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Machine Learning / Observability

Seeing the Big Picture with AIOps

By adopting AIOps, organizations can evolve their operations and gain better-quality insights that empower them to be nimble in the face of change.
Sep 23rd, 2022 7:12am by
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Spotting trouble as quickly as it starts is often easier said than done, particularly in IT operations environments where it’s hard to separate the noise of false alerts from the things that need immediate attention. And that’s a slog for your business and your people.

How do you weed out the noise and instead focus your valuable talent on operational issues that matter? Ideally, by using proactive, intelligent capabilities that predict and prevent problems and offer insights, you can go one step better than simply reacting quickly to problems.

Artificial intelligence (AI) is more prevalent than ever across service and operations management, both in distributed systems and the mainframe. How prevalent? The AI for IT operations — or AIOps — market that was valued at $13.51 billion in 2020 is projected to be worth $40.91 billion by 2026, according to Mordor Intelligence.

Every second counts when an application hiccups. Employee productivity, Net Promoter Score (NPS), business reputation and revenue can all take a hit when complex applications and services spanning on premises, cloud and even mainframe are not working as intended.

AIOps can help by applying the appropriate sophisticated AI and machine learning (ML) algorithms and models instead of traditional rules-based (and typically manual) processing methods for IT operations, introducing new speed and efficiency across the organization and providing higher-order insights that lead to better decision-making.

Observability goes hand in glove with AIOps, but they are two distinct concepts that feed off and complement each other. When you have both, you use AIOps for more intelligent and dynamic monitoring with anomaly detection and advanced root cause analysis learning from a broad set of data, including events, logs, traces, metrics and topology.

Observability is the notion of getting visibility into the full tech stack. Data observability is about gaining operational data insights, leveraging multiple sources and types of data, and being able to determine the state of a system based on that external data. The aspiration is full stack observability, which allows you to respond to situations that you were not aware of ahead of time and apply intelligent automation to examine your IT landscape and take the appropriate action, whether it’s compliance, a patch or the blocking or elimination of a threat. Put simply, the greater the observability and the greater the insights, the more powerful the actions will be that you can take and the more prepared you will be to respond to the unexpected.

Getting a full view of the business impact and moving from reactive to proactive can help you better prioritize business risks and problems to meet your service level agreements (SLAs) and improve customer, and employee, satisfaction and retention while capitalizing on differentiating opportunities for growth. It also opens the door for more innovation. As you get better at adapting to rapidly evolving technologies and processes, you can allocate your top talent to innovation projects that they find more rewarding.

A recent PwC business survey found that 62% of AI “leaders” — companies advancing with AI in the areas of business transformation, enhanced decision-making and modernized systems and processes all at the same time — are using the technology to support operations and maintenance. Deploying the right AIOps solution requires many considerations, including:

  • Open cross-domain engagement, observability and actionability: With true enterprisewide, platform-driven management, IT can better predict issues, resolve them faster and provide always-on service for the business.
  • Predictive insights and failure prediction: AI/ML can identify patterns in the data, identify trends and provide intelligent insights that would take significant human effort and investment. Projecting organic growth trends can help proactively identify impending resource constraints, while learning from trends related to previous failures can help predict failures before they happen.
  • Event noise reduction: Analysis powered by AI/ML separates the real problems from noise to deliver a clearer view of the real issues causing event storms.
  • Intelligent alerting: By federating data from across the IT environment, including third-party solutions, AIOps can filter and correlate data and transform it into actionable events so that potential problems are proactively flagged before they affect customers or the business.
  • Cross-domain situational understanding and probable cause analysis: By applying advanced analysis to aggregated data across infrastructure and applications, IT can identify and focus on the true problem and respond, saving time and energy that can be better allocated elsewhere.
  • Intelligent automation: AI/ML algorithms, policies and insights continuously detect the state of the infrastructure and service-desk activity to take or recommend automated actions for faster, informed fixes.

By adopting an AIOps posture, organizations can advance and evolve their operations and gain better-quality insights that empower them to be nimble in the face of change and prepare for the future.

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