IT Operations Analytics (ITOA) is a specialized area where IT professionals are solving the problems of dynamic IT environment in enterprise IT infrastructure using Big Data and advanced analytics technologies and techniques. We do this by collecting all the IT information available within the enterprise: metric data and logs from servers and devices within the IT infrastructure, including application servers, integrated infrastructure, racks, and network nodes, and so on, and automatically apply advanced analytics.
One of the main values of operations analytics is that instead of having to know what you should search for in the large “data lakes” of machine data collected, the tools can automatically and in real-time process through millions of records, apply machine learning to extract information, and suggest the entries that you need to pay attention to.
In our experience operations analytics has generated wide interest in many IT professionals, including: VP of Operations; Director of Operations; Line of Business owners; IT operations engineers; applications developers; DevOps teams; database architects, system administrators, network engineers, etc.
In this roundtable, we will review the challenges organizations face with regards to log management and analysis tools, and the evolution of the IT needs to machine learning-based log data analytics.
At the end of this session, you’ll have a good sense of where you are on the maturity curve. You’ll also have a good idea of the path to get to full ITOA.
Directly align IT to the business with Operations Analytics
In the second roundtable, we are going to cover how to directly align IT to the business. We are going to show that you can not only analyze the data that you are collecting for troubleshooting or IT efficiencies, but you can tie it to your business processes for additional benefits.
We are going to discuss examples of how companies can look into their business processes. For instance, when companies are bringing more users to their service, they take them through various steps multiple workflows – operations analytics allows to integrate machine data into such processes and establish higher level of service quality and customer satisfaction. Operations Analytics, with its ability to look throughout all of the raw machine generated, business and non-IT data, provides a unique capability to show what’s happening within the business process and identify problems earlier before they really impact your business and your customers.
What I want you to go home with is increased understanding of the issues your peers face with respect to ITOA and the benefits that could be obtained in the near term - now that I have brought all the data within my infrastructure or within my business process together, I can troubleshoot problems faster. But I can do more than troubleshooting, I can really look into the entire operations as a whole and help the business processes function properly, apply predictive analytics to detect the issues before they fully develop, and become a partner to the business stakeholders by providing insights for better business execution, customer satisfaction, and cost management.
About the author: Viktor Doundakov is Sr. Product Manager, operations analytics, HP Software
IT Operations expert with background in enterprise software, IaaS and PaaS platforms, data mining and machine learning. Experienced in solution architectures, strategic initiatives and large software implementations.