Your modern hybrid IT estate is constantly evolving, empowering your business to be more agile, competitive, and innovative. Yet, for your IT Operations Management (ITOM) team, growing complexity makes it increasingly difficult to monitor system health, maintain availability, and deploy new technology to support better business outcomes.
AI-based observability can help. AI observability solutions use machine learning and artificial intelligence to analyze log events, detect anomalies, and automate root-cause analysis. Rather than manually sifting through virtual mountains of logs, AI observability enables your ITOM team to access real-time insights into operational status. With the right solution, your teams can gain essential analysis of the impact of potential issues on business services, use recommended actions to resolve incidents quickly, and proactively troubleshoot to maintain uninterrupted user experiences.
As a leader in ITOM and AIOps technology, ScienceLogic provides a powerful AI-based observability solution in Sklyar Automated RCA. When critical business services go down, Skylar Automated RCA helps you automatically identify root cause—in plain English—without a time-consuming search through log files.
The need for AI-based observability tools
As organizations rely more heavily on their applications than ever before, the field of application monitoring has reached a critical moment. Widespread use of applications means that production incidents can easily impact thousands or potentially millions of users, making fast resolution of issues imperative. At the same time, the runtime complexity and operational data volume of applications continues to grow exponentially. Manual troubleshooting processes create huge bottlenecks, rapidly expanding mean time to repair (MTTR).
AI-based observability tools provide a better way to sift through logs and identify root-cause indicators. In contrast to IT infrastructure monitoring technologies that merely identify what’s working and what’s not, observability solutions seek to understand why something is not working. Observability technology collects and analyzes externally visible symptoms to help IT teams understand more about the internal state of a system or application. AI-based observability uses machine learning and artificial intelligence to speed this process, ingesting logs, traces, and metrics to derive useful insights into application performance.
The scale, complexity, and speed of evolution in software are growing so quickly that ITOM teams barely know what to look for or where to start when things break down. The limitations of the human brain have become a bottleneck to maintaining application availability and performance. With powerful AI-based observability solutions, ITOM teams can access automated, real-time insights that enable swift resolution of issues and the ability to proactively address potential problems before they occur.
Skylar Automated RCA AI Log Analysis
Skylar Automated RCA AI Log Analysis provides AI-based observability capabilities that do the heavy lifting for you when business services go down. To identify root cause, Skylar Automated RCA automatically ingests and runs machine learning (ML) log analysis across millions or billions of messages from log files across your applications and infrastructure—in real time—enabling your teams to identify and fix incidents more quickly.
With Skylar Automated RCA, you can:
- Diagnose issues 10x faster. Skylar Automated RCA eliminates most of the manual work involved in diagnosing root cause from logs. Using ML log analysis, it processes enormous volumes of log messages in real time, dramatically minimizing the time required to understand what’s broken and where to begin troubleshooting and repair.
- Identify unknowns. The complexity of modern business services makes it hard to anticipate what may break down next. Skylar Automated RCA lets your team catch new problems without having to sift through log data or manually build complex rules. This technology lets you identify unusual or novel issues and root causes, even when your ITOM teams and monitoring tools don’t know what to look for.
- Understand what’s happening. Skylar Automated RCA lets you see the story your logs are telling about your IT environment, even if you don’t know the vocabulary and syntax of a particular log. It distills billions of log lines down to the few most salient to identify root cause, providing plain language root cause summaries.
Why choose Skylar Automated RCA?
Skylar Automated RCA is a proven tool that delivers a fast and easy root cause analysis experience.
- Rich integrations. Skylar Automated RCA lets you turbocharge the effectiveness of observability, APM, notification, and collaboration tools you’ve already deployed. You can send root-cause insights directly to the most common AIOps and observability platforms. Eliminate duplicate efforts and improve efficiency by synchronizing common incidents and notification tools to manage alerts and messages.
- Flexible deployment. Skylar Automated RCA can be deployed in the cloud, on-premises, and in modern cloud-native Kubernetes environments. It also supports ingest via Fluentd, Logstash, Cloudwatch, Syslog, API, and CLI.
- Real value from AI and ML. While many vendors hype their products by simply claiming AI and ML capabilities, we’ve conducted large-scale, quantitative, third-party studies of the effectiveness of our tool in real-world scenarios across multiple stacks. Additionally, Cisco conducted a multi-month study of 192 customer incidents and found that Skylar Automated RCA created a report at the right time, with the right root-cause indicators from the logs over 95% percent of the time.
- Support for monitoring platforms. While Skylar Automated RCA is part of the ScienceLogic platform, it can also be deployed as a standalone solution for dashboards from DataDog, New Relic, Dynatrace, Elastic/Kibana, Grafana, and AppDynamics.
About ScienceLogic
ScienceLogic is a leader in IT Operations Management, providing modern IT operations with actionable insights to predict and resolve problems faster. Along with Skylar Automated RCA, ScienceLogic offers additional technologies that transform operations and enable forward-looking leaders to enrich IT data context, enable automation, and drive better business outcomes.
- The ScienceLogic AI Platform is an IT infrastructure monitoring and AIOps platform that sees everything across multi-cloud and distributed architectures. SL1 contextualizes data through relationship mapping and provides ITOM teams with insight through integration and automation.
- ScienceLogic Restorepoint< provides compliance-focused network automation and configuration management that enables centralized and secure backup and recovery of network and security devices with always-on auditing and one-click recovery.
AI Observability FAQs
What is AI-based observability?
Observability is the combination of modern application performance monitoring (APM) and analytics. AI-based observability uses artificial intelligence and machine learning to ingest and analyze logs to identify root cause much faster and more accurately than IT teams can manage.
What is observability vs. monitoring?
IT infrastructure monitoring uses tools and processes to understand what is happening throughout an organization’s IT estate in real-time. Infrastructure monitoring solutions alert teams to outages and issues, providing a moment-to-moment view of the health of operations. In contrast, observability digs deeper to collect information about a device, service, or application, allowing IT teams to infer the internal state of a system based on externally visible symptoms. In plain language, IT infrastructure monitoring reveals when an IT asset is performing poorly or not at all, while observability provides clues as to why.
