In our previous blog post, we discussed the three core capabilities that constituted AIOps solutions: data ingestion and handling, machine learning analytics, and remediation. With an exponential increase in the amount of data generated by all these devices and siloed tool sets, the job of IT Ops can only get more challenging. This includes the lack of timely insights to help deal with issues proactively, which is why it is important to equip your ITOM systems with enterprise monitoring solutions that not only has the ability to interrogate the IT environment to discover, create and maintain an IT infrastructure model in real time but also understand the contents of a model. This will drive better IT decisions by correlating the model information to the performance metrics, events, etc., establishing proactive actions and determining root cause when problems occur. Most organizations aren’t thinking about whether or not data is backed up, what quality-of-service implications they might be exposed to, what application or data permissions should be granted, or what audit or compliance challenges might be introduced — they just need to meet business demands. It’s the ephemeral nature of modern computing environments that are designed to move at the speed of business. According to the 2019 Gartner Market Guide for AIOps Platforms, “Artificial intelligence (AI) technologies such as machine learning have influenced the evolution of ITOM intermittently over the past two decades, and AIOps platforms are only the most recent example of that influence. Use of AI in IT operations has been driven by the adoption of digital transformation.”
4 Stages of ITOM That Need AIOps Support
To date, AIOps functionality has been used primarily in support of IT operations processes that enable monitoring or observation of IT infrastructure, application behavior or digital experience. According to the 2019 Gartner Market Guide for AIOps Platforms, these four stages of IT operations management are the ones that need AIOps support:
- Descriptive IT - through visualization and statistical analysis
- Anomaly detection and diagnostics - Through automated pattern discovery and correlations
- Proactive operations - Through pattern-based prediction
- Avoiding high-severity outages - Through using analytics to uncover root-cause analysis that can be missed by an IT operator
Traditional point tools can only allow IT teams to get hardware-specific data from the resources they control and manage. It gets more challenging as new technologies (e.g., containers and microservices) continue to evolve. However, outdated approaches inevitably result in the walling off of information between groups, causing a lack of visibility and even finger-pointing when problems occur. As the Gartner report points out, “Modern IT operations require visibility across IT entities, breaking down silos including applications, their relationships, interdependencies and past transformations to gain insight into the present state of the IT landscape.”
Transform ITOM With AIOps
Almost always, AIOps platform investments have been justified on the basis of their ability to decrease mean time to problem resolution and the resultant cost reduction. You need machine data from all sources/types to fully realize the service context, and machine learning can help you make sense of the mix of high-cardinality data. When you start with a robust and contextual data collection mechanism from different sources as your foundation stone, it enables observability and helps you to act on critical insights that allow you to analyze root cause and speed up troubleshooting and the resolution process. Whether you are in the midst of creating a completely new environment for your IT department or reevaluating monitoring tools and the potential of hosted IT services, you need AIOps solutions like Zenoss, purposefully built for taming the most complex, modern IT environments. To learn more about AIOps, download the 2019 Gartner Market Guide for AIOps Platforms.
