Let’s begin by ensuring we are all on the same page regarding what AIOps is and does, but within the context of application performance management (APM).
Within this milieu, AIOps means using data analytics to detect relevant incidents or patterns within application behaviours, analyse them, and when feasible, take steps to remediate problems automatically.
Gartner says: “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.”
In corroboration of this, one report notes that if AIOps cannot resolve issues automatically, it can at least perform root-cause analysis, providing IT engineers with the insights they need to identify and address problem sources quickly.
This visibility is especially critical in today’s complex application environments, where a surface-level performance issue could be caused by a variety of underlying problems which IT teams may struggle to identify when relying on manual investigation alone.
AIOps predictions hail it as the winner technology of the year, and confirm that ITOps teams need to move quickly, and that the dramatic changes to network operations and usage, witnessed in recent months, are expected to continue throughout 2021.
The report says network managers need to be able to understand new network baselines, bandwidth usage trends, application SLAs, and the new potential bottlenecks brought on by increased usage of cloud services, changing traffic patterns and migration from traditional WAN to SDWAN networks.
What is interesting is that this report affirms that without AIOps technology, teams will struggle to process and interpret the large data outputs from these systems – all of this is said to ensure AIOps will be the leading technology of 2021.
It is further predicted that in 2021, we will see the wide adoption of AIOps as more organisations move to the cloud. Improved AI capabilities are expected to provide pattern recognition capabilities that deliver operational analytics to network administrators at the speed of light. AIOps is further expected to become the foundation for automation, with artificial intelligence (AI) and machine learning (ML) technology and solutions being used to drive contexts from complex and distributed systems to help contextual automation across development and operations.
Heading problems off at the pass
AIOps can also be used to predict future application behaviour and suggest remediation steps before problems have even fully materialised.
AI and ML that power AIOps are founded on several key components, all of which must be in place to make AIOps-powered APM tools work properly These are as follows:
- Data is at the core of AIOps. A data lake/warehouse is required for storage. Typically, data for AIOps is organised using a time-series structure, which allows APM tools to track patterns over time and identify the root cause of performance problems.
- ML algorithms allow AIOps tools to make sense of the data analysed.
- Support for disparate data types.
Today’s APM tools need to be able to collect and analyse traditional system logs plus application tracing data, ephemeral log data produced by containers, serverless functions, data generated by business systems and other data sources that were not conventionally used for APM.
Successful AIOps platforms must be flexible and adaptable, able to support a broad range of scenarios and adjust their operations as needs change.
Only in this way can they provide holistic insight into application performance. They must also be able to integrate with all types of software tools used regardless of vendor, or if data has been structured in different ways.
Successful AIOps platforms must be flexible and adaptable, able to support a broad range of scenarios and adjust their operations as needs change. Moreover, the best AIOps tools are the ones that leverage experiences to hone predictive capabilities and grow the ability to remediate problems. The goal is continuous improvement to hone detection plus analysis abilities and solve performance issues.
Automatic intelligent remediation of performance problems is one of the key features that sets modern AIOps-powered APM platforms apart from what went before. However, it should be noted this may not always be possible, but it must remain first prize.
In situations where tools cannot completely automate remediation, APM devices should offer semi-automation features for reducing the manual effort required from engineers. Over time, the tools can learn from manual remediation workflows so that those workflows can eventually be automated.
Digital experience monitoring is the name of the competitive game.
Guaranteeing positive user experiences is the difference between not just competing in your space but winning. Without consistent positive user experiences, the game can very quickly be over in a world of multi consumer choices, increasing service excellence demands and digitally savvy customers with the ability to swap to a supplier who can deliver what they want: ongoing great service.
This has become so important that an entire discipline has emerged around it − digital experience monitoring (DEM) – which refers to using a variety of tools and monitoring methods to optimise the way people experience software, plus making machine-to-machine interactions as efficient as possible.
Having an intelligent APM solution in place is one essential element of DEM because APM provides the following insights:
- An omnichannel, end-to-end view of software environments, which enables businesses to track the user experience across multiple applications and platforms.
- Insights into the performance of both Web and mobile apps.
- Business KPIs and metrics such as user retention rates, drop-off frequency and revenue generation.
- Performance and crashes by software systems, and the way they correlate with user usage statistics.
The bottom line? The answer is clear − it is not possible to deliver an excellent user experience without the help of an APM solution that can monitor any type of application or environment at any level of scale necessary, while also helping to prevent the performance disruptions that will drive users away from the application. In short, this is why AIOps is the future of APM.
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