The Complete Guide to Edge Analytics

Edge analytics is one of the most disruptive technologies emerging today for companies striving to optimize real-time decisions and automation across distributed assets and processes. But what exactly does "edge analytics" mean and is it right for your business?

As a fellow data and analytics geek, I‘ll comprehensively explain everything you need to know about edge analytics in this detailed guide. I‘ll break down how it works, real-world use cases, key benefits, limitations to consider, leading solutions and providers in the space, and valuable implementation best practices.

By the end, you‘ll be an edge analytics expert! Let‘s dive in…

What is Edge Analytics?

Let‘s start with the fundamentals – a quick definition:

Edge analytics refers to collecting, processing, and analyzing data directly on local smart devices or sensors rather than transmitting vast streams of data to a centralized server or cloud.

Instead of floating all data upstream to the cloud, analysis happens on the "edge" nodes closest to the source of the data.

For example, a smart traffic camera equipped with edge analytics could process video feeds locally to analyze traffic patterns or identify accidents, without having to continuously stream high-def footage to the cloud.

Edge Analytics Diagram

Edge analytics solutions analyze data locally on devices rather than relying solely on the cloud

Edge analytics solutions typically involve an orchestration of the following core components:

Edge devices – these are the internet-connected endpoints like IoT sensors, robots, vehicles, controllers, switches, etc. that ingest and analyze data at the source location.
Edge gateways – more centralized computing units that aggregate and analyze data from multiple nearby edge devices before sending condensed insights upstream.
Edge software – refers to the AI, analytic algorithms and management software that enables real-time analytics on edge hardware.

It‘s important to note that edge analytics is not an either/or proposition versus cloud analytics. The two approaches complement each other. An optimal architecture utilizes edge analytics for localized instant insights combined with cloud analytics for broader oversight, storage and historical analysis.

Drivers for Edge Analytics Adoption

What factors are pushing more organizations to adopt edge analytics approaches? Here are some of the most impactful drivers:

1. Explosion of IoT Devices and Data Volume

Cisco predicts the number of connected IoT devices globally will grow from 7 billion in 2018 to 10 billion by 2022, generating 79.4 zettabytes of data.[1] Streaming all this data constantly across networks to centralized clouds for processing is infeasible. Edge analytics addresses this by preprocessing data locally.

2. Need for Real-Time Actionable Insights

Many business processes require instant data-driven decisions and responses by machines with virtually no latency. For example, an autonomous vehicle detecting a hazard needs to react immediately – it can‘t wait for inputs from a cloud server thousands of miles away. Edge analytics enables real-time automation.

3. Growth of Bandwidth-Intensive Technologies

Video analytics, VR/AR, digital twins, and smart factories are all fueling massive growth in data generated at the edge. 5G and WiFi 6 will provide fatter wireless pipes but bandwidth remains constrained, especially in remote/rural areas. Analyzing the firehose of data at the edge optimizes networks.

4. Maturing AI Chipsets

New specialized AI chipsets from Intel, Nvidia, Qualcomm and others can now run advanced ML inferencing right on edge devices rather than the cloud data centers. This unlocks the potential for localized analytics.

5. Emergence of Smart Cities

Municipalities are looking to edge analytics for real-time monitoring and automation across public infrastructure. For example, Chicago deployed video analytics on traffic cameras to monitor parking violations rather than stream everything to the cloud.[2]

6. COVID-19 Driven Digital Transformation

The pandemic prompted businesses to digitally transform operations in a hurry including remote monitoring of assets and supply chains. Lightweight edge analytics solutions are ideal for assets in the field.

Let‘s now dig deeper into the benefits organizations can gain by implementing edge analytics…

The 6 Biggest Benefits of Edge Analytics

1. Instant Insights for Time-Critical Automation

Edge analytics eliminates the latency of streaming all data to a distant cloud data center for processing. This enables systems to react in near real-time based on instant insights from the source data.

For example, an oil rig can monitor equipment for anomalies and trigger automated emergency stops in milliseconds before catastrophic failures occur. Waiting on slower cloud analytics could mean disaster in these scenarios.

