Initiate Your Edge Computing Journey

The first step of your journey must be taken in the right direction.

Analyst Perspective

Ensure you have the right use cases to take advantage of edge computing.

Whether you are making a point-of-sale (POS) transaction halfway across the country from a retailer's primary data center, running Internet of Things (IoT) devices on an oil rig or a manufacturing plant, or managing the moisture in your soil for optimum crop yields through sensors, edge computing is all around us.

So why is edge computing so important in current computing infrastructure strategies? The standard problem statements include latency, connectivity issues, and cost constraints. However, many businesses, including those in the manufacturing, retail, healthcare, and energy industries, are taking advantage of the real time processing, analytical, and decision-making capabilities that come with processing data at the edge instead of uploading it to a data center or cloud infrastructure.

In a data driven world, edge computing could give your business a competitive advantage by reducing latency while increasing privacy and reliability. But ensure you have all the relevant use cases and that the need is business-driven to accomplish that competitive advantage with your industry peers.

John Donovan
Principal Research Director, I&O Practice
Info-Tech Research Group

Insight Summary

Strategic Alignment for Value

Before diving into edge computing, ensure that your initiative aligns with your organization's overall strategy and goals. Identify the specific business challenges you aim to address through edge computing, whether reducing latency, improving real-time decision making, or enhancing user experiences. This alignment will ensure that your edge computing journey contributes meaningful value to your organization.

Start With a Use Case

Begin your journey by identifying a clear and well-defined use case that can benefit from edge computing. Avoid trying to implement edge computing across the entire organization at once. Instead, focus on a specific scenario where edge computing's low latency and real-time processing capabilities offer a distinct advantage. Starting small allows for easier testing, validation, and learning before scaling up.

Ecosystem Collaboration

Edge computing often involves a diverse ecosystem of hardware, software, networking, and data components. Collaborate with relevant stakeholders including IT teams, data scientists, developers, and business units. Engaging experts from various domains ensures a comprehensive understanding of the technical requirements and business value of your edge computing solution.

Executive Summary

Info-Tech Insight
Rather than adopting edge computing as a generic technology solution, focus primarily on identifying and tailoring solutions to specific use cases.

Define edge

What is edge computing?

Edge computing is a computational model that relates to various networks and devices located near the end-users. At its core, edge computing involves processing data in close proximity to its source, resulting in accelerated data processing at higher capacities. This, in turn, produces immediate and action-oriented outcomes in real time.

Edge devices: Everyday devices such as smart speakers, watches, and phones already incorporate edge computing capabilities. These devices collect and process data locally while interacting with the physical environment. Likewise, IoT devices, POS systems, robots, vehicles, and sensors can function as edge devices provided they conduct local computations and maintain connections with the cloud.

Network edge: Although it can be hosted on individual edge devices or routers, edge computing does not necessitate the establishment of a distinct "edge network. In cases where a separate network is employed, it simply represents another point between end users and the cloud. This is where the significance of 5G technology becomes evident. 5G offers robust wireless connectivity for edge computing, characterized by low latency and high cellular speeds. This convergence opens the door to exciting possibilities, including autonomous drones, remote telesurgery, smart city initiatives, and more. The network edge is particularly valuable in scenarios where deploying on-premises computing is cost-prohibitive and complex and the demand for high responsiveness makes distant cloud computing impractical.

Edge computing is a growing technology stack

• The global edge computing market size was valued at $11.99 billion in 2022 and is projected to grow from $15.96 billion in 2023 to $139.58 billion by 2030 (Fortune Business Insights, 2023).
• IDC predicts that by 2023 more than 50% of new IT infrastructure will be deployed at the edge.
• Nearly 70% of enterprises are fast-tracking edge adoption to gain a competitive edge, with many utilizing edge to tackle critical business issues (ITPro Today, 2023).

By 2023, 50% of new IT infrastructure will be deployed at the edge.
Source: IDC, 2020

Seventy percent of enterprises are fast tracking edge adoption for competitive advantage.
Source: ITPro Today, 2023

Edge decision making

Initiate your edge computing journey decision map

Important factors when deciding if you need edge computing

"As organizations embrace edge computing and embark on deployment of edge-specific infrastructure and operations platforms, they can realize the full potential of edge computing with a collaborative ecosystem that empowers frontline workers, drives innovation, institutes best practices, and ensures a successful digital transformation journey with respect to industry 4.0 and 5.0 capabilities."
- Madhu Gaganam, SME, Dell Edge

Info-Tech Insight
Define your decision making and understand what your competitors are doing to prove a competitive advantage while mitigating risk.

Benefits of edge computing

Edge computing is important for several reasons. It addresses various challenges and provides significant benefits in everchanging computing environments.

