Edge Computing in 2020 - Business Applications and Implementation Strategies

Edge computing is rapidly moving from an emerging technology to a business imperative. By processing data closer to where it's created rather than sending it to distant cloud data centers, organizations can achieve faster response times, reduced bandwidth costs, and enhanced privacy. This comprehensive guide explores the current state of edge computing in 2020, highlighting practical business applications, implementation approaches, and how organizations can leverage this technology to create competitive advantage.

The Current State of Edge Computing

Where the technology stands today:

• Commercial Maturity: Moving from experimental to production deployments
• Ecosystem Development: Growing vendor and partner landscape
• Infrastructure Evolution: Purpose-built edge hardware and software
• Standards Emergence: Beginning of interoperability frameworks
• Investment Acceleration: Significant funding across the edge value chain

These developments are shaping how organizations approach edge computing implementation in 2020 and beyond.

Edge Computing Fundamentals

Key concepts for business leaders:

1. Edge Computing Architecture

Understanding the edge continuum:

• Device Edge: Computing on IoT devices and sensors
• Local Edge: Processing in on-premises gateways and servers
• Network Edge: Computing within telecommunications infrastructure
• Regional Edge: Distributed data centers closer to users
• Cloud Integration: Coordination with centralized cloud resources

Business Impact: This distributed architecture enables organizations to place computing resources where they create the most value, balancing performance, cost, and management considerations.

2. Edge Computing vs. Cloud Computing

Complementary approaches:

Key Differences

• Location: Edge (distributed) vs. Cloud (centralized)
• Latency: Edge (milliseconds) vs. Cloud (tens to hundreds of milliseconds)
• Bandwidth Usage: Edge (reduced) vs. Cloud (higher)
• Processing Scope: Edge (local data) vs. Cloud (aggregated data)
• Availability Requirements: Edge (autonomous operation) vs. Cloud (connected operation)

Business Impact: Most organizations will implement hybrid architectures that leverage both edge and cloud computing, with edge handling time-sensitive, bandwidth-intensive workloads and cloud managing aggregation, advanced analytics, and long-term storage.

3. Edge Computing Technologies

Key enabling capabilities:

Core Technologies

• Edge Hardware: Purpose-built devices and servers
• Edge Software Platforms: Operating systems and management tools
• Edge AI: Machine learning at the edge
• Edge Networking: Connectivity and data transfer
• Edge Security: Protecting distributed infrastructure

Business Impact: These technologies are becoming more integrated and easier to deploy, reducing the complexity of implementing edge computing solutions.

4. Edge Computing Economics

Understanding the business case:

Economic Factors

• Bandwidth Savings: Reduced data transmission costs
• Latency Improvements: Value of faster response times
• Operational Resilience: Continued function during connectivity issues
• Hardware Utilization: Leveraging existing infrastructure
• Deployment and Management Costs: Distributed system considerations

Business Impact: Edge computing can deliver significant ROI through both cost reduction (bandwidth, cloud storage) and value creation (new capabilities, improved experiences), but requires careful analysis of the full lifecycle costs.

High-Value Business Applications

Practical edge computing use cases:

1. Industrial IoT and Manufacturing

Enhancing production environments:

Key Applications

• Predictive Maintenance: Real-time equipment monitoring
• Quality Assurance: Automated visual inspection
• Worker Safety: Environmental and behavioral monitoring
• Digital Twins: Real-time virtual representations of physical assets
• Autonomous Systems: Self-operating production equipment

Implementation Example: Siemens has implemented edge computing in its manufacturing facilities to process data from thousands of sensors locally, reducing latency from 100+ milliseconds to less than 10 milliseconds while cutting cloud data transfer by 80%.

2. Retail and Customer Experience

Transforming physical locations:

Key Applications

• Computer Vision Analytics: Understanding customer behavior
• Personalized Experiences: Real-time customer recognition
• Inventory Management: Automated stock monitoring
• Smart Checkout: Frictionless purchasing
• In-Store Digital Experiences: Interactive displays and assistance

Implementation Example: Walmart is using edge computing to power computer vision systems that monitor inventory levels in real-time, automatically alerting staff when restocking is needed and reducing out-of-stock incidents by 30%.

3. Smart Cities and Infrastructure

Enhancing urban environments:

Key Applications

• Traffic Management: Real-time flow optimization
• Public Safety: Video analytics for incident detection
• Environmental Monitoring: Air quality and noise pollution tracking
• Utility Optimization: Smart grid and water management
• Connected Infrastructure: Bridges, roads, and building monitoring

Implementation Example: Barcelona has deployed edge computing nodes throughout the city to process data from thousands of IoT sensors, enabling real-time traffic management that has reduced congestion by 21% and cut emergency response times by 25%.

