Enterprises and public sector organizations are drowning in real-time data from equipment, assets, suppliers, employees, customers, and city and utility data sources. The hidden insights buried within this data has the potential to optimize production, transform efficiency, and streamline flows of goods and services - but finding insights cost effectively remains a challenge. It is important to cost-effectively learn on data at the “edge” as it is produced. Enterprises need an architecture for learning on time-series data at the edge, using commodity hardware to create an efficient fabric of edge devices.
Businesses are rapidly adding new devices, tags, sensors, automation, software and compute elements to help track and optimize business performance – but are rarely able to act on the data generated by them. Centralized cloud systems struggle to scale when enterprise applications aggregate data from multiple local networks, and then must sync state throughout the application. Enterprises want to improve efficiency, cut costs, prevent failures, track assets and improve safety – driving a need to analyze and act on streams of data from their assets, customers, users, infrastructure and operations. However, the cloud-first paradigm is insufficient to deliver maximum value from data analytics investments. The time and cost of shifting data to a central location, storing it, and writing new apps to analyze it before a decision can be taken results in only a small subset of new data being made available, and often delivers insights too late to act on them.
The amount of edge data being generated globally is growing exponentially, creating both opportunity and challenges for data-rich enterprises. While analytics technologies continue to improve, IDC Research predicts that only 15% of data will be usefully tagged by the year 2025. In order to maximize the value of enterprise data, software must move to the data source. Making intelligence capabilities available at the edge can realize massive efficiency gains, and significantly lower operating costs for edge applications.
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.
You’ve come to the conclusion that your business needs an edge computing strategy. You’re buried under a mountain of sensor data and your existing business systems just can’t keep up. Edge computing will enable you to take advantage of real-time analytics to reduce costs, prevent equipment failure, and improve visibility into business processes, but you’re just not sure how to get started.
We've put together this Infographic to share our learnings on edge computing for massive real-time apps. Edge Computing enables massive scale Smart Cities applications. Distributing application intelligence to each Edge Device allows Smart City applications to dynamically compose multiple data sources into real-time experiences.
One of the biggest challenges with monitoring the health of industrial equipment is in transforming torrents of raw, unstructured machine data into useful insights about maintenance needs, system performance, and anomaly detection. Traditional methods of “store everything now, ask later” can overwhelm networks and expose bottlenecks which delay the discovery of insights until it is too late. In order to capture insights about machine health and react to those insights in a timely manner, it is critical that machine data be processed at the edge.
Edge Computing is at a stage where discussions about how Edge Computing can solve business problems happen in the abstract. Vendors tout their benchmark statistics, system integrators roll out new capabilities, but enterprises are left to guess how technologies like Edge Computing will work with their machines and other business objects. In this post, we’ll use the example of a manufacturing facility to demonstrate the concrete differences between Edge Computing/Hybrid and cloud-only OT applications. We look at the Part, Machine, Composite, and System levels to see how Edge Computing affects how applications interact with the entire hierarchy of an industrial deployment.
As 2017 draws to a close, the Swim team wanted to look forward to 2018 and the changes we’ll see in the IIOT technology landscape in the New Year. This year, major industry analysts like Gartner, Forrester, Frost & Sullivan, and many others have highlighted Edge Computing as an increasingly important piece the IIOT puzzle. Edge deployments benefit from significantly decreased latency of data delivery, while providing a distributed computing network which ensures more consistent uptime. Pairing Edge Computing technologies with other recent advancements in Machine Learning and streaming analytics can enable industrial enterprises to transform raw sensor data at the edge into real-time insights structured for use in IT and OT applications. In this post we’ll make some predictions about both the industrial IT and OT landscape in 2018, and explain why Edge Learning will see wide adoption in the New Year.
We've put together this Infographic to share our learnings on edge analytics: insights at the source. Learn more about Industrial Transformation, Fast Data Analytics Adoption, and Technology Investment Priorities in our Infographic: