AIOps applies machine learning over large amounts of operational data to automate IT operations, including event correlation, anomaly detection, and causality determination. IT teams are exploring AIOps for several reasons, like reducing alert fatigue, proactively detecting performance problems, and avoiding outages. Many of those teams are searching for an all-in-one AIOps solution that does it all. The challenge these teams face isn’t with the predictive algorithms and models. Instead, their challenges are more practical concerns around collecting, normalizing, and routing data to the right places.

AIOps tools need the flexibility to ingest and index data from many sources. These include infrastructure, networks, applications, a range of monitoring tools, and deployed software agents. All data from these diverse sources must be normalized before it can be used for either real-time analytics over data in flight or for historical analysis over larger datasets at rest. Successfully deploying AIOps into the enterprise means managing three core constraints: volume, accuracy, and precision.