Think about IT: Why You Can’t Use an L1 Tool to Solve an L3 IT Incident 

As IT environments become more complex — and even fickle to accommodate regular updates and changes —, enterprise IT is constantly seeking ways to improve incident resolution and reduce the workload of their most skilled engineers. “Shifting left” is a well-known strategy in IT, where incident resolution tasks traditionally handled by Level 3 (L3) support teams are moved down to lower levels, such as Level 1 (L1), or even to self-service (Level 0). Root cause analysis (RCA) based on machine learning and automation makes this shift possible. The goal is to streamline operations, reduce costs, and improve resolution times. 

But what happens when an L3 incident does require specialized expertise or when there are deeply rooted system issues? You simply cannot use an L1 tool to solve many L3 incidents, and “shifting left” is impossible. That’s like trying to start your broken-down, old car with a 2024 Maserati key. The likely result is that your car will be stuck in the driveway for quite some time. 

So, when it comes to complex L3 incidents, robust datasets and machine learning (ML) are imperative. By using advanced analytics and RCA based on ML models, IT departments not only resolve these challenging L3 incidents faster but also learn from them to prevent future occurrences. Tools with the capability to resolve only L1 issues do not have the data and visibility needed to see issues across the entire IT estate and pinpoint the root cause of more nuanced problems. 

When an L3 issue escalates, IT service desks need powerful diagnostics to help get to the root cause quickly. The ability to conduct root cause analysis empowers service desk teams to accelerate troubleshooting for the most complex technical issues, enhancing the digital employee experience, engagement, and productivity.  

Lakeside’s L3 root cause analysis capability in SysTrack combines historical and real-time data across the IT estate to run automated diagnostics and provide detailed drilldowns for L3 technicians to triage either physical or virtual desktops. SysTrack’s L3 root cause analysis centralizes both the collection and analysis of data related to system performance, application behavior, and environment bottlenecks, by using:  

• Automated diagnostics 

• 1,300+ smart sensors 

• Dependency mapping 

• Powerful visualizations 

This built-in L3 expertise stands out among DEX solutions in the market today. In fact, in the Forrester Wave™: End User Experience Management, Q3 2024, customers cited Lakeside’s “exceptional support and extremely robust RCA capabilities” and “significant cost savings from using (SysTrack).” The report also noted SysTrack’s “real-time reporting and market-leading data retention policies to enable deep historical analysis, making it an excellent tool for level-three RCA.”

Understanding L3 Incidents and the Importance of Root Cause Analysis 

L3 incidents typically involve the most complex and critical issues in an IT environment. These could range from system outages such as a Blue Screen of Death or software malfunctions that require a high level of technical expertise to diagnose and fix. Unlike L1 or L2 incidents, which often are resolved through predefined scripts or automations, difficult L3 issues demand deep investigation into the underlying causes of the problem. 

Root cause analysis is critical in these cases. Rather than treating the symptoms, RCA aims to identify the core issue that triggered the incident in the first place. Conducting RCA for L3 incidents, however, can be time-consuming, requiring collaboration among multiple teams and sifting through vast amounts of data. Accordingly, a DEX tool that may thrive when it comes to L1 ticket resolution may not be the best option for solving complex L3 issues. What’s more, machine learning can make a significant impact by automating parts of the RCA process, enabling faster and more accurate resolution. That is why the depth, breadth, history, and quality of data matter. Lakeside SysTrack, for example, collects more data than any other DEX tool on the market. Specifically, it collects 10,000 data points every 15 seconds from an endpoint.  

Using Lakeside SysTrack, a U.K.-based global law firm’s IT team, for example, was able to: 

• Detect three sensors going off thanks to ML-based anomaly detection, impacting 800 machines in the environment, or nearly 10% of staff. 

• Investigate the root cause of the spiking CPU and discover the culprit was a common video driver. 

• Resolve the issue with a driver update before the issue hit the whole firm and affected employee 

How Machine Learning Enhances Root Cause Analysis 

Why does Lakeside SysTrack stand out as a go-tool DEX tool for L3 incident response (in addition to solving L1 and L3 tickets)? The differentiators boil down to two things: data and AI based on machine learning. Machine learning, when applied to IT operations, enables systems to analyze large datasets, identify patterns, and predict potential issues before they escalate. For L3 incidents, ML models can be trained on historical incident data to recognize trends, common failure points, and correlations between various system events. Here is how ML-powered RCA works in practice: 

1. Anomaly Detection: ML models can continuously monitor system behavior to detect anomalies in real-time. These anomalies, whether they are unexpected spikes in network traffic or unusual application response times, often serve as early indicators of larger problems. Identifying these anomalies early allows IT teams to focus their investigation on specific areas, reducing the time needed for RCA. 

2. Automated Correlation: When an L3 incident occurs, a major challenge is understanding its relationship with other system events. ML can automate this process by correlating the incident with other events across the infrastructure, such as recent software updates, configuration changes, or performance degradation in related systems. This automated correlation narrows down the possible root causes, enabling IT teams to take more targeted action. 

3. Historical Analysis: ML can analyze past incidents to discover recurring patterns. For example, if similar issues have occurred in the past due to a specific network configuration, the ML model will suggest this issue as a probable cause when a new incident arises. Over time, as more incidents are logged and resolved, the model becomes increasingly accurate in its predictions. Here, data history matters; it is why Lakeside SysTrack stores data for up to three years. Other DEX tools simply do not take this extra step related to endpoint data collection. 

4. Proactive Recommendations: Beyond simply identifying the root cause, ML can provide proactive recommendations based on historical data and predicted trends. If the ML model predicts that a similar incident is likely to happen again due to recurring system issues, it can suggest preventive measures such as software patches or system reconfigurations. 

Business Value of Using Data and Machine Learning for L3 Ticket Resolution 

The integration of machine learning into the RCA process for L3 incidents offers several significant business benefits: 

• Faster Incident Resolution: By automating parts of the investigation process, IT teams can resolve L3 incidents faster, reducing downtime and minimizing the impact on business operations. This improved efficiency reduces mean time to resolution (MTTR). 

• Cost Efficiency: L3 incidents are typically the most expensive to resolve due to the high level of expertise required. Machine learning reduces the need for manual investigation, allowing organizations to save on operational costs while still maintaining high-quality resolutions. 

• Better Digital Employee Experience: RCA allows for troubleshooting without interrupting end users, allowing IT teams to maintain strong digital employee experience. 

• Continuous Improvement: Machine learning models improve over time as they are trained on more incident data, leading to increasingly accurate RCA and more proactive incident prevention. 

• Increased IT Resilience: Proactive identification of root causes and early anomaly detection enhance the overall resilience of the IT environment, allowing businesses to avoid outages and maintain service availability. 

As IT environments grow more complex, managing L3 incidents through traditional methods is no longer sufficient. Leveraging data and machine learning for root cause analysis transforms how organizations approach these complex issues, allowing for faster, more accurate resolutions and preventing future incidents from arising. Data provides the big picture and granular visibility RCA needs to resolve complex L3 issues related to endpoint devices, software, networks, web applications, and the historical performance of the device or IT estate.   

IT leaders who invest in ML-powered solutions for L3 tickets such as SysTrack not only will reduce operational costs but also build a more resilient, future-ready IT infrastructure. Because SysTrack also is a powerful tool for resolving L1 and L2 tickets, why not choose the DEX solution that does IT all?