As your applications and IT environment grow more complex, observability solutions can help your IT teams to more effectively monitor and maintain system health. Observability platforms leverage artificial intelligence (AI) and machine learning (ML) to automate IT root cause analysis, sifting through massive amounts of log files and interpreting data with greater speed and accuracy than any human IT professional. With the ability to quickly and proactively remediate issues, observability solutions help to prevent disruption, ensure performance, and deliver the always-on, always-available experiences that your customers and users demand.
ScienceLogic Skylar Automated RCA AI Log Analysis fulfills the promise of observability solutions with a platform that does the heavy lifting for ITOps teams. After automatically ingesting millions or billions of messages from log files throughout your hybrid environment, Skylar Automated RCA performs ML-based analysis in real time to identify root cause and fix incidents more quickly.
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What is observability?
Observability goes beyond traditional IT infrastructure monitoring to provide end-to-end visibility across your entire IT infrastructure. Over the last decade, observability solutions have emerged as a more effective way to monitor system health and performance as organizations have shifted from monolithic to distributed IT architectures.
Where traditional monitoring technology focuses on whether applications and infrastructure are working—and alerting ITOps when they are not—observability solutions are designed to offer a deeper understanding of system performance within the context of business services and the broader IT environment. If monitoring solutions are built to let you know when a system isn’t working, observability solutions are designed to help you investigate why.
With the rise of advanced technologies like microservices, containers, and hybrid cloud infrastructure, IT teams that rely on manual processes for root cause analysis can no longer keep up. There’s simply too much data and too much complexity to manage systems without the help of machines. Observability solutions leverage AI and ML to collect and analyze data with incredible speed. These solutions unify the data from metrics, logs, and traces and provide a single platform for infrastructure monitoring, application performance monitoring, and SaaS monitoring. By providing complete visibility and automated root cause analysis, observability solutions help improve the customer experience, enhance employee productivity, and optimize digital infrastructure.
ScienceLogic’s Skylar Automated RCA AI Log Analysis
Skylar Automated RCA drastically reduces the time it takes to identify, diagnose, and resolve issues that impact business services. When issues arise in a modern IT environment, Skylar Automated RCA applies powerful machine learning analytics and draws on both real-time and historical data to provide a contextual understanding of the root cause of the issue and the services it impacts. Skylar Automated RCA’s automated capabilities can remediate issues with minimal intervention from IT teams, helping to reduce fix time on the problems that typically take the longest amount of time to solve.
With Skylar Automated RCA, your ITOps teams can:
- Diagnose issues 10x faster through automated root cause analysis.
- Eliminate much of the manual work required to identify and fix issues.
- Drastically reduce the time it takes to understand what is broken.
- Focus on other priorities—Skylar Automated RCA’s unsupervised machine learning capabilities can produce results in less than 24 hours.
- Deploy quickly—there’s no manual training required.
- Identify unknown issues and root causes without manually building complex rules or poring over log data.
- Correlate unusual behavior with recent changes in performance metrics to understand the potential business impact of every issue.
- Get plain-language root cause summaries that distill billions of log lines down to a few salient data points and that describe the root cause visually.
Building toward fully automated workflows
In addition to Skylar Automated RCA, ScienceLogic offers solutions that enable organizations to move faster to support business goals despite increasing IT complexity. ScienceLogic SL1 is an infrastructure monitoring and AIOps platform that collects key operational telemetry from your systems, delivering visibility into every component that could impact essential services. With SL1, ITOps teams can:
- See everything. With the broadest set of data collection capabilities today—from cloud-native microservices to containers and VMs all the way to physical entities like network devices and laptops—ScienceLogic provides ITOps teams with a comprehensive view to help understand everything that’s happening in your environment.
- Contextualize data. SL1 helps you understand how the lowest level building blocks in your environment are contributing to high-level abstractions like business services. The platform automatically uncovers unusual events and spots correlations between them. Together, SL1 and Skylar Automated RCA automate root cause analysis to understand where the issues are and make recommendations.
- Automate action. ScienceLogic automates corrective actions in many cases, enabling your teams to use runbook automations or automated workflows to perform remediation actions. Offering rich automation capabilities, a library of existing automation best practices, a low-code/no-code workflow automation framework, ScienceLogic empowers staff members at every level to build automations for new problems.
Why choose ScienceLogic?
ScienceLogic is a leader in IT operations management, providing AIOps solutions, hybrid cloud observability, and software for network management and automation. By empowering intelligent, automated IT operations, ScienceLogic frees up time and resources and drives business outcomes with actionable insights.
Our patented discovery techniques find everything within an IT environment, delivering visibility across all technologies, vendors, data centers, and clouds. Relationship mapping contextualizes data, while integration and automation enables faster action to resolve issues and improve availability.
Trusted by thousands of organizations around the world, the ScienceLogic platform is designed for the rigorous security requirements of the United States Department of Defense, proven for scale by the world’s largest service providers, and optimized for the needs of leading enterprises.
