IT downtime is no doubt a costly business. As soon as service starts to degrade, companies start to lose money. Studies by Gartner and IBM show that the average cost of unplanned downtime to enterprises ranges between a staggering $5,600 and $9,000 per minute. For ecommerce businesses, like Amazon, the stakes are even higher, potentially resulting in a loss of up to $220,000 for every minute of downtime.
However, the impact of downtime also extends beyond financial losses. When user service levels take a hit, customer satisfaction suffers, and corporate reputations are tarnished. Meanwhile, the relentless cycle of downtime erodes the productivity and morale of engineers who must drop everything to investigate the incident, address a mounting ticket queue, and implement a fix.
Addressing The Business Burden of Downtime
To minimize the business impact of downtime, IT leaders must find ways to improve IT’s ability to characterize and resolve service impacting incidents. The complexity of modern IT has made it increasingly difficult for teams to maintain a clear understanding of their systems, discern the complex relationships between their software services, and pinpoint areas where things can go wrong.
Furthermore, traditional software development methodologies have given way to continuous integration and continuous delivery (CI/CD), where new features are pushed to production in a matter of hours, rather than days or weeks. While this agility accelerates innovation, it also means that IT teams are constantly struggling to keep up with the pace of change and the ephemeral state of the IT environment.
Monitoring and Observability Only Go So Far
Common solutions to these challenges include monitoring and observability tools which collect telemetry and alert IT teams to issues. Yet, despite these investments, changes in the modern IT environment continually happen faster than efforts to improve Mean Time to Repair (MTTR). In most enterprises, MTTR for serious issues is well north of an hour, which means serious business disruptions. And even when these tools catch symptoms of an issue quickly, identifying the root cause of the problem typically spurs a time-consuming race against the clock as teams search through a mass of log files to find a solution. This results in both frustrated customers and crucial resources being redirected from other tasks.
Now imagine what it would be like to have artificial intelligence (AI) and machine learning (ML) do the heavy lifting for you – automatically ingesting and analyzing thousands of log streams from your applications and infrastructure in real-time and identifying the root cause of incidents so you can fix them faster.
For Faster MTTR and Better Business Outcomes, Automate Root Cause Analysis
AI and ML (aka AIOps) have long been promoted as enabling automated troubleshooting, yet these solutions are often limited to telemetry collection and automated workflows. In contrast, ScienceLogic has developed a unique approach.
ScienceLogic’s SL1 platform applies ML and event-driven automation to accelerate the troubleshooting and remediation process. Leveraging ML-driven root cause analysis capabilities, SL1 automatically analyzes thousands of logs from across your multi stack infrastructure in real time to diagnose issues 10x faster than manual log sifting. The platform also harnesses the power of large language models (LLMs) to explain the root cause in plain English for actionable remediation recommendations. Plus, unlike rules-based observability tools that struggle with unknown or novel problems, SL1 harnesses AI and ML to accurately and proactively detect problematic patterns and early warning indicators – both known and unknown – and associated root cause before they lead to serious incidents.
ScienceLogic’s Impact: The Results Speak for Themselves
Don’t just take our word for it. In contrast to competing vendors, ScienceLogic further sets itself apart with third-party evidence showcasing the platform’s proven effectiveness across real-world scenarios and multiple stacks. SL1 has been the subject of numerous customer studies underscoring, capabilities and benefits including:
- >66% reduction in unplanned downtime
- >95% measured accuracy in root cause identification
- ~80% reduction in time to resolution
The benefits of automated root cause analysis also extend to the C-suite. Faster issue resolution and reduced downtime protects revenues, enhances customer satisfaction, and improves the productivity of engineering resources.
Alleviating Downtime Challenges and Accelerating Recovery
AIOps and automated root cause analysis are reshaping the landscape of IT operations, offering innovative solutions to address the complexities and challenges posed by modern IT environments. As your organization strives for faster remediation, increased resilience, and improved customer satisfaction, ScienceLogic is a powerful ally to help you achieve these objectives.
For more information on ScienceLogic’s SL1 automated root cause analysis capabilities, please visit https://sciencelogic.com/automated-root-cause-analysis.