4. Telecommunications and Media

Enhancing network services:

Key Applications

• Content Delivery: Caching and streaming optimization
• Virtual Network Functions: Software-defined networking
• Real-Time Analytics: Network performance optimization
• Mobile Edge Computing: Enhanced mobile applications
• Augmented and Virtual Reality: Low-latency immersive experiences

Implementation Example: Verizon is deploying edge computing capabilities within its 5G network that reduce latency from 100+ milliseconds to less than 20 milliseconds, enabling new applications like cloud gaming, AR/VR, and real-time collaboration.

Industry-Specific Edge Applications

How different sectors are leveraging edge computing:

1. Healthcare and Life Sciences

Improving patient care:

• Medical Device Integration: Real-time monitoring and alerts
• Telemedicine Enhancement: High-quality remote consultations
• Imaging Processing: Local analysis of medical images
• Patient Monitoring: Continuous health tracking
• Pharmaceutical Research: Distributed data processing

Example: GE Healthcare has implemented edge computing in its medical imaging devices, enabling preliminary analysis to happen within seconds at the point of care, helping physicians make faster decisions while reducing bandwidth requirements for image transfer.

2. Transportation and Logistics

Optimizing movement of goods and people:

• Fleet Management: Real-time vehicle monitoring
• Autonomous Vehicles: Local processing for navigation
• Predictive Logistics: Anticipating supply chain needs
• Asset Tracking: Location and condition monitoring
• Transportation Infrastructure: Smart roads and traffic systems

Example: Maersk is using edge computing on container ships to process IoT sensor data from refrigerated containers, automatically adjusting conditions and alerting crew to potential issues without requiring satellite connectivity.

3. Energy and Utilities

Managing critical infrastructure:

• Grid Optimization: Real-time power management
• Renewable Integration: Managing distributed energy resources
• Predictive Maintenance: Monitoring generation and distribution equipment
• Outage Management: Faster detection and response
• Energy Efficiency: Optimizing consumption patterns

Example: Duke Energy has deployed edge computing at substations to process data from thousands of sensors, enabling real-time grid management that has improved reliability by 15% while reducing operational costs.

4. Financial Services

Enhancing security and experiences:

• Fraud Detection: Real-time transaction monitoring
• Branch Transformation: Enhanced in-person experiences
• ATM Intelligence: Advanced functionality and security
• Trading Optimization: Reduced latency for transactions
• Physical Security: Advanced monitoring and access control

Example: JPMorgan Chase is using edge computing in its trading operations to process market data locally, reducing latency by crucial milliseconds and improving trading algorithm performance.

Edge Computing Implementation Approaches

Strategies for successful adoption:

1. Edge Strategy Development

Creating a comprehensive approach:

Strategy Elements

• Use Case Prioritization: Identifying high-value applications
• Architecture Planning: Designing the edge-to-cloud continuum
• Technology Selection: Choosing appropriate solutions
• Deployment Phasing: Implementing in manageable stages
• Operating Model Definition: Managing distributed infrastructure

Best Practice: Develop a comprehensive edge strategy that aligns with broader digital transformation initiatives, focusing on specific business outcomes rather than technology for its own sake.

2. Edge Infrastructure Planning

Building the foundation:

Infrastructure Considerations

• Hardware Requirements: Processing, storage, and networking needs
• Software Platform Selection: Management and orchestration tools
• Connectivity Planning: Network requirements and redundancy
• Physical Environment: Space, power, and cooling considerations
• Security Architecture: Protecting distributed systems

Best Practice: Implement standardized edge infrastructure configurations where possible to simplify deployment and management, while ensuring sufficient flexibility to meet varying use case requirements.

3. Edge Data Management

Handling distributed information:

Data Management Approaches

• Data Processing Rules: Determining what to process where
• Data Synchronization: Keeping edge and cloud in harmony
• Data Retention Policies: Managing local storage constraints
• Data Privacy Controls: Ensuring regulatory compliance
• Data Quality Management: Maintaining accuracy across locations

Best Practice: Develop clear data management policies that specify what data should be processed at the edge, what should be sent to the cloud, and how data consistency will be maintained across distributed environments.

4. Edge Security Implementation

Protecting distributed systems:

Security Elements

• Physical Security: Protecting edge hardware
• Network Security: Securing communications
• Device Authentication: Ensuring legitimate connections
• Data Protection: Encryption and access controls
• Security Monitoring: Detecting and responding to threats

Best Practice: Implement defense-in-depth security that addresses the unique challenges of edge environments, including physical access risks, network exposure, and resource constraints.

