What Is Explainable AIOps?

Explainable AIOps (Explainable Artificial Intelligence for IT Operations) refers to the integration of artificial intelligence (AI) and machine learning (ML) technologies into IT operations to enhance decision making, automate routine tasks, and improve overall system performance. It contextualizes the extensive data in an organization’s IT structure in real or near-real time. 

Combining this data with relevant historical information and trends can produce valuable and actionable insights. Explainable AIOps essentially functions as an intelligent assistant with profound knowledge of the IT and network environment. It leverages this knowledge to conduct real-time analysis, suggesting or even executing necessary steps to optimize operations.

AI is a large field that creates systems capable of performing tasks that typically require human intelligence. Examples of such tasks include problem solving, decision making, recognizing patterns, and learning from experiences. AI simulates human intelligence to reason and act.

ML is a subset of AI. It focuses on developing algorithms or statistical models that enable computers to learn from data and make predictions or decisions based on that data. Machine learning algorithms iterate and improve over time. 

Both AI and ML have numerous applications across diverse industries and have revolutionized how tasks are performed and decisions are made. For example, ML algorithms for personalized recommendations are widely used with e-commerce, streaming platforms, and other industries where content curation plays a key role.

Key Components of Explainable AIOps

Collecting data isn’t a challenge for IT teams anymore; instead, today’s challenges are more commonly about harnessing a large volume of data to transform it into useful insights. A robust AIOps platform is built on several components working together: machine learning, automation, data collection and analysis, real-time contextualization, and the integration of historical data. Together, these components give organizations the comprehensive view they need to optimize their operations.

1. Machine Learning

• Using ML algorithms and contextualized data, an explainable AIOps platform conducts root cause analyses and autonomously addresses straightforward issues within an app.
• ML algorithms can identify patterns and anomalies, enhancing operations and improving decision-making processes. 

2. Automation

• A successful implementation of AIOps requires an advanced AI engine capable of correlating events, along with machine learning algorithms that extract knowledge or patterns from observed data.
• Automation streamlines processes, reduces human intervention, and ensures timely responses to incidents

3. Data Collection and Analysis

• Explainable AIOps excels in contextualizing requests, expediting troubleshooting, and offering data-driven actions and recommendations to streamline operational workflows.

4. Real-Time Contextualization

• Explainable AIOps processes log data in real time, providing a dynamic view of an app’s environment.

5. Integration of Historical Data

• Relevant historical data is the foundation for a comprehensive understanding of an app’s performance and trends.
• Predictive analytics and proactive monitoring also rely on the integration of historical data.

Explainable AIOps marks a significant advancement in IT operations management. By harnessing the power of these components and bringing them together, organizations can proactively monitor app performance, conduct root cause analyses, improve their decision-making processes, and even predict network behavior. 

Core Principles of Explainable AIOps

The core principles of Explainable AIOps are focused on efficiency and reliability. These principles, including proactive monitoring, root cause analysis, and predictive analytics, redefine how organizations manage their digital infrastructure. By anticipating potential issues, identifying issues when they arise, and resolving them promptly, explainable AIOps make resilient and optimized operations possible. 

Proactive monitoring allows operations teams to anticipate and address potential issues before they impact performance, thereby reducing downtime and establishing a positive user experience. Real-time insights enable organizations to stay ahead of emerging issues, ensuring consistent service and minimal disruptions. 

Root cause analysis is an essential component of Explainable AIOps, enabling teams to identify the underlying causes of incidents in real time. By correlating real-time events and processing contextualized data, teams can recognize and resolve issues, minimizing their impact on performance. Root cause analysis supports performance optimization and helps apps operate at peak efficiency.

Predictive analytics makes it possible for teams to anticipate network behavior and to prevent problems from arising. Rather than relying on a firefighting mode of reacting to issues after they occur, predictive analytics lets organizations stay ahead of emerging challenges. By analyzing historical data and identifying patterns, predictive analytics can provide suggestions or remedies that address performance issues or anomalies. This proactive approach helps teams allocate resources efficiently so that they can spend more time addressing what’s most important. With this approach, teams can save money, improve efficiency, and ensure a seamless user experience—all at once.