Observability Solution FAQs
What is observability?
In IT operations, observability is the ability to understand the health of a system or component by collecting and analyzing data across complex systems. Observability helps IT teams to understand system performance within the context of business services and the broader IT environment.
What are observability solutions?
Observability solutions use artificial intelligence (AI) to analyze log events, detect anomalies, and automate root cause analysis with greater efficiency and accuracy. Rather than manually sorting through logs and traces to identify root cause of incidents, IT teams may use observability solutions to access automated, real-time insights into operational status. Observability platforms also reveal the impact of potential issues on business services and recommend actions for quick incident resolution.
What’s the difference between monitoring and observability?
IT infrastructure monitoring shows teams what’s happening in an IT environment and alerts them to trouble and outages. It offers a moment-to-moment view of the health of operations. Observability goes beyond traditional monitoring to reveal deeper insights into why a system or component isn’t working. It enables IT teams to automate root cause analysis and resolve incidents more quickly.
What is AI-powered observability and how does it differ from traditional monitoring?
AI-powered observability refers to the use of artificial intelligence and machine learning to automatically collect, analyze, and correlate telemetry data—such as logs, metrics, and traces—across complex IT environments. Unlike traditional monitoring, which relies on static thresholds and manual rule sets, AI-powered observability continuously learns from data patterns to detect anomalies, predict potential failures, and surface root causes with minimal human intervention. This allows for proactive remediation and significantly reduces mean time to resolution (MTTR).
Why is AI critical for achieving true observability in modern IT environments?
Modern enterprise IT ecosystems are increasingly hybrid, dynamic, and distributed. Manual methods of monitoring and root cause analysis cannot scale effectively in such environments. AI enables real-time pattern recognition, intelligent alert suppression, and automated incident correlation across silos. It enhances signal-to-noise ratio, reduces operational noise, and uncovers hidden dependencies. By augmenting human operators with machine intelligence, AI-powered observability transforms reactive IT operations into predictive and autonomous workflows.
How do AI-powered observability solutions improve root cause analysis?
AI-powered observability solutions accelerate root cause analysis by using advanced correlation engines and anomaly detection models. They automatically map relationships across infrastructure components, services, and applications to identify causality rather than just symptoms. For example, an AI observability platform can link a latency issue in a customer-facing application to a configuration change in a backend Kubernetes node, even across cloud boundaries. This eliminates guesswork, reduces diagnostic cycles, and empowers IT teams to resolve incidents faster and with greater confidence.
Can AI-powered observability scale across hybrid and multi-cloud environments?
Yes, AI-powered observability platforms are specifically designed to provide end-to-end visibility across hybrid and multi-cloud environments. They ingest data from diverse sources—cloud-native platforms, on-prem systems, SaaS services, and edge devices—and unify it into a single, contextualized view. AI models normalize, enrich, and analyze this data to surface actionable insights, regardless of where the workloads reside. This centralized intelligence enables organizations to manage performance, availability, and compliance uniformly across disparate environments.
What types of data do AI-powered observability solutions analyze?
Comprehensive AI-powered observability platforms analyze a broad spectrum of telemetry data, including logs, metrics, traces, events, and topology metadata. Advanced solutions also integrate business KPIs, user experience signals, and configuration data. AI algorithms process this multimodal data to detect anomalies, correlate symptoms to causes, forecast capacity needs, and optimize service delivery. The result is a holistic understanding of system behavior that enables more effective decision-making.
How does AI observability support AIOps and autonomous operations?
AI observability is a foundational capability for AIOps, enabling the automation of routine IT operations through intelligent data analysis. It feeds AI/ML pipelines with real-time observability data, allowing for dynamic event correlation, root cause identification, and automated incident remediation. This reduces alert fatigue and empowers ITOps and SRE teams to focus on strategic initiatives. Over time, these systems can evolve toward autonomous operations, where AI dynamically tunes resources, mitigates issues preemptively, and orchestrates remediation workflows with minimal human oversight.
What should organizations look for in an AI-powered observability solution?
Organizations evaluating AI-powered observability tools should consider scalability, data integration capabilities, real-time analytics, and the maturity of AI models. Key features include full-stack visibility, automated discovery and topology mapping, anomaly detection, event correlation, and customizable dashboards. Seamless integration with existing ITSM, DevOps, and AIOps ecosystems is essential. A proven track record in large-scale, heterogeneous environments and the ability to demonstrate tangible outcomes—such as reduced MTTR and improved service availability—are critical selection criteria.
How does ScienceLogic enable AI-powered observability for enterprises?
ScienceLogic provides an enterprise-grade observability platform that harnesses AI and machine learning to deliver deep visibility, intelligent automation, and actionable insights across complex IT environments. By integrating hybrid infrastructure data with business context, ScienceLogic enables real-time decision-making and proactive incident resolution. The platform’s patented discovery, dynamic dependency mapping, and event correlation capabilities empower IT teams to shift from reactive monitoring to predictive, AI-powered operations that drive resilience, agility, and performance.