Edge Computing Technology Ecosystem

The developing edge landscape:

1. Edge Hardware Providers

Physical infrastructure components:

Key Players

• Dell Technologies: Edge servers and gateways
• HPE: Edgeline converged edge systems
• Cisco: IoT and industrial networking equipment
• NVIDIA: GPU-enabled edge computing platforms
• Intel: Edge-optimized processors and reference designs

Strategic Approach: Hardware providers are creating purpose-built edge solutions that combine computing, storage, networking, and management capabilities in form factors appropriate for various edge environments.

2. Edge Software Platforms

Management and orchestration tools:

Leading Platforms

• Microsoft Azure IoT Edge: Cloud-to-edge extension
• AWS Greengrass: Edge runtime for AWS services
• Google Anthos: Kubernetes-based hybrid platform
• VMware Edge Compute Stack: Virtualization for the edge
• Red Hat OpenShift: Container platform for edge deployments

Strategic Approach: Software platforms are focusing on consistent management across edge and cloud environments, with increasing emphasis on containerization and Kubernetes for application deployment.

3. Telecommunications Edge

Network provider capabilities:

Telco Approaches

• Multi-access Edge Computing (MEC): Computing within cellular networks
• 5G Integration: Edge capabilities within next-gen wireless
• Network Slicing: Dedicated virtual networks for edge applications
• Telco Cloud Transformation: Virtualized network infrastructure
• Edge Colocation: Distributed data center facilities

Strategic Approach: Telecommunications providers are leveraging their distributed infrastructure to offer edge computing capabilities that complement their connectivity services, particularly in conjunction with 5G deployments.

4. Edge AI and Analytics

Intelligent edge capabilities:

AI Approaches

• Edge AI Frameworks: Optimized machine learning tools
• Model Optimization: Techniques for efficient edge deployment
• Federated Learning: Distributed model training
• Computer Vision at the Edge: Visual processing capabilities
• Natural Language Processing: Voice and text analysis

Strategic Approach: AI at the edge is focusing on optimized models that can deliver high performance within the constraints of edge devices, with increasing capabilities for on-device training and adaptation.

Overcoming Edge Computing Challenges

Addressing common implementation obstacles:

1. Edge Management Complexity

Handling distributed systems:

• Challenge: Difficulty managing numerous remote devices
• Solutions:
  • Implement centralized management platforms
  • Adopt automation for routine operations
  • Develop standardized configurations and templates
  • Create clear operational procedures and responsibilities
  • Leverage remote monitoring and management tools

Example: Target has implemented a standardized edge computing platform across its 1,900+ stores with centralized management that enables remote deployment, monitoring, and updates, reducing operational costs by 35% compared to its previous store technology approach.

2. Edge-Cloud Integration

Creating seamless environments:

• Challenge: Coordinating processing across edge and cloud
• Solutions:
  • Implement consistent platforms across environments
  • Develop clear data synchronization mechanisms
  • Create application designs that function in both contexts
  • Establish connectivity redundancy and failure handling
  • Use containerization for deployment flexibility

Example: Royal Dutch Shell has implemented a hybrid edge-cloud architecture for its oil and gas operations that processes critical control data locally while seamlessly integrating with cloud systems for analytics and optimization, improving operational reliability while reducing costs.

3. Edge Security and Compliance

Protecting distributed assets:

• Challenge: Securing numerous remote systems with physical access risks
• Solutions:
  • Implement zero-trust security models
  • Deploy hardware-based security features
  • Create automated security monitoring and response
  • Develop comprehensive device lifecycle management
  • Establish clear data residency and compliance controls

Example: Johnson & Johnson has implemented a comprehensive edge security framework for its manufacturing facilities that includes hardware root of trust, network segmentation, and continuous monitoring, meeting strict regulatory requirements while enabling advanced IoT capabilities.

4. Edge Skills and Expertise

Building necessary capabilities:

• Challenge: Shortage of expertise in edge technologies
• Solutions:
  • Develop internal training programs
  • Partner with experienced system integrators
  • Leverage vendor professional services
  • Create centers of excellence for knowledge sharing
  • Implement simplified edge platforms to reduce complexity

Example: Procter & Gamble established an edge computing center of excellence that provides standardized architectures, training, and support to business units, accelerating adoption while ensuring consistent implementation across the organization.

Measuring Edge Computing Success

Approaches to evaluating effectiveness:

1. Performance Metrics

Assessing technical improvements:

• Latency Reduction: Decreased response times
• Bandwidth Optimization: Reduced data transfer
• Processing Efficiency: Computational performance
• Availability Improvements: Enhanced uptime
• Scalability Achievements: Ability to handle growing demands

Best Practice: Establish clear baseline measurements before implementation and track improvements over time, focusing on metrics most relevant to your specific use cases.

