Delivering collective intelligence with the five pillars of AIOps

Delivering collective intelligence with the five pillars of AIOps

Delivering collective intelligence with the five pillars of AIOps

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

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.

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.

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Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards