IT operations analytics (ITOA) is the practice of monitoring systems and gathering, processing, analyzing and interpreting data from various IT operations sources to guide decisions and predict potential issues.
Information is gathered from live running infrastructure as well as older logged data from application, service and infrastructure hardware logs. Other sources of data include software agents running in operating systems or hypervisors gathering data relevant to I/O, transactions and resource usage. The results of scripted tests are gathered, along with real-time analysis of running protocols in a network.
All or some of the operations data is used to yield a high-level view of infrastructure that can enable better management of IT resources, employees and infrastructure. However, the sheer volume of data gathered can overwhelm the resources to effectively make use of it, which can lead to IT operations management (ITOM) becoming more reactive than proactive.
The application of big data analytics technologies has made ITOA more effective. Technologies that are specialized in processing and interpreting massive amounts of data, such as Hadoop, NoSQL, Cassandra and ExtraHop, can be applied to the information collected. IT operations management can then use the extracted patterns to better predict performance, issues and outages.
IBM Data Scientists Dan Roscigno, Anthony Brew, and Robert McKeown share how IT Operations Analystics solutions are addressing the operational big data needs of the clients they are working with: