Getting more value from data with machine data analytics
For the study, commissioned by technology company Logtrust, 451 Research surveyed 200 IT executives responsible for advising on or directly choosing machine data analytics, which the firm defines as “the relatively new breed of technologies that are specifically designed to help with the analysis of data created by machines, Web servers, mobile devices, sensors and other smart devices.”
The goal of the research was to understand the current state of adoption and maturity of machine data analytics, and for those organizations already using machine data analytics what they consider the benefits, challenges and future opportunities.
The vast majority of respondents (94 percent) said they already use machine data analytics, but they also face challenges with this usage. Of those organizations already using machine data analytics, 71 percent are now leveraging it to analyze data in machine real-time (within milliseconds), with 53 percent achieving human real-time (five seconds to five minutes latency), and the rest analyzing data in minutes, hours, or days.
Among the 200 survey respondents, there was a clear desire to analyze data as rapidly as possible. When asked specifically at which levels of speed they wanted to expand their use of machine data analytics, most respondents said ‘machine real-time’ speed (69 percent), compared with ‘human real-time’ (51 percent), and minutes, hours or days (29 percent).
About one-third of respondents (34 percent) said their existing machine data analytics offering doesn’t feature machine real-time analytics, while 53 percent said their current technology wasn’t even capable of human real-time analytics.
The most common user of machine data analytics within an organization is the IT operations manager, while the most common use cases are IT operations management (81 percent), security (60 percent), Internet of Things (IoT) (51 percent), big data analytics including Hadoop (51 percent), fraud analytics (45 percent), and IT governance or data sovereignty and compliance (34 percent).
Among the organizations already doing IoT work, nearly all (95 percent) use machine data analytics in support of their IoT projects.
With advances in machine data analytics, “it’s possible to finally begin getting some value from what some have called the ‘data lake’ on Hadoop,” the report notes. “Too many companies have put data into the open source data processing engine without thinking about whether that data can actually be extracted and analyzed later.”
But just over half of the survey respondents are using machine data analytics to do exactly that, showing that machine data analytics is often suitable not just for real-time analysis but also for batch and historical data analysis.
Most respondents (89 percent) are using machine data analytics to analyze and visualize structured data, while 47 percent are using it to analyze semi-structured data such as Twitter or Facebook feeds, and 18 percent said they even use machine data analytics to analyze unstructured data such as documents, images, and video.
When asked in which areas they see the most opportunity to expand their use of machine data analytics, 65 percent of the respondents said performing complex queries and correlation for applications such as security information and event management (SIEM) data.
“This highlights the need to be able to analyze real-time data but also to compare it with historical data in order to understand the real trends and implications of the latest data,” the report says.
The IoT also figured strongly in expansion plans, with 55 percent of the survey base saying they want to use machine data analytics more in this area.