KBC Research predicts industries will spend $12 billion annually on edge analytics solutions to achieve these real-time insights by 2023.[3]

2. Dramatically Lower Network Bandwidth Usage

Rather than flooding networks with massive raw data streams from end devices upstream to cloud servers, edge solutions preprocess and filter data locally – only transmitting the most critical event data and metadata for longer term storage and analytics in the cloud.

Cisco estimates most edge analytics solutions provide >90% reductions in bandwidth utilization. This provides tremendous cost savings, especially for remote assets where connectivity is constrained.

3. Improved Security and Data Governance

With edge analytics, raw data remains local rather than transmitted across networks to cloud platforms that can have vulnerabilities. analyze For use cases involving sensitive data like customer information in retail environments, edge analytics improves governance and reduces risk of breaches.

Gartner predicts by 2025, 75% of enterprise data will be processed at the edge rather than the cloud.[4]

4. Continuity for Distributed Assets and Operations

Edge analytics allows distributed assets like factories, rigs, hospitals and distributed energy grids to keep functioning locally even when companies experience large-scale cloud outages. Rather than being "blind" waiting for data and insights from unreachable cloud servers, edge analytics nodes allow continuity.

5. Flexible Scalability

Edge deployments make scaling analytics infrastructure more flexible and efficient for companies. Rather than centralized scaling, additional compute and storage capacity can be deployed exactly where needed – at the edges.

Emerging edge micro-data center solutions provided by firms like Vapor IO allow easy modular scaling. Vapor IO‘s solution enabled one mobile operator to reduce edge latency by 90%.[5]

6. Generate Valuable Training Data for AI

Edge devices close to real-world operating environments can collect vast datasets to continuously train and improve machine learning models faster than second hand cloud data.

For example, an edge node on a factory floor can gather images of normal and abnormal equipment far faster, cheaper and easier than trying to compile equivalent training data solely in the core cloud/data center.

Let‘s now look at a few examples of how innovative organizations across sectors are using edge analytics…

Real-World Edge Analytics Use Cases

Manufacturing

Leading auto manufacturers like Volkswagen are piloting edge analytics across factories. Computer vision algorithms running at the edge autonomously monitor production line quality – instantly flagging defective parts rather than waiting for human inspectors.[6] This near real-time feedback optimizes quality and yield.

Energy

Norway‘s Equinor is deploying edge analytics and computer vision within its offshore oil platforms and rigs. By analyzing sensor data at the edge, the systems can detect equipment issues like corrosion or irregular vibrations and perform predictive maintenance before failures.[7] This improves safety and uptime.

Retail & Marketing

Brick and mortar retailers are leveraging edge analytics locally within stores to perform real-time video analytics of customer shopping behavior such as foot traffic patterns, dwell time, queue analytics, and social distancing adherence. These instant insights delivered at the edge optimize merchandising, staffing, promotions and more.

Smart Cities

Urban municipalities are deploying intelligent traffic cameras enhanced with edge analytics rather than relying on cloud connectivity alone. Chicago, for example, runs models locally on traffic cameras that detect parking violations. This reduced bandwidth needs by 99% compared to streaming all footage to the cloud.[2]

Autonomous Vehicles

Self-driving vehicles are mission-critical edge analytics environments processing vast sensor streams and running ML models locally to enable real-time navigation and object detection. For example, TuSimple‘s autonomous truck fleet is continually trained by edge data.[8] Offloading all this to the cloud would mean disaster for these safety-sensitive applications.

Healthcare

Hospitals are piloting edge analytics for scenarios like analyzing locally-generated imaging scans to flag insights to doctors in real-time rather than waiting for batch cloud processing. Startups like healthcare.ai provide specialty edge analytics solutions for medicine.

These use cases highlight how organizations across sectors are using edge to unlock instantaneous insights. Now let‘s examine some leading solutions enabling edge analytics…

The Downsides and Challenges of Edge Analytics

While edge analytics opens up tremendous possibilities, it also brings new complexities. Here are 7 downsides and hurdles organizations must address:

1. Potential Data Loss at the Edges

With analysis on local devices, some raw data that might hold future analytical value may be discarded rather than streamed to the cloud for aggregation. This data loss at the edges could impact historical training of AI models.

2. Integration and Interoperability Challenges

There are currently no universal standards for edge hardware and software platforms. Getting components from different vendors to integrate and work together remains challenging.

