Steps to Taming Hybrid Cloud Complexity: Eliminating Visibility Gaps & Enabling Actionable AI-Powered Insights

Steps to Taming Hybrid Cloud Complexity: Eliminating Visibility Gaps & Enabling Actionable AI-Powered Insights 

For years “the move to the cloud” implied a singular event – a singular migration to a singular entity. It all sounded so simple. Yet, the “simple” act of moving to the cloud stands in stark contrast to the reality of today’s complex, hybrid IT estates where the overwhelming volume of data flows can make it challenging for IT teams to effectively pinpoint and rectify service incidents. 

IT operations (ITOps) teams must manage a labor-intensive mix of legacy and modern applications and infrastructure. For the teams tasked with monitoring and managing service levels – and minimizing business disruption – these complex environments only add to the massive increases in volume, variety, and velocity of data to be managed, and subsequent service insights that need to be pulled from that information. 

How can teams respond to this complexity when monitoring tools are vendor-specific and only provide visibility into certain technologies or domains, such as cloud instances, network equipment, databases, and so on? 

And once IT teams have eyes on their data, how can they transform the information into meaningful insights and actionable intelligence? How can they infuse context into the data to discern service trends, patterns, and events that have the potential to trigger performance issues – before they occur? 

Without the support of comprehensive stack observability and automation, they can’t.  

As a result, ITOps teams lack unified, consistent, contextualized visibility of the IT estate that business services rely on, leaving them to struggle with retroactive incident management among a range of other challenges including: 

• Limited awareness of service status and visibility gaps 
• Isolated reporting and manual analysis 
• Human-intensive data aggregation, analysis, interpretation, and action 
• Inconsistent processes, difficult integrations, and time-consuming workflows 
• Difficulty identifying and understanding the relationships between IT system components 
• Reactive, human-driven remediation 
• Barriers to proactively pinpoint and addressing incident risk 
• Missed SLAs and downtime 

The business impacts are significant. Manual root cause identification is time-consuming, challenging, and can take hours if not months of labor to complete. This human-powered log analysis typically accounts for up to 70% of the time it takes to restore service operations following an incident. And failure to visualize and understand hybrid cloud infrastructure realities means that IT may struggle to support the rapidly changing demand of the digital business, power new services, and deliver a consistent and reliable customer experience. 

ScienceLogic’s Hollywood release, the latest update to the company’s SL1 IT infrastructure monitoring platform, helps businesses solve these problems. 

Step 1: Collect, consolidate, and see all your data in one place 

SL1 applies advanced analytics in the form of machine learning (ML) and artificial intelligence (AI) – aka AIOps – to provide intelligent, actionable insights into the different layers of technologies that make up an organization’s IT infrastructure as well as the dependencies between those tools and applications.  

With a watchful eye, SL1 does what humans can’t by creating a real-time operational data lake. The platform automatically collects and consolidates performance data from every layer in the organization’s stack, delivering a comprehensive view of what’s happening in its IT environment. 

Rather than jumping between disparate monitoring tools, ITOps teams get a seamless experience via a single user interface and at-a-glance insights with SL1. This reduces the time spent troubleshooting performance issues in complex IT environments and allows teams to quickly drill into problematic devices or services to easily correlate events and business impact.  

This holistic view of the organization’s stack also enables IT operations teams to quickly onboard new assets without risk of visibility gaps. Additionally, with the SL1 Studio low/no-code tools, teams can reduce custom monitoring build time by 8x, enabling not only seamless onboarding, but also the monitoring of new devices and services.  

Step 2: Gain actionable human-friendly service insights 

In addition to achieving visibility, the Hollywood update to SL1 also empowers IT teams to address these challenges and go beyond observability to understanding – to create a comprehensive system record of the status of an organization’s IT environment.  

Once data is captured and organized, SL1 leverages AI/ML capabilities to automatically correlate and analyze telemetry and contextual information to gain actionable insights into system health. And, in today’s increasingly automated world, SL1 applies generative AI/ML algorithms to proactively detect rare or anomalous service behavior and then correlates those anomalies and events within a service context, automatically cutting through the noise of to uncover critical issues and diagnose root cause up to 10x faster.   

And, because the platform can sift through massive volumes of data, ITOps teams can stay one step ahead of their constantly changing environments. 

What does this mean for ITOps and the business? With SL1, IT operations managers can:  

• Rapidly identify service-impacting issues as early as possible so they don’t impact customer experiences. Predictions are provided in an easy to interpret format, drawing on relevant contextual information to help understand outcomes and prioritize work based on business impact.  
• Unlike other large language models that learn as they go, SL1 uses unsupervised AI to identify novel issues and events, even without prior knowledge. 
• Automatically analyze and determine issue root causes in real-time with 95% accuracy, reducing noise, reliance on multiple tools, and manual log sifting while speeding MTTR. 

Looking ahead 

Overall, moving from manual ops to a machine-powered, self-aware IT state is just one step on the journey to continued innovation. In fact, 84% of organizations see AIOps as a means to achieve a fully automated network, and 86% expect to do so within the next five years. 

SL1 doesn’t stop at just delivering insights. It provides recommendations to optimize IT estates, supports automated remediation workflows, and breaks down barriers to self-aware, self-healing, and self-optimizing operations. The platform delivers the foundation necessary to support the shift from modern AIOps to fully automated, autonomic network operations.  

Visit https://sciencelogic.com/platform/overview to learn more about how SL1 supports the AIOps journey.