What is Observability and Why It’s Essential to Effective AIOps
Modern hybrid IT estates generate huge volumes of data at velocity, a testament to today’s digital reality.
But for IT Operations Management (ITOM) teams charged with keeping tabs on system health, this data proliferation can be a nightmare scenario. Tool sprawl, alert storms, manual analysis, and disconnected insights make maintaining a current state, let alone supporting better business outcomes, a perpetual challenge.
In addition, as cloud-native applications become more interconnected, the complexity of monitoring these systems and understanding interdependencies across the IT estate increases, amplifying the impact of any potential failures.
As IT architectures grow increasingly complex, ITOM teams are increasingly seeking enhanced observability to swiftly identify and address issues on-premises, in the cloud, and across hybrid IT environments.
What is Observability?
Observability goes beyond traditional monitoring – which focuses solely on singular cloud stacks or a fixed set of devices – to provide end-to-end visibility across the entire IT infrastructure.
And, unlike the concept of monitoring, which primarily checks if something is working or not, observability delves deeper into understanding system performance within the context of business services and the broader IT environment.
Observability achieves these outcomes using machine learning and artificial intelligence (AI) to analyze log events, detect anomalies, and deliver precise root cause analysis insights (with up to 95% accuracy). Instead of manually sifting through traces and logs, observability enables ITOM teams to access automated, real-time insights into operational status, impacts of potential issues on services, and recommended actions for quick incident resolution.
In fact, the proactive nature of SL1’s observability insights could help enterprises reduce nearly 15% of overall incidents, potentially saving more than $200,000 annually depending on the size of the organization.
Moreover, observability insights, such as root cause analysis and contextual understanding, can benefit other teams, including DevOps. By swiftly resolving performance issues and outages, DevOps teams can allocate more time to improving application deployment frequency, ensuring high-quality code delivery, and aligning with business goals.
Overall, observability empowers ITOM teams to efficiently navigate vast data volumes, gain essential insights, and proactively troubleshoot to maintain uninterrupted business services and user experience.
How Observability Can Accelerate Your AIOps Journey
Compared to just “monitoring” the IT estate, the enriched insights provided by observability – both “seeing” and “contextualizing” – are the cornerstone for more advanced functionalities like self-operating systems. These systems can enrich tickets, resolve issues autonomously, and proactively address potential problems before they occur – representing the true potential of AIOps.
Indeed, ScienceLogic’s vision for AIOps is one of “Autonomic IT,” in which organizations can use the insights that observability brings to connect their IT estates together, drive automated workflows, consolidate tools, and automatically spot and resolve risk before it impacts business operations.
At the same time, autonomic IT helps gain operational efficiencies and frees IT teams to be more successful and focus on new innovations and business opportunities.
In conclusion, observability plays a pivotal role in the transformation of AIOps within ITOM. Without incorporating observability into any AIOps journey, the full realization of AIOps and the achievement of autonomic IT are not possible. Therefore, observability stands as a crucial component in completing the puzzle of autonomic IT.
Get Started with Machine-Driven Observability
Learn more about how ScienceLogic SL1 can help you unlock the power of AIOps to see all your data in one place, contextualize it for actionable insights, and automate common triage and remediation actions.
