Big data poses new challenges to organizations as found by research firm IDC in its 2018 white paper “Data Age 2025”:
The need for a new automated approach
The global datasphere (that is all data created, captured and replicated) could increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. To put that in perspective, this would correspond to
watching the entire Netflix catalog 489 million times.
With the digitization of our lives and organizations, IT environments are becoming more complex and ever changing by nature.
According to the same report
only 32% of data available to enterprises is put to work. The remaining 68% goes unleveraged
.
In order words, organizations do not get the complete picture of what is happening and miss on very valuable information represented by this data. And we are not even talking about data that is generated but not made available.
However fresh tools and approaches can represent an important part of the solution. A way to tackle these challenges is to adopt an automated real-time approach. Using machine learning and minimum user input, systems can be designed to autonomously process streamed data and immediately present the results of direct analysis to the users.
For further reading, please see
here
Share :
Logmind’s proprietary clustering method simplifies log analysis by reducing patterns to actionable insights using ML techniques like deep clustering to ensuring accuracy, flexibility, and real-time performance.
What is a log parser? In this post I will give an introduction to what a log parser does, why it is important, its applications and the different types of parsers available.
ML can improve the IT workflow in several different areas: let's find the right model for the right task.