At .conf2015, you learned how you can bring “sexy back to IT” with better service monitoring. Our 2015 announcement of Splunk IT Service Intelligence (ITSI) focused on how you can apply a data-driven approach with machine learning to monitoring thereby removing IT complexity and delivering business context—and now the broader industry is playing catch up.
In their recently published Market Guide, Gartner defines Artificial Intelligence for IT Operations (AIOps): “AIOps platforms are software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.” This validates our approach and the importance of coupling intelligence with a scalable data platform to create long-term IT success.
IT is Getting More Complex
Managing IT operations has become significantly harder with increased expectations for customer experience and the adoption of technology trends like cloud, DevOps, automation, and software-defined infrastructure, just to name a few. With the ever increasing number of assets IT must manage and the variety of data across users, it’s challenging to maintain an accurate view of the system to report out to stakeholders. Existing tools often reinforce silos and prevent users from seeing the full picture and pinpointing issues quickly. Furthermore, the ephemeral nature of new technologies only makes this more difficult.
A New Model for IT Operations
To manage this complexity, IT requires a platform that inherently captures and correlates data as well as provides machine learning and AI capabilities that leverage that data. IDC recently named the IT Operations Analytics Software Market the fastest growing IT market of 2016. This report, coupled with the Gartner AIOps report, reinforces the importance of having a scalable data platform as the foundation to manage IT operations. Combining this platform with machine learning and AI defines a new model for IT that enables advanced use cases. For instance, with this approach, you can simplify incident detection, train systems on incident severity, detect root cause more quickly, trigger procedures for specific alerts, and predict future state of a system and when a failure might occur. With these types of solutions, the role of IT operations evolves from a reactive firefighter to a proactive business partner.
Making Machine Learning and AI Ubiquitous
Today, Splunk already provides analytical capabilities like automated pattern discovery, anomaly detection, and root-cause analysis. You can experiment with our machine learning techniques and apply custom models to your data with the Splunk Machine Learning Toolkit. Machine learning and AI become true catalysts when you don’t have to be a data scientist to take advantage of these techniques. Think of Alexa or Siri—you don’t really care what AI is underpinning it as long as the experience is good. To that end, we have already integrated anomaly detection and adaptive thresholds into Splunk ITSI in a pre-built, use-case specific way. Recognizing the importance of ease of use, we will continue to invest in ways to make it easier for everyone to take advantage of machine learning and AI.
SVP IT Markets
Source: Gartner, Inc., Market Guide for AIOps Platforms, Will Cappelli | Colin Fletcher | Pankaj Prasad, 03 August 2017
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