Stop aiming for a perfect observability strategy

Change is the only constant in today’s continuously shifting IT landscape. Whether you’re adding new observability tools, retiring existing monitoring systems, establishing new business units, or onboarding IT systems from acquisitions, managing these non-stop changes can challenge even your expert ITOps team.

Trying to get your monitoring house in order is a daunting task. And it’s even more difficult if you have monitoring gaps, observability redundancy, a maze of tools, or endless technical debt. Does this describe your reality? Rest assured that you’re far from alone – and that there’s a better way to improve your monitoring performance.

At BigPanda, we’ve talked to hundreds of companies in similar circumstances. Keeping up can feel overwhelming, but by incorporating AIOps into your monitoring and observability strategy, you can smoothly navigate an ever-changing IT landscape.

Read on to learn:

• How AIOps elevates your observability strategy for better monitoring performance
• Do you need to get your monitoring and observability in order?
• Improve observability with AIOps
• Take your observability from Phase 1 (Reactive) to Phase 2 (Proactive)
• Take your observability from Phase 2 (Proactive) to Phase 3 (Preventative)
• Achieve observability excellence with AIOps

Do you need to get your monitoring and observability in order?

While many companies believe they must ‘perfect’ their existing monitoring and observability tool performance for real-time, comprehensive application or infrastructure insights, this isn’t necessary. This mindset can lead to a never-ending proliferation of change management, service management, and monitoring tools for this ever-changing technology landscape. 

Each time a new tool is introduced, it serves a specific use case for a particular area. But accumulating tools creates a constant battle to simplify the monitoring stack. Organizations struggle to efficiently leverage their existing resources instead of adding to them. 

But without visibility into which tools deliver real value, it can be difficult to know where to invest – and where to cut bait. Rather than working tirelessly to achieve the ideal monitoring state, AIOps can help companies achieve these improved monitoring outcomes through AI, ML, and automation.

“Don’t wait to start your AIOps journey once you are overwhelmed with alerts. Start early to get a single pane of glass to understand which monitoring tools you really need.” — Sanjay Chandra, Vice President of Information Technology, Lucid Motors.

Improve observability strategy with AIOps

Using AIOps together with observability is the solution for fast-moving IT landscapes. Using AIOps as part of your observability strategy makes changes to data sources, monitoring tools, or managed services almost frictionless. BigPanda offers these capacities, which are especially critical for DevOps, SREs, and other agile teams to eliminate change risks. 

However, your path to improved observability depends on your current maturity state. First, we’ll discuss how companies with less observability maturity can use AI to improve. Then, we’ll explore how companies with more sophisticated observability can take their capacities to the next level with AIOps.

Take observability from Phase 1 (Reactive) to Phase 2 (Proactive) 

Are your company’s monitoring and observability tools still evolving? Or maybe you’re rebuilding a gap-filled monitoring/observability stack. Within the AIOps maturity model, your monitoring or observability maturity is likely at Phase 1 or the Reactive phase. What are the characteristics of this phase?

• Your monitoring tools are adequate but siloed within domain teams. 
• Monitoring systems may generate significant alerts for low-priority or non-actionable issues. 
• Extensive monitoring coverage exists, but alerts need more context and filtering. Your monitoring tools result in alert noise rather than clarity.

However, if your company’s observability is already proactive and addressing these issues, you can skip ahead to the next section, ‘Go from Proactive to Preventative observability.’ Now let’s discuss how to improve your Reactive observability with AI.

• Enhance monitoring visibility: AIOps clarifies the complex web between servers and applications, providing an interconnected view of your IT system. This complete overview aids in quickly resolving incidents and strengthens the system’s robustness.
• Streamline alerts: If you use a system like ServiceNow, introducing AIOps can refine, group, and prioritize alerts. This results in fewer but more relevant alerts and faster incident response.
• Boost efficiency with context: Placing an AIOps solution between your monitoring and ticketing systems can be transformative. AI-driven insights and automation help to focus on real issues, freeing up your ITOps team and streamlining the incident handling process.

Take observability from Phase 2 (Proactive) to Phase 3 (Preventative) 

On the other hand, maybe your IT organization has been around the block a few times. Or perhaps you have a legacy stack of services to support and a modern collection of cloud-based applications to bring online. Maybe you’re inheriting new infrastructure through acquisitions. In any case, you likely have an extensive collection of monitoring tools, noise, and overlapping visibility problems. 

Do these proactive characteristics sound like your organization?

• Able to aggregate and correlate cross-domain alerts and reduce alert noise from monitoring and observability platforms. 
• Alerts may lack the necessary context and filtering.
• Teams must improve in identifying what is actionable and essential to reduce alert noise further.

Are you tempted to optimize your existing monitoring tools before adding an AIOps solution? While garbage in can equal garbage out, even messy monitoring can greatly benefit from using an AIOps solution early on. Here’s what this looks like for Proactive organizations:

• Streamline alerts and boost efficiency: AIOps consolidates and enriches alerts, pinpointing visibility gaps and potential root causes. This AI enhancement reduces alert overload, sharpens visibility, and makes incidents more actionable.
• Add context for better insights: While standard monitoring tools offer technical data, they can miss the bigger operational context and incident insights. AIOps fills this gap, giving you a detailed view of your IT landscape, showing how assets connect, and spotlighting customer-affecting incidents. Plus, AI-driven filtering identifies the important alerts, keeping your ITOps team on track.
• Optimize monitoring resources: With AIOps’ clear dashboards and analytics views, your ITOps team can now easily see which monitoring tools provide value and which are redundant – valuable information when budget season rolls around.

Achieve observability strategy excellence with AIOps

Relying solely on observability makes it hard to keep pace with today’s rapidly evolving IT environment. However, using AI and automation together with observability bridges these gaps to enhance visibility, refine, correlate alerts, and adapt seamlessly to the dynamic changes within your tech stack. Take it from Alvin Smith, VP of Infrastructure of Global Operations at IHG, who shared how BigPanda can help move from one observability tool to another. 

Smith also stated, “[BigPanda] can look in other areas outside of our typical monitoring. We can work with our application teams and look at some of the data that they’re leveraging and add BigPanda’s enrichment,” 

BigPanda has helped organizations with all levels of observability maturity — including some of the world’s largest companies. Connect with us for a personalized demo to see how leveraging BigPanda ensures more efficient monitoring, a smarter observability strategy, and reclaims your ITOps team bandwidth in a fast-paced digital landscape.