IT IT

3 Ways to Get Started With an AIOps Solution

In our last blog post, we introduced artificial intelligence for IT operations (AIOps) and what it means for you. But knowing how to do that is a little bit different than doing. How can organizations begin to gain value from an AIOps solution?

A key aspect of gaining value from AIOps is data. Having a platform that can support the variety of data types and provide analytics is key to getting started. The Gartner Market Guide for AIOps Platforms provides best practices on how organizations can easily gain ROI from an AIOps platform.

1. Focus on ingesting data first.

Rome wasn’t built in a day, and an effective AI deployment is no different. Enabling AIOps requires access to all types of data: unstructured machine data and metrics, as well as relational data for enrichment. These different data types allow you to construct a holistic perspective across all silos and take actions meaningful to the situation and data type.

Ingesting and analyzing all of the data effectively and quickly can be daunting. Instead start with accessing and analyzing raw historical machine and metric data to establish a base understanding, and use clustering algorithms and analytics to identify trends and patterns. Raw data is the best type of data if you truly want real-time detection. Then you can begin to analyze streaming data to see how it fits those patterns, and apply AI powered by machine learning to introduce automation and eventually predictive analytics.

2. Ingest and analyze as many data types as you can.

As the saying goes, history repeats itself. Therefore it’s critical to gain insight into past states of your systems and correlate the past learnings with the present, so you can proactively fix errors, prevent downtime and optimize efficiency.

To achieve this, organizations must ingest and provide access to a vast range of historical and streaming data types. The data type that you select—be it log, metric, text, wire or social media data—depends on the problem you’re solving. For example, you can use metric data from your infrastructure to monitor capacity, or application logs to ensure that you are providing an outstanding experience to your customers.

Many AIOps platforms have historically only focused on a single data source. Restriction to a single data type limits your insights into system behavior—regardless of whether those insights come from an IT admin or an algorithm. Hence, enterprises should select those platforms that are capable of ingesting and analyzing data from multiple sources.

3. Resist the temptation to do it all at once.

Applying AI in IT operations isn’t easy. Your strategy first and foremost should be focused around finding the root cause of your highest priority problem. Then, once you’ve conquered this and found the root cause of your problem, you can progress to monitoring data. Only after this has been accomplished should AI be approached. Even then, take it step-by-step:

  • Start with implementing an AIOps platform that gives you an effective foundation in organizing large volumes of data that make it easy to take action and monitoring capabilities that reveal patterns.
  • Next, explore the degree to which those patterns enable you to to predict incidents and have a more proactive IT that allows you to decrease not only your MTTR but also the number of business impacting incidents you face.
  • Finally, work with machine learning-powered root-cause analysis to get to a predictive state where you can determine the incident and its impact before it even affects your key business services and customer experience.

Want to learn more about AIOps? Learn more about how you can use the Splunk platform for AIOps to automate your insights and fix problems faster. To learn more about Gartner’s recommendations, check out the Gartner Market Guide for AIOps Platforms.

Rick Fitz
Posted by Rick Fitz

Rick Fitz has served as Splunk’s senior vice president, IT Markets since 2015. Prior to this role, Rick served as Splunk’s vice president of IT Markets, from 2014 to 2015. Prior to joining Splunk, he served as vice president of product strategy and management at MarkMonitor, a Thomson Reuters company, from 2013 to 2014. From 2008 to 2013, he served as senior vice president of product management at CA Technologies, a software company. Previously, Rick served in a variety of senior management positions at Network General, BMC Software, Peregrine Corporation and Remedy Corporation. He holds a BSCE degree from University of the Pacific and an M.B.A. from Golden Gate University.

 

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