose-built edge solution, 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 with technology research and advisory firm ISG. “The most significant way in which edge computing complements DT initiatives is by enabling edge-native applications that leverage cloud-native principles while considering the unique characteristics of the edge: standardization, connectivity, scalability, security, hyper-personalization, manageability, and cost.”

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

How edge computing helps with digital transformation

Here are some of the common ways in which digital transformation initiatives and edge computing are coming together today to create greater business value:

1. Predictive maintenance and intelligent processes

"Factory operations teams run these algorithms on edge infrastructure deployed on-site, which mitigates the latency to the cloud by minimizing the movement of data."

Many manufacturing and industrial companies are already experiencing the benefits of edge-enabled digital transformation. The need for security, predictive maintenance, and autonomous processes has driven this early adoption, says Yugal Joshi, vice president of digital, cloud, and application services research for Everest Group.

The resulting predictive maintenance and asset optimization algorithms are improving a key metric in many organizations, says IDC’s McCarthy: overall equipment effectiveness (OEE). OEE measures manufacturing productivity by evaluating equipment availability, performance, and quality. “Factory operations teams run these algorithms on edge infrastructure deployed on-site, which mitigates the latency to the cloud by minimizing the movement of data,” McCarthy says.

2. Controlling costs while delivering application services, anywhere

Amounts of data continue to increase from the ever-increasing number of devices, applications, and people who continuously need to connect, says Rosa Guntrip, senior principal marketing manager, cloud platforms, Red Hat. “If all data needs to go back to a central data center for processing, organizations could be faced with needing to scale up their data center infrastructure to meet rising demands, which impacts costs from both a CapEx and OpEx perspective. In addition, if all of that data needs to go back to a central site, organizations are also looking at the costs of backhauling data (i.e. cost of bandwidth).”

3. New customer experience and service delivery models

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, from wearables to connected vehicles, says Joshi of Everest Group. Edge can also support better user experiences with bots and voice-enabled intelligent assistants.

4. Real-time visibility and responsiveness

Retailers are rapidly deploying edge systems in stores and regional warehouses. “These organizations are faced with an increasing amount of IoT systems including point-of-sale, digital signage, and asset tracking,” IDC’s McCarthy says.

Consumer packaged goods companies can exploit the intersection of edge computing and  digital transformation for supply chain visibility and logistics oversight.

“Edge computing can aggregate data locally, providing real-time visibility into operations. It also can summarize data into meaningful events before sending to the cloud or centralized data center, reducing data transfer and storage costs.”

Consumer packaged goods companies can also exploit the intersection of edge computing and DT for greater supply chain visibility and logistics oversight.

5. Support for latency-sensitive applications

“Easy to identify opportunities, such as streaming media and real-time collaboration, are often those that can provide the biggest 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 in streaming high-definition media, from online gaming to augmented reality (AR) applications for service technicians to next-generation sports stadiums’ streaming of real-time videos. “This requires edge computing for achieving highly responsive applications while eliminating the need to backhaul prohibitive amounts of data to the cloud,” says Andhare of ISG.

Edge computing can also speed things up in life-or-death scenarios. Healthcare organizations can store and process data locally instead of depending on centralized cloud services. As a result, clinicians can get more immediate access to important medical data like MRI or CT scans or information from an ambulance or ER, for faster diagnoses or treatments.

6. Improved user experiences

The events of the last year have placed a heavy burden on global network infrastructures.

The COVID-19 pandemic fueled the need to better support remote workplaces and ecosystems. But the events of the last year have placed a heavy burden on global network infrastructures. Business traffic has shifted from dedicated business networks to shared residential circuits, Burns points out. “The distance and destination that network traffic must travel to connect a remote worker to their corporate network resources will likely have changed significantly,” Burns says. “ These changes in landscape can often result in a less than desired user experience, and lead businesses to consider utilizing a different content delivery strategy.” In many cases, incorporating edge-optimized resources can provide better experiences for employees, partners, and customers.

7. Orchestrating and securing distributed assets and devices

Companies in the energy and utility sector, for example, may find value in deploying edge capabilities to enable real-time interventions for operational efficiency. Operating an oil rig can involve managing multiple legacy assets from different manufacturers. “Edge-orchestration platforms can help quickly connect and disconnect heterogenous devices (each with different interfaces and communication protocols) and achieve a zero-touch and zero-trust management of these devices,” says ISG’s Andhare.

For remote operations with limited or no connectivity to the cloud – say, deep in a mine or out in an agricultural field ­– edge computing can perform real-time operational decisions based on local analysis of sensor data, Andhare points out.

Many IoT sensors used in warehouses, factories, fields, and vehicles have adopted an asynchronous data model approach.

“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, without the need to interact with any other resource in real-time,” says SPR’s Burns. “Field sensors that can analyze soil and moisture data themselves can provide a more instantaneous response to control equipment, providing more value than sensors that rely on a cloud-dependent approach to perform compute actions.”

Here, there is a healthcare corollary as well. Edge solutions can transform patient care for remote individuals, using wearable IoT devices.

[ Want to learn more about edge and data-intensive applications? Get the details on how to build and manage data-intensive intelligent applications in a hybrid cloud blueprint. ]

Stephanie Overby is an award-winning reporter and editor with more than twenty years of professional journalism experience. For the last decade, her work has focused on the intersection of business and technology. She lives in Boston, Mass.


digital technologies like IoT, Data Analytics, AI can help you to utilize advanced digital solutions to automate the data collection, process integrations using real-time data insights to adapt and meet your ever-evolving customer needs. Hakuna Matata, we look at digital transformation holistically to help companies like yours leverage the potential of digital technologies to the fullest.

Social Media Share Icons