The Building Blocks of AIOps

published on 19 March 2024

Coined by Gartner in 2016, Artificial Intelligence for IT Operations (AIOps) is a term used to describe the use of big data analytics and machine learning to enhance information technology(IT) operations like automation, performance monitoring, anomaly detection, and causality determination.

Modern IT, characterized by microservices, distributed systems, and hybrid cloud infrastructures, has brought an explosion of data that traditional ITOps are simply not equipped to cater to.
Although valuable in the past, their limitations, such as manual data overload, limited visibility, and inefficient resource allocation, have begged the need for a more automated and data-driven approach to keep up with the demands of modern IT.

Businesses can no longer afford to rely on outdated processes that are bleeding revenue, churning customers, and incurring significant costs due to ineffective troubleshooting and reactive firefighting.
AIOps emerges as a transformative solution, tirelessly working through data to identify patterns, predict issues, and automate solutions. This proactive approach unlocks unprecedented efficiency and resilience for businesses, but how does it achieve this? Let's explore the core capabilities of AIOps and the building blocks that power this transformation.

What are the capabilities of AIOps?

AIOps encompasses essential features and capabilities that work together to manage IT operations effectively. These capabilities, which range from collecting and storing data to analyzing and taking action proactively, allow you to employ a system that makes sense out of the data in an actionable way.

They offer incremental value and should be implemented sequentially with later iterations. The key AIOps capabilities are:

Data harvesting: Data harvesting involves continuously collecting performance metrics and text logs from various sources. Its crucial capabilities include data organization, standardization, and flexible ingestion APIs to handle diverse data formats. AIOps thrive on interpreting massive datasets from various sources, so seamlessly consuming, normalizing, and structuring data for analysis across any source is essential.

Learning and correlation engine: AIOps leverages machine learning to analyze massive events and performance metrics datasets. Identifying patterns and relationships within this data builds an understanding of your IT infrastructure's normal behavior. This allows AIOps to detect anomalies that might indicate potential issues. By analyzing these anomalies, AIOps can pinpoint the root cause of problems, leading to faster and more effective resolution.

Topology discovery: Topology refers to the physical structure, relationships, and dependencies of artifacts or assets in an organization's IT ecosystem. It can be represented in many layers and business needs, from technical network diagrams to higher-level business topologies. Navigating through the topology layers is key to understanding the context and importance of any anomaly. The topology includes infrastructure, applications, and services independent of the data center, physical, container, or cloud deployment.

Business relevant alerts: Unlike traditional alerts, AIOps alerts do not rely on pre-configured alerting defined by technical teams. They count on algorithms to identify anomalies, which requires solid data, a sophisticated and robust anomaly detection engine, and contextual understanding. The alerts are prioritized based on business impact, focusing on crucial issues and suppressing irrelevant alerts.

By cutting down on alert fatigue, IT teams can pinpoint and fix critical issues faster, leading to quicker and smoother operations.

Bonus capability - Actionable dashboards: A bonus capability from all the data and actionable insight is dashboards and reports. In practice, the data is real-time business intelligence. Translating this data into reports and dashboards empowers stakeholders of varying levels of expertise across the organization to make informed decisions, ensure IT governance, and impact profit and loss.

Taking action: AIOps empowers action at every step. It starts with data harvesting and gathering information from diverse sources. This data is fed into the anomaly detection engine, which pinpoints deviations from normal behavior. Finally, it undergoes context-aware analysis, providing deeper insights into the potential issues.

Once the probable root cause is pinpointed, swift action can be taken, either manually by operators or automatically, through pre-defined actions designed to self-heal the issue.

Why should you care about AIOps?

Having explored the capabilities of AIOps, let's delve into the benefits it offers your organization. Why should your company consider taking the leap and adopting AIOps for its distributed systems? Here are some compelling reasons:

Better observability: With the explosion of microservices across multiple cloud providers, it can become easy for these data to remain siloed in the services producing them.

This problem with fragmentation compounds when you consider the sheer volume of data this multitude of microservices generates. It becomes complicated for traditional teams to make sense of or glean insights from all these data, leading to blind spots, delays, and overall difficulty in finding and resolving issues.

With AIOps observability tools like Eyer, you can have a holistic view of your heterogeneous IT infrastructure, enabling complete transparency into your distributed systems. This unparalleled visibility helps you identify anomalies and potential issues before they impact users, ensuring smooth operation and a positive user experience.

Improved time management and prioritization: AIOps tools sift through the noise and prioritize alerts on potential impact and urgency by analyzing historical data and current trends, allowing teams to focus on critical issues first.

Additionally, AIOps tools enable you to save time by automating repetitive, manual tasks like error detection, alert analysis, and event reporting, freeing up resources and reducing the likelihood of human errors.

Faster Mean Time to Repair (MTTR): While the modern software landscape thrives on 1000+ applications generating valuable data, about 85% of this data remains unanalyzed "dark data." This untapped information holds the potential to uncover insights crucial for optimizing performance and resolving issues efficiently. However, organizations struggle to harness this wealth of data without the right tools and resources.

This is where AIOps solutions shine. By utilizing advanced analytics and machine learning algorithms, AIOps platforms can sift through vast amounts of data in real-time, identifying patterns, anomalies, and potential issues before they escalate. This proactive approach accelerates incident detection and enables faster root cause analysis and resolution.

By harnessing the power of AI-driven insights, AIOps empowers IT teams to streamline their operations, minimize downtime, and ultimately achieve faster MTTR, ensuring optimal performance and reliability of their systems.

Cost optimization: Enhanced automation, proactive problem identification, and reduced MTTR all translate to cost savings and optimization.

Beyond minimizing financial losses due to downtime, AIOps optimizes cloud costs by leveraging software to make crucial resource provisioning decisions. Applications get the exact amount of resources they need, when they need it, without compromising on performance. This is especially useful in a space where organizations have admitted to wasting about 32 percent of their cloud spend.

Accelerated innovation: From automating repetitive manual tasks to reducing the overall mean time to identify and resolve issues and even preventing some issues altogether before they snowball to resource-intensive issues, AIOps presents a massive shift from the traditional IT operations human resource-intensive way of life.

With AIOps solutions, team members are freed up to create solutions that drive innovation and strategic initiatives.

Additionally, AIOps practices allow organizations to use tools that learn continuously. These tools reduce human resource dependency, mitigating knowledge loss risk when team members leave the organization.

What are the others saying about AIOps?

While discussions surrounding AIOps tools and what they can offer have taken the modern software landscape by storm, only a small percentage of organizations have boldly integrated AIOps tools into their ecosystems. This hesitation can be attributed to comfort zone biases and concerns about integration complexity. While headless AIOps solutions like Eyer can address integration issues, there is also the challenge of a lack of specialized AIOps knowledge across teams.

The good news is despite these challenges, the future seems bright for the adoption of AIOps. Gartner predicts a significant increase in uptake, which isn't surprising considering the current state of ITOps. Organizations are overburdened, with 72% admitting to managing nine monitoring tools and 47% swamped by 50,000 alerts monthly. Traditional methods simply can't keep pace with the data demands of large-scale businesses.

What’s next?

This shift towards AIOps isn't just a trend; it's a necessity. As data volumes explode and IT environments become increasingly complex, traditional ITOps cannot cope. Embracing AIOps, with its potential for automation, intelligent insights, and proactive problem-solving, is no longer a question of "if" but "when." By proactively embracing this shift, organizations can ensure their IT infrastructure remains resilient, responsive, and a valid driver of their success.

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