Data and insights are valuable assets for any company embracing data driven decision making, with analytics permeating every aspect of our lives. Historically, businesses collected data from different IoT devices and sensors, gathered them in a central data repository like a data lake or data warehouse, then performed analysis to gain insights.
But what if organizations could skip the step of centralizing data and go directly to analyzing it at the source? This approach is called “edge analytics” and it enables businesses to achieve:
- Autonomous machine behavior: Devices can act independently without waiting for input from centralized analytics.
- Higher security: Less data is communicated over networks, reducing exposure.
- Lower costs: No need to store all raw data centrally or transmit it over networks.
As an expert in this field with over a decade of experience in data analytics and integration, I‘ve seen the transformative impact of edge analytics firsthand. In this comprehensive guide, we‘ll explore what edge analytics is, why it matters now, how it works, key benefits and use cases, and advice to avoid pitfalls.
What Exactly is Edge Analytics?
Edge analytics refers to performing data analysis at the edge of a network, directly on devices like sensors, switches, and other connected endpoints. Insights are generated at or near the source of data, rather than relying on a central location far from where the data originates.
With the rise of IoT technologies, businesses are collecting more data from more sources than ever before. But shuttling all that data to and from a central server for analysis is time consuming and resource intensive. Edge analytics allows for faster decentralized insights closer to the network edge where data is born.
Edge analytics shifts processing to the network edge, near data sources.
Why Edge Analytics Matters Now
Two key reasons make edge analytics crucial today:
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Need for speed: With businesses relying heavily on automated, data-driven decisions, edge analytics enables much faster response times by reducing latency. This is critical for use cases like autonomous vehicles that must react instantly.
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Data overload: The explosion of data from IoT devices is straining networks and central analytics systems. Edge analytics reduces traffic and costs by processing data on-site.
According to MarketsandMarkets, the edge analytics market will grow from $3.6 billion in 2020 to over $15.7 billion by 2025, at a CAGR of 34.1%. Top industries investing in edge analytics include manufacturing, energy, automotive, telecom, and retail.
How Edge Analytics Works
Edge analytics solutions generally follow this basic workflow:
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Data collection: Sensors and edge devices gather data as events occur.
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Edge processing: Using analytics and ML models, the data is analyzed right on the local devices.
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Action: If needed, the edge device can immediately act on the analytics, without waiting for direction.
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Data transfer: Only relevant summarized data is forwarded to centralized systems for aggregation.
For example, smart power meters with built-in analytics can monitor energy usage in real-time. If abnormal patterns are detected indicating a household appliance issue, the meter itself can instantly take action to shut off power to that device, while alerting the utility company of the anomaly.
This not only prevents hazards, but saves the time and bandwidth needed to stream all raw usage data to a central location for analysis. The edge meter handles processing locally, only sending the utility company a summary alert.
Key Benefits of Edge Analytics
Edge analytics offers significant advantages over traditional centralized analytics:
- Speed: With analysis at the data source, insights and actions are much faster. Latency is dramatically reduced.
- Lower costs: Less data transmission, storage and processing burden on central systems.
- Security: Only processed analytic output leaves the edge, minimizing exposed data.
- Scalability: Easily grow edge data sources without overloading central systems.
- Reliability: Edge systems function even if connections to central servers go down.
According to one survey, 75% of IIoT experts say faster anomaly detection is the top benefit of edge analytics. It enables real-time response vs. waiting for central analytics.
Top Use Cases and Examples
Many industries are applying edge analytics to solve key challenges:
Manufacturing
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Predictive maintenance: On-equipment sensors detect anomalies like vibration or heat build-up, allowing issues to be addressed before failure.
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Quality assurance: Computer vision inspection of products at the production line rather than manual post-production checks.
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Inventory optimization: Analytics on equipment and supply levels guide just-in-time inventory management.
Energy
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Grid optimization: Local analytics on transformers, meters and generation assets to balance and improve electricity distribution.
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Renewable optimization: Edge data from wind/solar farms enables real-time adjustments for optimal output.
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Outage prevention: Sensors identifying equipment faults or overloads allow preventative fixes.
Retail
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Inventory management: Edge devices track shelf inventory levels to automate re-ordering and optimize stock.
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Customer analysis: In-store video and traffic analysis provides real-time insight on promotions, placement, etc.
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Loss prevention: Edge analysis of video, POS and sensor data identifies shoplifting and fraud rapidly.
Public Venues
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Crowd management: Edge analytics on local cameras, mics, WiFi, etc optimizes crowd flow and resources.
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Security: Instant detection of disturbances or unauthorized access from local surveillance data.
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Cleanliness: Sensors monitor inventory and usage of soap, towels, etc. to dispatch janitorial resources.
Pitfalls to Avoid
While promising, edge analytics does come with potential pitfalls:
- Security risks if edge data/devices are compromised. Proper encryption and access controls are essential.
- Skill gaps on edge analytics and data science as adoption accelerates. Training and hiring are key.
- Siloed data if edge deployments aren‘t integrated into central analytics. Governance and architecture matter.
- Analytics debt from disconnected systems using outdated models over time. Plan for model monitoring and updates.
Leading Edge Analytics Solutions
Many vendors now offer dedicated edge analytics platforms, including:
- Microsoft Azure IoT Edge: Modules to deploy containerized analytics models locally on IoT devices.
- Google Cloud IoT Edge: Tools to manage TensorFlow models at the edge integrated with Google Cloud.
- Amazon AWS IoT Greengrass: Runs Lambda functions for analytics on edge devices while syncing with AWS cloud.
- IBM Watson Edge Analytics: Enables AI-powered analytics on edge nodes fully managed by IBM.
- SAP Analytics Edge: In-memory analytics processing embedded directly in IoT gateways and devices.
- SAS Event Stream Processing: Performs continuous analytics on geographically distributed event streams.
The Future of Edge Looks Bright
Edge analytics unlocks game-changing potential for companies across industries. As digital transformation accelerates, the ability to harness insights instantaneously at the source of data is critical to business success. While challenges exist, the benefits far outweigh the pitfalls.
With trusted partners like Aimultiple, companies can build effective edge analytics strategies leveraging the right mix of cutting-edge technologies, infrastructure, talent and governance. This powers data-driven competitive advantage today and future-proofs operations for tomorrow.
Have questions about edge analytics and where to begin? Reach out for help crafting the perfect edge solutions for your unique needs.