• Reduced Latency: Edge computing brings data processing closer to the data source, minimizing the time it takes for data to travel to centralized data centers and back. This reduced latency is crucial for applications that require real-time responses, such as IoT devices, autonomous vehicles, industrial automation, and augmented reality.
• Bandwidth Optimization: When processing data locally at the edge, only relevant information needs to be sent to the central cloud, reducing the amount of data transferred over networks. This optimization is particularly valuable in scenarios with limited bandwidth or high data transmission costs.
• Scalability and Distributed Processing: Edge computing supports distributed processing, allowing applications to scale horizontally by adding more edge nodes. This is especially useful for handling spikes in data volume or computation demand.
• Local Insights and Decision-Making: Edge computing allows organizations to extract insights and make decisions locally, without needing to transmit all data to central servers. This is valuable in situations that require immediate action, such as predictive maintenance in industrial settings.
• Offline and Intermittent Connectivity: Edge computing enables applications to operate offline or with intermittent connectivity by processing data locally. This is essential for remote areas, industrial sites, and transportation systems where continuous connectivity cannot be guaranteed.

"Edge Computing may be the key to mastering data complexity and eliminating the need to compromise in IT and OT networking."
- Felix Wildemann
Product Management Industrial PC
Beckhoff Automation

Edge computing architecture

Edge computing is a continuum and not just a project.

Diverse Applications Spanning Industries: Edge computing applications span across sectors like manufacturing, healthcare, transportation, energy, retail, and more. Each industry leverages edge computing to address specific challenges and gain real-time insights that lead to improved efficiency, cost savings, and enhanced user experiences.

Edge computing use cases

"Edge computing will unlock the potential of the Fourth Industrial Revolution, catalyzing advancements in autonomous systems, machine learning, the Internet of Things (IoT), and more."
- Satya Nadella, CEO Microsoft

How Manufacturers Can Maximize Edge Computing, Industry 4.0

Case study

INDUSTRY
Electronics Manufacturing

SOURCE
Infopulse
Daihen Corporation

Intelligent defect inspection, powered by AI and neural networks, can ramp up productivity by 50% and improve defect detection by 90%, compared to human-led inspections.

Build your edge team

Hardware engineers, software developers, data scientists, and operations teams play distinct but interconnected roles to ensure the successful design, implementation, and management. Here's an overview of each group's role:

1. Hardware Engineers:

• Design Edge Devices: Hardware engineers design the physical edge devices and sensors that collect data at or near the source.
• Select Components: They choose the appropriate hardware components, such as processors, memory, and communication interfaces, based on the specific requirements of the edge application.
• Optimize for Efficiency: Hardware engineers work to ensure that edge devices are energy-efficient and capable of withstanding the environmental conditions of their deployment locations.
• Scalability: They design hardware with scalability in mind, allowing for easy expansion of edge infrastructure as needs grow.

2. Software Developers:

• Edge Software Development: Software developers create the applications, firmware, and software that run on edge devices. This includes developing code for data processing, analytics, and real-time decision-making.
• Edge Device Management: They build tools and interfaces for managing and configuring edge devices remotely.
• Application Integration: Software developers integrate edge applications with cloud services and backend systems.
• Security: They implement security measures to protect edge devices and data from threats and vulnerabilities.

3. Data Scientists:

• Data Analysis: Data scientists analyze the data collected by edge devices to extract meaningful insights and patterns.
• Machine Learning: They develop machine learning models for predictive analytics and anomaly detection, enabling real time decision-making at the edge.
• Data Preprocessing: Data scientists prepare data for analysis by cleaning, transforming, and feature engineering.
• Continuous Improvement: They improve data models and algorithms to enhance the accuracy and efficiency of edge computing applications.

4. Operations Teams:

• Deployment and Maintenance: Operations teams are responsible for deploying and maintaining edge devices across various locations.
• Monitoring: They continuously monitor the health and performance of edge infrastructure, ensuring that devices are operational and data flows smoothly.
• Issue Resolution, Scalability and Resource Management: Operations teams manage the scalability of edge infrastructure, allocating resources as needed to accommodate changing workloads. Operations teams troubleshoot any hardware or software issues that may arise at the edge.
• Security and Compliance: Operations teams implement and enforce security protocols and compliance measures to protect edge computing environments.

Navigate the practical aspects of your edge computing journey

Build a strong foundation

Projects for Proof of Concept:
Launch small-scale pilot projects to validate the feasibility and benefits of edge computing for your chosen use cases. These pilots allow you to test hardware, software, connectivity, and performance in a controlled environment before full-scale deployment. Learn from these pilots to refine your strategy and address any unforeseen challenges.

Select Appropriate Hardware and Connectivity:
The hardware and connectivity selected will depend on many factors, including the environment in which the device or edge node will be located and the type of network connectivity available (e.g. 5G, SDN, Bluetooth, etc.). Think latency and speed.

Implement Effective Data Management:
Develop a robust data management strategy that encompasses data collection, storage, processing, and transmission. Determine which data should be processed locally and which should be sent to the cloud. Consider data synchronization mechanisms to ensure consistency between edge and central systems.

Info-Tech Insight
The success of your edge computing journey hinges on your understanding the nuances of each use case and crafting solutions that align with specific objectives. While the technology itself is powerful, its real impact comes from its strategic application to solve specific problems in the most effective way.

