Edge Computing: 5 Potential Pitfalls—and How to Overcome Them

Edge Computing: 5 Potential Pitfalls—and How to Overcome Them

Edge computing is rapidly becoming a cornerstone of enterprise IT, bringing storage and analytics closer to data sources—especially in IoT networks. The promise of lower latency and reduced WAN costs is compelling, but the technology is still nascent, and many organizations face unforeseen hurdles.

Tech Spotlight

• Proving the value of analytics on the edge (CIO)
• The cutting edge of healthcare: How edge computing will transform medicine (Computerworld)
• Securing the edge: 4 trends to watch (CSO)
• How to choose a cloud IoT platform (InfoWorld)
• Edge computing: 5 potential pitfalls (Network World)

According to a May 2021 Grand View Research report, edge‑computing revenues were $4.68 billion in 2020 and are projected to reach $61.14 billion by 2028—an almost 13‑fold increase.

However, without real‑world performance data and a clear deployment roadmap, organizations can stumble. Below are the five most common pitfalls—and practical guidance to avoid them.

1. Choosing the Right Approach

With no established success metrics, IT leaders struggle to determine whether on‑prem, hosted, managed, or cloud‑based solutions are best for their needs. “The biggest challenge is the lack of real‑world data to guide decisions,” says Jennifer Cooke, research director of edge strategies at IDC.

“There are a myriad of options, and the coordination required—databases, applications, infrastructure, connectivity—can overwhelm many teams,” she adds. Many enterprises now partner with specialists to assemble a cohesive ecosystem and validate promised performance gains.

2. Security Risks and Expertise Gaps

Edge environments expose a large attack surface: thousands of IoT devices, distributed networks, and massive data streams. Matt Kimball, senior analyst at Moor Insights & Strategy, warns, “How can an organization achieve zero‑trust across all layers?”

“CISOs and IT executives must invest in skilled professionals who can design integrated security from device to cloud,” Kimball explains. Vendors offer tools, but the real challenge is finding talent capable of orchestrating these complex, layered defenses.

3. Managing Data and Analytics at Scale

Extracting actionable insights from edge‑generated data is a major draw, yet it is a distributed data‑management puzzle. Vijoy Pandey, vice president of engineering and CTO at Cisco, describes the edge as “a large‑scale distributed data‑management problem.”

Data science is as critical as security, Kimball notes, “The company that can rapidly turn raw data into intelligence will win.” Yet skilled data scientists are scarce, making external consulting a valuable asset.

4. Preparing the IT Infrastructure

Edge deployments demand robust networking, power, and environmental controls—often beyond what traditional office servers require. Kimball highlights that many retailers operate a few servers for remote office connectivity; edge requires far more complex, resilient systems.

“Start with vendors you already trust—Dell, HPE, Lenovo, Cisco, Supermicro—then scale as needed,” Kimball advises. Duos Technologies, which deploys automated edge systems for railways, emphasizes the need for ruggedized hardware that can survive remote, harsh environments.

5. Scaling Without Growing Complexity

When business units independently acquire edge devices for specific tasks, the result can be fragmented, inefficient infrastructure. Gil Shneorson, senior vice president of edge portfolio at Dell, stresses the importance of early IT involvement.

“IT should architect a unified, flexible infrastructure that supports multiple use cases across sites and clouds,” Shneorson says. Modernizing the edge foundation with consistent hybrid‑cloud architecture is essential to unlock data value.

By anticipating these pitfalls and partnering with experienced vendors, organizations can harness edge computing’s benefits while minimizing risk.