AIOps adoption is on the rise. According to Gartner, by 2023, 40 percent of DevOps teams will augment application and infrastructure monitoring tools with AIOps platform capabilities. Use cases are also expanding beyond IT to include IT Service Management (ITSM), digital experience monitoring (DEM), DevOps, Application Performance Monitoring (APM) and third party services.
Infrastructure and operations leaders are opting for AIOps solutions to mitigate the rapid growth in data volumes generated by the IT systems, networks and applications; increased data variety and ongoing need to analyze events, metrics, transactions, customer sentiment data etc.; increased velocity at which data is generated; and the rapid rate of change within IT architectures and challenges in maintaining observability.
While the market is abuzz with AIOps solutions, top offerings share a focus on six key areas of functionality which eventually determine the platform’s robustness, benefits, and value. If you are in the market for AIOps technology, these are the 6 key areas to evaluate to reap the full benefits of the platform for your business. Feel free to download our AIOps Request for Proposal (RFP) document to help guide your decision making process.
#1: Scope of data coverage
A prerequisite of every AIOps platform is the ability to consolidate and analyze all business data. AIOps platforms must be able to ingest historical and real-time data by aggregating inputs from storage, databases, analytics, monitoring, APIs and SDKs, CRM and data streams etc. Regardless of the business’s original data architecture and silos, data is streamed into one centralized analytics platform to analyze 100 percent of data streams and metrics.
#2: AI and predictive capabilities
AIOps platforms vary widely in their approaches to AI and ML, running the gamut from statistical and probabilistic analysis, to automated pattern discovery and prediction, unsupervised learning for anomaly detection and topological analysis, to any amalgamation of these techniques. While there is no “right” solution to this problem, top vendors use advanced AI and ML to learn the unique behavior (and seasonality) of every metric, offer a library of model types for different signal types (every metric that comes in goes through a classification phase and is matched to its optimal model), and algorithms for sequential adaptive learning capable of initializing a baseline and rapidly computing the relation of each new data point going forward.
#3: Advanced correlation engine
Correlations are crucial for understanding metrics in context. To transcend the mere detection of outliers, events must be correlated across metrics and dimensions with potential business impact and other concurrent processes. Abnormal correlation, naming correlation, graph correlation, and implicit analytics topology — or any combination thereof – are some of the derivatives used by AIOps solutions for granular correlation between metrics in real time.
#4: Determine the root cause
For the patterns that AIOps platforms detect to be relevant and actionable, a context must be placed around any outlying event. Pruning down the network of correlations established by the automated pattern discovery to define causality chains linking cause and effect is key to reducing time to resolution. Root cause analysis not only ties between different events, but also establishes a causal connection that enables rapid remediation. While at the current stage of maturity automated root cause analysis can only provide probabilistic indications, AIOps solutions vary in their root cause analysis capabilities, often employing different approaches. It’s worth while to determine if resolution insights are provided, and how granular are the reported incidents and proposed root causes (i.e. down to the server, service, or individual log line).
#5: Reduce current Mean Time To Detect & Resolve (MTTD & MTTR)
While AIOps systems can and do provide valuable insights about your infrastructure, operations, application etc., at their heart they are geared at helping the business bounce back from events and glitches as rapidly as possible, thus reducing damages to the minimum. Fast remediation can curb downtime and associated losses of ROI, reputation and customers, so leading AIOps usually offer hard data about their ability to reduce MTTR through real-time analysis, event correlation, and root cause analysis.
#6: Engage and act capabilities
Still in its infancy, the “action” phase of AIOps is still lacking in most solutions. However, since this is the direction this domain is going in, it’s a good idea to check with respective vendors where they stand on automated actions. These automated, closed-loop processes are referred to as ITSM or “self-driving ITOM”. Currently, they can be observed in low level tasks, such as automating “bounce the server” or an “open a ticket” type of script. But as the technology matures autonomous remediation is likely to become a dominant feature for leading platforms. In the meantime, however, it’s crucial to verify that the platform can effectively communicate granular data and insights to both IT stakeholders and other IT systems that can be used in the remediation phase.
Conclusion
While approaches to AIOps vary between vendors, what matters most is how each solution’s capabilities stack up to meet your objectives. Robust AIOps platforms help IT operations teams monitor, detect and mitigate irregular and anomalous behavior on IT infrastructure and services by leveraging advanced machine learning techniques to constantly analyze and correlate every business parameter, providing real-time alerts, and lowering mean time to detection and resolution.
While this is a lot to take in, the AIOps RFP breaks down the concepts, features and capabilities covered in this post into bite-size chunks that will enable you to evaluate competing technologies head to head. So download our AIOps RFP template, and don’t hesitate to reach out if you need further guidance. We’re here to help.
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Written by Anodot
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