Explainable AIOps Benefits

Explainable AIOps combines the power of AI and ML to transform IT operations and enhance the user experience. Through proactive monitoring and incident management, explainable AIOps ensures a more stable and reliable user experience. This fosters user satisfaction and loyalty—key in a crowded digital marketplace. 

Operational efficiency is a primary benefit of explainable AIOps. Automation frees up resources, allowing teams to focus their time and energy on what’s most important. This enhanced efficiency has a real impact: Organizations that use Conviva’s Operational Data Platform have been able to save up to 62,000 human hours in operational efficiency. 

Automation is the process of implementing technologies and systems that minimize manual intervention. It can be applied across domains, including IT operations and customer service, to handle repetitive tasks. Automation relies on algorithms, rules engines, and decision-making logic. By leveraging automation, organizations can streamline processes, reduce errors, and allocate their resources most effectively. Human hours are freed up to allow people to spend time on strategic activities while automation improves the speed and accuracy of routine operations. 

AIOps supports the scalability of app services by anticipating and accommodating increased user demands. By providing a holistic view of IT environments, AIOps offers insights into performance, dependencies, and potential issues across the entire infrastructure. Conviva is the only company to operationalize the collection and processing of machine data at Internet scale. No one else can analyze millions of endpoints in real time. Conviva can handle 5 TiB of traffic volume and 4.5 billion events per hour. 

Scalability is crucial in today’s digital landscape, especially for companies dealing with large events or with sudden spikes in traffic. (Any company that has asked their marketing department to make something “go viral” needs to be prepared for it to actually happen.) By leveraging AIOps for scalability, organizations can maintain a competitive edge and deliver exceptional user experiences, even during periods of high demand.

With Conviva’s Operational Data Platform, organizations can achieve unprecedented levels of user satisfaction at the same time as they achieve unprecedented efficiency. Explainable AIOps keeps companies competitive and delivers unparalleled user experiences.

How Conviva’s Time-State Technology Leverages Explainable AIOps

Conviva’s Time-State technology changes the way organizations can monitor and optimize user experience and performance.

Many traditional observability monitoring services may trigger alerts for incidents that don’t actually impact the end user experience. Any team inundated with notifications, alerts, or alarms risks something important getting lost in the shuffle. With AIOps, false alarms are reduced. Conviva’s AI Engine continuously scans 450K cohorts and analyzes 120 billion metrics per hour to identify anomalies and measure impacts to the user experience on every device and for every user, only triggering alerts when users are genuinely affected.

Conviva’s AI alerts surface issues and anomalies that are relevant for business impact, and it does so automatically, without the need for any difficult coding or queryingThis way, teams can take immediate, responsive action on the things that actually matter before they negatively impact user experience and revenue. The amount of time it takes for someone to log in, discovery, buffering ratios, an app not loading, and video playback failure, are examples of key areas for around-the-clock monitoring.

AIOps helps determine causality with root cause analysis. Conviva’s experience-centric observability allows operations teams to truly understand the complexities of user experience by carefully monitoring the sequence, timing, and context of user actions to create experience metrics based on timing, time intervals, sequences, and system state. For example:

• Who was impacted by the issue?
• When were they impacted?
• How long were they impacted?
• What kind of problem was it (device authentication, app authentication, or both)?

Conviva’s Operational Data Platform, powered by explainable AIOps, gives operations teams census-level monitoring to identify experience-impacting issues in real time. With this level of awareness, they can drill down into root cause and implement issue resolutions promptly.

Safeguarding the user experience protects revenue and reputation. Leveraging AIOps to surface only the most relevant issues and anomalies reduces noise and streamlines operations. With unparalleled visibility into user experience metrics in real time, teams can swiftly identify and address issues before they escalate. Seamless service delivery drives business success.