Traditional tools, solutions and approaches weren’t designed in anticipation of the volume, variety and velocity generated by today’s complex and connected IT environments. Instead, they consolidate and aggregate data and roll them up into averages, compromising data fidelity.
In today’s high-volume data environment, it’s critical to embrace team collaboration and critical thinking as a way to influence the intelligence engine, as it automatically learns patterns, trends and tendencies from customer’s proprietary environments, and provides the most valuable insights to teams at the right time.
One of the biggest differentiators for AIOps platforms is their ability to collect all formats of data from multiple sources and then layer in automated analysis on top of it to empower IT teams to be smarter, more responsive and proactive. A comprehensive AIOps strategy demands operations teams broaden their purview of both IT and business initiatives, as they offload repetitive break/fix tasks and take on strategic projects.
Rather than narrowing your AIOps approach to one specific aspect of the incident response process, New Relic recommends strengthening the relationships between each stage of the implementation process to create a more powerful solution. Focusing only on faster detection, understanding, response, or follow-up is not enough; teams need a tool that thinks like their best SREs—from a systems perspective.
Below are five pillars of innovation that will help customers apply intelligence and realise business value from their AIOps strategy successfully.
Is AIOps the future of DevOps?
1. Noise reduction
While modern software environments present a number of challenges, one of the most urgent issues is the deluge of event volumes teams are being forced to sift through on an ongoing basis. Over the course of any given week, operations staff are overwhelmed by hundreds, if not thousands, of alarms.
By establishing robust AIOps capabilities, IT operations teams can correlate events to reduce noise and increase context. It starts with ingesting data from diverse sources and technologies, and aggregating a variety of data types, including events, logs, metrics and end user experience monitoring data in a single consolidated data repository.
Ultimately, event suppression is achieved by distinguishing between those arising within bands of normalcy versus those arising due to true abnormalities that could impact users. This way, IT operations teams will be notified only when a human action is required by their team.
2. Continual improvement
Gartner estimated the average cost of IT downtime at $5,600 per minute a few years back. If that’s any indication for downtime overages today, modern companies are in need for better ways to avoid these interruptions altogether. Continual improvement is a highly valuable intelligent capability, which brings software engineering teams closer to their overall vision of leveraging team knowledge.
AIOps continuously learns patterns and applies learned models against incoming alert streams to make sense of cascading and parallel impacts. It groups related alerts into inferences based on the learning models. IT and DevOps teams can then manage these inferences instead of addressing individual alerts, reducing the “noise” that users need to sift through in everyday operations. And they can build these inferences to operate continuously and contextually, supporting a continuous CI/CD pipeline.
The top use cases for AIOps in enterprise IT operations
IT leaders reveal what they’re really using AIOps for in their organisations. Read here
3. Assistance
After implementing existing, manual workflows into an AIOps solution to automate and scale them, it is critical that teams assess the value of those workflows, modify and improve them and finally, develop new ones based on the existing or to address gaps. The promise of AIOps is the ability not only to execute what heretofore wasn’t practically feasible; it’s doing it at a scale and speed that makes previously unrealised analytics opportunities possible. IT Ops will move from a “practitioner” to an “auditor” role. Teams will now have an improved understanding how systems are processing data and whether the desired business outcomes are being achieved.
4. Detection
Detecting anomalies in order to locate problems and understand trends within infrastructure and applications is a key use case for AIOps. Detection allows tools to both recognise behaviour that is out of the ordinary (such as a server that is responding more slowly than usual, or uncommon network activity generated by a breach) and react accordingly. AIOps tools can even take automatic action to resolve problems after they have identified them. For example, they could block a host or close a port automatically in response to a security threat or spin up additional instances of an application if they determine that the existing instances are insufficient to meet demand.
This is a critical component of an AIOps strategy as it allows software engineering teams to not only detect issues as early as possible, before their customers are impacted, but it allows them to minimise ongoing maintenance of detection configuration. Ultimately, it instills confidence in teams that their piece of the production environment is being monitored correctly and in near real time.
What is unique about AIOps for mission critical workloads?
5. Observability
Most organisations are at the stage of early adoption of cloud native technologies, with the failure modes of these new paradigms still remaining somewhat nebulous and not widely advertised. To successfully manoeuvre this brave new world, gaining visibility into the behaviour of applications becomes more pressing than ever before for software development teams. Engineering teams must be able to effectively and efficiently operate modern software systems. In the long term, as systems continue to get more complex, the only way to implement an AIOps strategy effectively is to automate as many tasks as possible for our customers and assist with and augment those that require human involvement – all in a fully transparent and open way to maintain customer trust. This will empower software engineering teams to reduce toil and easily audit, control access and validate their configuration to increase confidence it is setup correctly.
The true benefit of AIOps is realised when it delivers truly collective intelligence. This collective knowledge will allow organisations to break through legacy silos, fueling true, efficient and meaningful collaboration. In this way, AIOps delivers invaluable insights that encourage optimised operations and service levels.