3. Data Security Concerns

While local edge analysis improves security in many ways, physical edge devices in far flung locations can also present vulnerabilities hackers could exploit to infiltrate networks. Securing thousands of distributed endpoints is challenging.

4. Technical Complexity

Managing and orchestrating analytics infrastructure and data flows across thousands of remote edge nodes and the cloud is extremely complex. It requires considerable IT skills and platform maturity.

5. Talent Shortages

Most organizations lack staff with the required skills in edge analytics software development, data science, and MLOps. This talent gap can delay deployments. Investing in retraining is key.

6. Immature Edge Solutions

While the cloud analytics software market is mature, edge-native solutions remain relatively new and untested at large scale for many enterprises. Stability and reliability issues do emerge.

7. Upfront Costs

The upfront capital costs of edge hardware, supporting infrastructure, and software can be daunting, especially for smaller organizations. Building business cases can be difficult.

These downsides are not blockers for most edge analytics use cases but rather hurdles to anticipate and address from the start.

Now let‘s dig into some best practices for executing a successful edge analytics implementation…

6 Best Practices for Implementing Edge Analytics

Based on early deployments and lessons learned by pioneers, here are 6 recommendations when launching an edge analytics initiative:

1. Crawl Before You Run

Start small rather than attempting to tackle every endpoint at once. Pilot edge analytics for a targeted high impact use case. Once the benefits are proven, expand systematically to other business processes and locations.

2. Assess Maturity of Solutions

Vet potential platforms thoroughly. Solution maturity, scalability and security are imperative – especially for mission-critical processes. Keep long-term needs in mind.

3. Build An Internal Innovation Lab

Develop an internal lab environment to build skills in edge analytics development, data engineering, and MLOps. Learning is key before launching large-scale deployments.

4. Plan For Integration

Carefully design how edge systems will integrate upstream with core cloud/data center environments. Eliminate data silos between the edge and cloud.

5. Involve IT Early

Engage IT teams early to assess network readiness, bandwidth needs, security protocols, and management processes. Edge success requires IT buy-in.

6. Identify Metrics and KPIs

Define metrics like utilization rates, node downtime, model accuracy, and bandwidth reduction to continually track edge analytics performance, bottlenecks, and opportunities.

The Future of Edge Analytics

Edge analytics sits at the nexus of several inexorable technology trends – exponential growth in IoT devices, need for real-time insights, rise of smart autonomous machines, 5G rollouts.

IDC forecasts the market for edge analytics software, hardware and services will grow at a 37% compound annual growth rate to exceed $250 billion by 2024. [9]

Nearly every industry will adopt edge analytics at some level over the next decade. As solutions mature, edge analytics will become the norm – as transformative as how cloud computing evolved.

Companies that leverage edge analytics to make faster, localized data-driven decisions will gain sustained competitive advantages.

Key Takeaways and Advice

Let‘s recap the key points we covered in this comprehensive edge analytics guide:

Edge analytics moves data processing and analytics closest to the source rather than the cloud – enabling instant localized insights.
Leading drivers include IoT growth, need for real-time automation, constrained networks and maturing AI chipsets.
Major benefits span instant insights, bandwidth savings, improved security, flexible scaling and generating training data.
Manufacturing, energy, retail, transportation and healthcare are leading adopters.
Downsides like data loss, security risks, talent gaps and platform maturity must be managed.
AWS, Microsoft, Cisco, Dell and Intel offer leading solutions but the market is still nascent.
Start small with targeted use cases and build in-house skills before scaling edge analytics across the enterprise.

Here is my advice for any organization exploring edge analytics:

First, carefully identify business processes where edge analytics could dramatically improve decision speed, automation, continuity and cost savings. The use cases with the clearest payoffs should be your initial targets.

Next, work closely with your infrastructure and analytics teams to conduct small pilots. Prove the ROI before wholesale edge transformations. Monitor performance closely and keep improving.

Edge analytics undoubtedly brings new complexities – but the benefits will be game-changing. Now is the time to start developing expertise across devices, data and analytics to lead your industry.

I hope this guide has demystified the world of edge analytics for you and provided a foundation to explore further as use cases evolve! Let me know if you have any other questions.