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Speed of the Essence for ‘Machine Data’ Analytics
The ability to automate the analysis of “machine data” is most widely leveraged to speed IT operations along with use cases such as data security, a new industry survey finds, while accelerating the analysis of ephemeral machine data is emerging as a priority.
The market survey on the impact of machine data analytics conducted by 451 Research was commissioned by data-in-motion specialist Logtrust. The report authors defined machine data analytics as a set of technologies specifically “designed to help with the analysis of data created by machines, web servers, mobile devices, sensors and other smart devices.”
While a hefty 94 percent of the 200 IT managers surveyed said they use the data analytics to manage their IT operations, the technology is also gaining traction for analyzing the data firehouse created by the Internet of Things. An equal number (51 percent) said they use machine data analytics to crunch big data while 60 percent cited security applications as IT managers look to boost real-time threat detection and response.
Nevertheless, IT managers remain frustrated by a performance gaps in current analytics platforms as they tackle more real-time data and attempt to blend it with batch and historical data analysis. The imperative, the report’s authors note, is straightforward: “The faster you can run some analytics on data, and subsequently respond to the findings, the greater the chance of having achieved something that adds business value…”
So how fast is “fast”? More than two-thirds of survey respondents said they require a “machine real time” capability, that is, response times in the milliseconds. Just over half said they would settle for “human” real time, in the range of five seconds to five minutes latency.
While machine real-time capability is seen as essential for survival, 53 percent of those polled said their current data tools were incapable of achieving the “human” real time threshold. “It couldn’t be clearer… that faster data analytics really is better,” the report emphasized.
The survey also confirms the growing trend toward cloud-based data analytics, with 37 percent of those polled saying they run machine data analytics in the cloud. “Perceived” security risks have prevented 43 percent from moving analytics to the cloud, keeping them on-premise for now. But about two-thirds of respondents said they would move data analytics to the public cloud in the future.
The survey findings also challenged the notion that open source approaches to data analytics cost less. IT managers were evenly divided on the merits of open source versus proprietary approaches. Data platform vendors such as Logtrust, Sunnyvale, Calif., predictably argue in favor of proprietary solutions, stressing that the 451 Research survey found that 67 percent expect to adopt proprietary machine data analytics tools in the future.
As real-time data analytics becomes a necessity, the crunching of unstructured data, including images and video, also is emerging as a key application. Still, 89 percent of respondents said their main focus was analyzing and visualizing structured data.
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