Skip to content
Security Operations

Infrastructure, scaling and staffing top barriers to analytics success

April 17, 2017

Infrastructure, scaling and staffing top barriers to analytics success

The number of organizations implementing big data and analytics projects has increased dramatically, but many struggle with a number of challenges to achieving the returns on investment they hope for.

That is the conclusion of a new study on machine data analytics by 451 Research and sponsored by Logtrust. The report, entitled “The Need for Speed: Machine Data Analytics in 2016-17” found that most organizations currently employ data analytics, and are most frequently using it for IT operations management (cited by 80 percent) and security (cited by 60 percent). Other common use cases include the Internet of Things (cited by 51 percent), big data analytics (cited by 51 percent), fraud analytics (cited by 45 percent) and IT governance/compliance (cited by 34 percent).

When asked what challenges they have faced with achieving hoped-for results with analytics projects, respondents cited infrastructure requirements ( cited by 36 percent), scaling challenges (cited by 33 percent), staffing requirements (cited by 33 percent), slow analytics (cited by 32 percent), expense (cited by 31 percent) and technical challenges (cited by 30 percent).

One of the most important features that respondents said they look for in analytics capabilities is speed, the report notes.

Just how fast is considered ‘fast’? Sixty-nine percent of respondents want machine real-time (within milliseconds) while 51 percent want human real-time (five seconds to five minutes latency), a desire validated in the report by author Jason Stamper, data analyst at 451 Research.

“Real-time is absolutely critical for companies to compete effectively,” Stamper says. This, however, illustrates a current market gap, as 53 percent said their technology wasn’t even capable of human real-time analytics.

Respondents were split between open source (cited by 39 percent), proprietary (cited by 36 percent) and a mixture of the two. The majority (67 percent), however, said they would choose proprietary technology when they use machine data analytics in the future.

When asked where they plan to expand machine data analytics, 65 percent said performing complex queries and correlation for various applications such as security information and event management (SIEM) data. IoT also came out strongly, at 55 percent, as did prognostic health (cited by 48 percent), smart city integration (cited by 42 percent) and connected customer marketing analytics (cited by 36 percent).

In terms of data types, 89 percent of respondents are using data analytics to analyze and visualize structured data, 47 percent are using it to analyze semi-structured data, and 18 percent use it on unstructured data such as documents, images and video.




More Data. More Clarity. More Confidence.