Data loses its value when it can’t be analyzed fast enough. Edge computing and analytics can solve the challenge for enterprises ranging from oil and gas production to banks and retailers.

Security cameras, phones, machine sensors, thermostats, cars and televisions are just a few of the items in daily use that create data that can be mined and analyzed. Add to it the data created at retail stores, manufacturing plants, financial institutions, oil and gas drilling platforms, pipelines and processing plants, and it’s not hard to understand that the deluge of streaming and Internet of Things (IoT) sensor data can — and will — very quickly overwhelm today’s traditional data analytics tools.

Organizations are beginning to look at edge computing as the answer. Edge computing consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs.

Why Data Is More Valuable at the Edge

Much like the time value of money, the time value of data means that the data you have in this second won’t mean as much a week, day or even hour from now. This coupled with the proliferation of IoT sensor, social, and other streaming data is driving organizations to use edge computing to provide the real-time analytics that impact the bottom line, or in some cases, stop a disaster from happening before it starts.

Organizations are currently reliant on large and complex clusters for data analytics, and these clusters are rife with bottlenecks including data transport, indexing and extract, as well as transform and load processes. While centralized infrastructures work for analyses that rely on static or historical data, it is critical for many of today’s organizations to have fast and actionable insight by correlating newly obtained information with legacy information in order to gain and maintain a strong competitive advantage.

An increasing amount of data is priceless in the seconds after it’s collected—consider the instance of a fraudster or hacker accessing accounts—but it loses all value during the time it takes to move it to the centralized data center infrastructure or upload it to the cloud. Losing value from that data due to slow decisions is not acceptable, especially when an edge-computing platform that eliminates moving data provides the near-instant intelligence needed. Organizations cannot afford to wait days, weeks or even months for insights from data. With data analytics at the edge, they do not have to.


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