2. Operational Impact Metrics

Measuring business process improvements:

• Process Efficiency: Time and resource savings
• Quality Improvements: Reduction in errors and defects
• Asset Utilization: Improved use of equipment and resources
• Workforce Productivity: Enhanced employee effectiveness
• Operational Resilience: Ability to function during disruptions

Best Practice: Identify specific operational KPIs that edge computing should influence and measure both before and after implementation to quantify the direct business impact.

3. Financial Metrics

Assessing business outcomes:

• Cost Reduction: Savings from bandwidth, cloud services, etc.
• Revenue Enhancement: Increased sales from new capabilities
• Capital Efficiency: Better utilization of infrastructure investments
• Time-to-Market Improvement: Faster deployment of new offerings
• Return on Investment: Overall financial benefits versus costs

Best Practice: Create a comprehensive business case that captures both direct cost savings and broader value creation, recognizing that some benefits may be difficult to quantify precisely.

4. Strategic Value Metrics

Evaluating long-term impact:

• New Capabilities: Previously impossible functions enabled
• Customer Experience Enhancement: Improved satisfaction and loyalty
• Competitive Differentiation: Advantages over market alternatives
• Innovation Acceleration: Faster development of new offerings
• Business Model Evolution: Fundamental changes in operations

Best Practice: Look beyond immediate operational improvements to assess how edge computing enables strategic transformation and creates sustainable competitive advantage.

The Future of Edge Computing: 2020 and Beyond

Emerging trends and developments:

1. Edge AI Evolution

Advancing intelligence at the edge:

• On-Device Learning: Local model training and adaptation
• Collaborative Intelligence: Coordinated learning across devices
• Neuromorphic Computing: Brain-inspired processing architectures
• Tiny Machine Learning (TinyML): AI on ultra-constrained devices
• Explainable Edge AI: Understanding automated decisions

Strategic Implication: Edge AI will become increasingly sophisticated and autonomous, enabling devices to learn and adapt to their environments without constant cloud connectivity.

2. Edge-Native Applications

Software designed for distributed environments:

• Distributed Application Architectures: Functions spanning locations
• Mesh Applications: Peer-to-peer coordination
• Event-Driven Design: Responsive to real-world triggers
• Offline-First Functionality: Operating without connectivity
• Context-Aware Behavior: Adapting to local conditions

Strategic Implication: Applications will increasingly be designed specifically for edge environments rather than adapted from cloud or on-premises models, taking full advantage of edge capabilities.

3. 5G and Edge Convergence

Network and compute integration:

• Network Slicing for Edge: Dedicated virtual networks
• Dynamic Resource Allocation: Adapting to changing demands
• Edge Service Discovery: Finding nearby computing resources
• Seamless Handoff: Maintaining sessions across locations
• Network-as-a-Service APIs: Programmatic access to capabilities

Strategic Implication: The combination of 5G and edge computing will enable new classes of applications requiring both mobility and low latency, from autonomous vehicles to augmented reality.

4. Edge Ecosystem Standardization

Moving toward interoperability:

• Reference Architectures: Standard implementation patterns
• Open Interfaces: Consistent APIs across platforms
• Edge Marketplaces: Simplified application deployment
• Cross-Vendor Management: Unified operational tools
• Interoperability Frameworks: Seamless component integration

Strategic Implication: Standardization will reduce the complexity and risk of edge computing implementations, accelerating adoption and enabling more sophisticated multi-vendor solutions.

Conclusion: Edge Computing Imperatives for 2020

As we progress through 2020, edge computing is transitioning from experimental technology to essential business infrastructure. While challenges remain, organizations across industries are implementing edge solutions that deliver measurable value. The most successful organizations will be those that:

1. Develop a clear edge strategy aligned with broader digital transformation initiatives
2. Focus on specific business outcomes rather than technology for its own sake
3. Create seamless hybrid architectures that integrate edge and cloud appropriately
4. Implement comprehensive security that addresses the unique challenges of distributed systems
5. Build the necessary skills and partnerships to deploy and manage edge infrastructure effectively

By approaching edge computing as a strategic capability rather than merely a technical deployment, organizations can create sustainable competitive advantages while preparing for a future where computing is increasingly distributed and embedded in the physical world.

Remember that edge computing implementation is not a destination but a journey—one that requires ongoing adaptation, learning, and refinement as both technologies and business needs evolve.

This article was written by Nguyen Tuan Si, an edge computing strategy specialist with experience helping organizations develop and implement effective distributed computing solutions across various industries.