Expert Interview (Part 1): Splunk’s Andi Mann on IT Service Intelligence, ITOA and AIOps
For over 30 years across five continents, Andi Mann (@AndiMann) has built success with Fortune 500 corporations, vendors, governments, and as a leading research analyst and consultant. He currently serves as Splunk’s Chief Technology Advocate. He is an accomplished digital business executive with extensive global expertise as a strategist, technologist, innovator, and communicator. In the first of this two-part interview, he shares his thoughts on IT Service Intelligence (ITSI) and its role in IT Operational Analytics (ITOA) and Artificial Intelligence Operations (AIOps).
What is ITSI and how does it fit with ITOA and/or AIOps?
According to Gartner, IT Operational Analytics (ITOA) is a market for solutions that bring advanced analytical techniques to IT operations management use cases and data. ITOA solutions collect, store, analyze, and visualize IT operations data from other applications and IT operations management (ITOM) tools, enabling IT Ops teams to perform faster root cause analysis, triage, and problem resolution.
As it has become more sophisticated, Gartner has redefined ITOA as “AIOps,” initially calling it Algorithmic IT Ops, now morphing into “Artificial Intelligence Ops,” reflecting the increasing use of machine learning, predictive analytics, and artificial intelligence in these solutions.
Splunk IT Service Intelligence (ITSI) is a next-generation monitoring and analytics solution in the ITOA/AIOps space, built on top of Splunk Enterprise or Splunk Cloud. ITSI uses machine learning and event analytics to simplify operations, prioritize problem resolution, and align IT with the business.
Using metrics and performance indicators that are aligned with strategic goals and objectives, ITSI goes beyond reactive and ad hoc troubleshooting to proactively organize and correlate relevant metrics and events according to the business service they support. With ITSI, IT Ops can better understand and even predict KPI trends, to identify and triage systemic issues, and to speed up investigations and diagnosis.
This allows maturing IT organizations to quickly yet deeply understand the impact that service degradation has not only on the components in their service stack, but also on service levels and business capabilities – think more “web store” than “web server.”
We are seeing a lot of investment by organizations in leveraging the value of Big Data, what do you see as the major drivers for this?
I see three main drivers for this new focus on Big Data.
Firstly, the increasing volume of data is creating a maintenance nightmare, but also an analytics dream. This new data – from online applications, mobile devices, cloud systems, social services, partner integrations, connected devices, and more – is full of insights, but cannot be managed with traditional tools. Big Data is often the only way to understand a modern business service at scale.
Secondly, speed and agility are emerging as market differentiators. Slow, old-school techniques like data warehousing, Extract-Transfer-Load (ETL) operations, batch data processing, and scheduled reporting are not fast enough. New-style Big Data tools, by contrast, ingest data in real time, use machine learning and predictive analytics to generate meaning, instantly display sophisticated and customizable visualizations, and produce actionable insights from Big Data as it is produced.
Thirdly, there is an increasing focus on data-driven decisions to drive innovation. From junior IT admins to senior business execs, innovation requires all stakeholders make accurate decisions in real time. Big Data allows everyone to try new ideas, determine what works and what doesn’t, and then iterate quickly to course-correct from failures or double-down on successes, quickly adjusting to new information and meeting the changing demands of the market.
In Part 2, Andi Mann discusses the reasons mainframe and distributed IT are sharing data, and the use cases where organizations are building more effective digital capabilities with mainframe back ends.
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