Small Steps, Big Gains: Kickstart Observability with Early Wins that Drive Impact

As the newly appointed observability leader at a large enterprise, Alex felt the weight of the task ahead. With thousands of stakeholders depending on them, Alex faced the daunting challenge of rolling out an observability strategy that would deliver measurable results—and fast. The scope was staggering: sprawling applications, multiple teams spread across the globe, and a flood of data to manage. Leadership expected swift insights, but Alex knew tackling such a vast initiative without focus could lead to more problems than solutions.

I can relate to Alex’s challenge all too well. Having spent years in a large enterprise, I often dreamed of the day when observability would become ubiquitous—an unquestioned, essential part of our DevOps practice. Describing the task as daunting is putting it mildly. Implementing successful observability requires a solid strategy and significant investments in time, finances, resources, staff and cross-functional team coordination. As more digitally-forward companies seek an edge, observability is quickly becoming an integral strategy that optimizes performance and bolsters a competitive advantage.

Back to Alex’s challenge, I’d invite you to think about how you can make smaller, powerful impacts that will keep your initiative moving forward and have your boss shouting your praises.?

Breaking Down Observability into Smaller Components

Observability centers around collecting metrics, traces, and logs to generate actionable insights on application and infrastructure performance. With these insights, businesses can refine customer experiences, accelerate innovation, and boost revenue. But here’s a little secret: you don’t need a fully mature observability program to start reaping the benefits.

In my role I talk with hundreds of observability practitioners across industries, and during those discussions I often suggest breaking the challenge down into manageable steps. If your organization is just beginning its observability journey, you’ve likely tied it to broader business goals, such as improving customer experience, driving revenue, or accelerating innovation. But it’s crucial to remember that observability can? also be a tool that helps achieve your strategy– versus a burden that complicates it. Starting small and resisting the urge to aim for perfection or tackle everything at once will help you maintain focus and keep the project moving forward. It’s also great for reducing stress and improving your mood.

Traces: The Sometimes Forgotten Hero of Telemetry Data

Among the three critical pillars of observability, traces are my favorite when it comes to identifying problems. The reason is simple: traces give you both the data and context to understand performance, often with visualizations that pinpoint negative contributors in your technology stack or code.?

A trace represents the path of a single transaction as it flows through your system, with spans capturing the smaller, individual steps within that path. Despite their value, many people overlook traces because they believe you need a complete, end-to-end picture before they become useful. To those who hold this belief, I challenge you to think differently.?

Starting with a small but problematic code may yield faster results than expected. Even a tiny, early success can attract attention, inspiring others to follow suit or trigger pressure from developer leadership to replicate the result.

To manage the sheer volume of trace data and keep costs under control, it’s essential to develop a sampling strategy. Techniques like head-based or tail-based sampling allow you to focus on the most critical data without collecting and storing everything, which can quickly drive up costs. These strategies help you gather actionable insights while being mindful of resource consumption.

The takeaway? Don’t assume that an incomplete trace lacks value early on. Driven by these early wins, full coverage will come in time.

Target a Few Key Metrics to Reap the Most Value

Much like with traces, if you’re just starting out, picking the right metrics may seem overwhelming. Starting simple can deliver surprising results.?

One of the best starting points is tracking load, or the volume of requests your service is handling, along with response time and error rate. Together, these three metrics are a powerful combination, offering an immediate understanding of your application’s health. Over time, you can add more metrics to provide greater detail or to reflect specific observability needs, such as Kafka replica count or Kubernetes pods, as just two examples.?

However, metrics can lead you astray if you’re not careful in selecting and interpreting them. Blindly picking metrics from documentation without fully understanding their purpose or context can result in noisy alerts and unnecessary distractions, especially during late-night incident calls.?

Understanding what each metric represents is crucial in ensuring you’re collecting the right level of detail. Without this discipline, you risk false positives and, over time, rising observability costs as unnecessary metrics add up.

Understanding the Increasingly Complex World of Logs

Logs provide fantastic context and depth when combined with your traces and metrics. They have been a long-time favorite for developers for troubleshooting. On the surface, they might seem even easier to collect than traces or metrics, but their value comes with unique challenges. In traditional systems, standardized logs like SYSLOG provide consistent and trusted data. But application logs are a different story, with wild variations in format and content. Developers often write logs to suit their own needs without worrying about context or clarity for others, since they understand the code intimately.

With the rise of cloud and microservices, application logs have exploded both in volume and distribution, making them harder to manage. The challenge is that logs need to be accessible and meaningful to those unfamiliar with the code.?

As with metrics, a lack of strategy around logging can lead to lower value and higher costs. To unlock the full value of logs, you’ll need to develop a strategy that includes methods for collecting, transforming, and distributing logs across your systems. Setting some level of standardization that outlines key log elements and format is also critical. Finally, it’s essential to consider both the source and destination of your logs. Executing on a clear telemetry pipeline strategy, for instance allows teams to efficiently process logs - enriching, transforming, and distributing them to drive greater value while keeping costs in check.

You can learn more about managing log complexity and costs here.

Keep SLOs in Mind as You Go After Your Quick Wins

As you focus on quick wins in your observability journey, it’s important to keep SLOs (Service Level Objectives) in mind. You may not be able to instrument an entire transaction immediately, but thinking early about how you’ll measure success is key. Planning for SLOs, even in small increments, ensures that your wins are driving toward long-term goals. For more insights on how SLOs, SLIs, and SLAs work together, visit this blog post.

Conclusion: Bringing it All Together with a Fresh Mindset

As you reflect on the points in this article, approach the challenge of implementing observability with a different mindset. Take the time to plan, build a strategy, and focus on small, highly visible wins that can help refine your approach and drive broader adoption. Remember to consider how you can cultivate an observability culture, reduce friction for developers and ensure your metrics, traces and logs are delivering real value. Observability is no small feat and it’s essential to stay grounded in the reasons you introduced it to your organization in the first place.

Ultimately, by starting smart and thinking strategically, your observability efforts will evolve from a program into a sustainable and impactful practice, directly impacting your organization’s highest-level business goals.?