How do you get started with AIOps?
The best way to get started with AIOps is an incremental approach. Best practice is to start small by reorganizing your IT domains by data source. Learn how to work with large persistent data sets from a variety of sources. Let your IT operations team become familiar with the big data aspects of AIOps. Start with a data set of historical data, and gradually add new data sources as you improve your practice.
Focus on ingesting data first: 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, applying AI powered by machine learning to introduce automation and, eventually, predictive analytics.
Ingest and analyze as many data types as you can: Historical data is extremely valuable as you get started with AIOps. If you start by analyzing and understanding past states of your systems, you will be able to correlate what you learn with the present.
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.
Don’t try to do it all at once: Focus on finding the root cause of your highest priority problem. Then 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 both an effective foundation for 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 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 in which you can determine the incident and its impact before it even affects your key business services and customer experience.