Edge Intelligence Delivers on the Promise of Big Data

by Brad Johnson, on Jan 23, 2018 12:12:00 PM

Big Data was never going to be a universal panacea. The goals of Big Data are clear, monitor and measure enterprise business and processes, analyse and act on that data to achieve ever higher levels of efficiency and cost reductions. But it’s prohibitively costly to store every bit of data generated by a business today, especially considering that the vast majority of enterprise data being created carries little or no value. There are many insights to gain within the mountains of data being created daily, but enterprises must employ new strategies which identify actionable insights in real-time economically from streams of dark data.

The most logical way to process real-time data is at the source, leveraging edge intelligence to identify relevant insights. Ideally data should be reviewed and acted at the point of creation - at ‘the edge’ on the first device that sees the data.  Machine learning algorithms can then filter out duplicate or irrelevant data, identifying critical events and reducing the volume of data sent over local networks to be processed by downstream applications. Edge computing enables enterprise applications to process massive volumes of data in parallel, distributing the load by leveraging the compute resources of each edge device. Centralized systems can then perform higher level analysis on already-structured data, reducing buffer times and significantly reducing the amount of data that needs to be stored in the cloud or by external database systems.

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Edge Computing Enables Business to Capitalize on Big Data

Edge computing extends the benefits of the cloud to edge devices, providing full application context to all nodes in the system. Instead of stateless REST-based cloud architectures, which must query for state from the cloud before performing an operation, stateful edge computing architectures can store relevant context locally, where it can be accessed in real-time. With local state available, edge-based applications can each intelligently process data without waiting for state transfer from the cloud. This enables enterprise applications to operate independently from cloud systems, which is ideal for remote applications or other situations which may have an unreliable or limited capacity  internet connection.

Real-time access to edge data can transform enterprise operations. These real-time insights can form tight feedback loops using Machine Learning algorithms to power intelligent data reduction, predict maintenance requirements, and to improve anomaly detection. Intelligently processing data at the edge produces more manageable data volumes, making downstream applications more efficient in the process.

Real-Time Data is the Key to Unlocking Value, Minimize Costs

By intelligently reducing edge data in real-time, enterprises can identify valuable insights sooner and make those insights available wherever they are needed. Enterprises no longer have to sort through mountains of data in order to find the figurative “needle in the haystack.” Instead, intelligent edge-based systems can detect only the “needles” and forward them to downstream applications or to central cloud storage. This leads to decreased network and infrastructure costs and significantly reduces the amount of storage needed to operate enterprise applications, as only high-value insights are ever published. Most importantly, time critical insights are exposed sooner, helping staff and operators to prevent costly downtime and improve existing operations.

Edge computing unlocks the value hidden within enterprise data, and does so more efficiently and effectively than cloud-only architectures. For enterprise use cases that generate massive volumes of data at the edge, machine learning can provide the first layer of data reduction and transform edge data into more manageable data streams. These reduced streams can then be integrated to inform downstream applications like ERP systems, inventory systems, and other enterprise applications. Smart decision makers facing mountains of real-time data should turn to edge computing and machine learning in order to deliver on the full promise of Big Data.

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Learn how SWIM uses edge intelligence to deliver real-time insights from the dark data generated by connected enterprise systems.

Topics:Machine LearningStateful ApplicationsSWIM SoftwareIndustrial IOTEdge AnalyticsManufacturingAsset TrackingAsset ManagementSWIM AIEdge ComputingFog Computing