Overcome obstacles by working toward the motivation and opportunities

Complexities of edge solutions

Info-Tech Insight

1. Complex Integration: Integrating edge computing infrastructure with existing systems and applications can be complex and requires specialized expertise. Ensuring seamless connectivity and interoperability is a significant challenge.
2. Data Security Concerns: Edge devices are often deployed in vulnerable remote locations, making them susceptible to physical attack or tampering. Protecting data at the edge from unauthorized access and ensuring data encryption is a persistent challenge.
3. Scalability and Management: As edge deployments grow, managing distributed networks of edge devices becomes increasing difficult. Ensuring scalability while maintaining centralized management and monitoring is a complex task that requires careful planning and resources.

Transformative potential of edge

Tailored solutions for specific use cases lead to optimized impact.

• Optimize Resources: Edge computing resources, such as processing power and storage, can be allocated precisely where they're needed, improving efficiency and reducing unnecessary costs.
• Enhance Performance: Customized edge solutions can be fine-tuned for specific tasks, ensuring faster response times, reduced latency, and improved overall performance.
• Address Critical Needs: By understanding the specific pain points of a use case, you can design edge solutions that directly address the most critical needs, delivering the greatest value.
• Minimize Complexity: Focusing on targeted use cases helps prevent overengineering and unnecessary complexity, making implementation and management more streamlined.
• Prove Value Quickly: Tailored solutions are often quicker to implement and validate, allowing you to demonstrate the value of edge computing sooner.
• Enable Innovation: Encourage innovative thinking by adapting edge computing capabilities to unique challenges, potentially leading to breakthrough solutions.

Concentrating on tailored solutions

"Rather than adopting edge computing as a generic technology solution, the main focus should be on identifying and tailoring solutions to specific use cases."
- John Donovan, Principal Research Director, I&O Practice, Info-Tech Research Group

Strategic importance of edge computing

76% ... say their edge investment is directly linked to streamlining and digitizing business processes.
Source: NTT Data, 2023

Seventy-four percent say their edge investment is directly linked to reducing the workforce and automating tasks.
Source: NTT Data, 2023

What global enterprises say about gaining an edge advantage.

"Edge computing empowers businesses and industries to harness the power of data and computation in real-time, while overcoming the limitations posed by latency, bandwidth, and connectivity issues in traditional cloud-centric models."
- PJ Ryan, Research Director, Info-Tech Research Group

Info-Tech Insight
The shift from centralized (cloud/data center) models to a more decentralized (edge) model can lead to faster, more efficient, and more resilient systems.

Artificial intelligence and edge computing

Artificial intelligence and edge computing often go hand in hand to create powerful and responsive systems. Edge computing enhances AI applications by enabling them to process data closer to the source, reducing latency and allowing for real-time decision making. This synergy is particularly valuable in scenarios where immediate insights and actions are critical. Here are some examples of AI and edge computing working together:

1. Smart Cameras and Surveillance:
Edge devices equipped with AI-powered cameras can analyze video streams locally to detect anomalies, such as intruders or suspicious behavior, without needing to send every frame to a central server. This reduces network traffic and enables quicker responses by triggering alerts in real-time.

2. Autonomous Vehicles:
Self-driving cars rely on AI algorithms to process sensor data and make split-second decisions. Edge computing enables these vehicles to process sensor data locally, ensuring rapid responses to road conditions and minimizing reliance on cloud communication, which might introduce unacceptable latency.

3. Healthcare Monitoring:
Wearable health devices with AI capabilities can monitor vital signs in real time. Edge computing allows these devices to analyze data locally, identifying potential health issues immediately and sending only critical information to healthcare providers.

4. Retail Personalization:
Edge devices in retail stores can use AI to analyze customer behavior and preferences. This data can then be used to personalize shopping experiences in real time (e.g. providing tailored recommendations or promotions).

5. Manufacturing Quality Control:
AI-driven quality control systems in manufacturing facilities can quickly identify product defects using image recognition. Edge computing allows these systems to process images locally, reducing the time between inspection and corrective action.

6. Natural Language Processing (NLP):
AI models for speech recognition or language translation can be deployed at the edge to enable real-time communication without relying on the cloud.

Case study

How manufacturers can maximize edge computing, Industry 4.0

INDUSTRY
Automotive

SOURCE
Infopulse
Nokia

Risks associated with edge computing

Manage these vulnerable areas in edge computing.

Data custody and security handling
Data transmitted and processed at the edge does not have the same level of physical or digital protection as data in a central location. Concerns include removal and transfer of disks to jump drives and unreliable data backup.

Login credentials and user authentication
Many edge devices use passwords that are unhardened or forgotten by operators. Others are void of passwords and policies altogether, making them especially vulnerable to hacking. Similarly, IoT devices with weak passwords can put important data at risk of theft.

Data sprawl
As edge devices and processors become pervasive in an organization with many locations (e.g. a nationwide retailer), they eventually become overrun by limitations (e.g. bandwidth overuse, IoT traffic) that jeopardize their security.

Info-Tech Insight
Edge and IoT devices are installed outside of a managed data center or cloud environment. This makes them inherently more prone to data loss or theft, both digitally and physically. Dealing with these concerns should be part of the overall project that brings the data and analysis to the source. IT architects should be aware of this threat.