In today’s fast-paced digital landscape, having access to accurate and actionable data is more critical than ever. At Rely.io, we strive to empower development teams by providing them with the tools they need to succeed.

Recently, there has been significant buzz on social media and several insightful blog posts from industry players discussing the ease of collecting metrics making it look like it’s an easy task. While we acknowledge the importance of this conversation, we believe there is a misconception regarding the simplicity of metric collection.

Collecting the right metrics is indeed a tough challenge for engineers and engineering leaders, as it often involves gathering data from multiple tools that collect, represent and interpret metrics in different ways. More importantly, we agree with many voices in the industry on one key point: the real challenge lies in understanding what to do with the metrics once they are collected.

In this post, we’ll explore how Rely.io simplifies metric collection and how our new Engineering Performance capability helps teams turn metrics into actionable insights.

Collecting the right metrics is a complex task that many teams struggle with. It requires not only identifying the key performance indicators that matter most but also integrating data from various tools that may interpret and present metrics differently. This fragmentation can lead to confusion and inefficiencies, making it difficult for engineering teams to gain a clear understanding of their performance.

For instance, consider the end-to-end process of pushing a code change to production. It may start with a developer merging their changes in a GitHub repository. The next step typically involves using a continuous deployment tool like ArgoCD to capture those changes and deploy the latest version to a Kubernetes cluster. The entire workflow generates a wealth of metrics: from commit frequency and build success rates in GitHub to deployment times and resource utilization in Kubernetes.

Tracking this process so far already includes two different tools. Also to map this CI/CD process properly you already need a third component - typically a commit hash or docker image tag.

However, the challenge doesn’t stop there. To truly understand the impact of these changes, teams also need to correlate this deployment data with incident management tools like PagerDuty. When an incident occurs, it’s crucial to identify whether it was triggered by a recent deployment or if it’s a recurring issue. This requires a holistic view of metrics across multiple systems, which can often be siloed and difficult to integrate.

At Rely.io, we recognize these challenges and have developed our Engineering Performance capability to address them head-on. Our platform simplifies the process of collecting and interpreting metrics by providing seamless integrations with a wide range of development tools. This ensures that teams can collect relevant data consistently and in an automated way, allowing for a more accurate and comprehensive view of their performance.

By unifying these disparate data sources, Rely.io enables teams to track their metrics end-to-end, from code merge to deployment and incident resolution. This comprehensive approach not only helps in identifying correlations between deployments and incidents but also empowers teams to make data-driven decisions that enhance their operational efficiency.

Having all your metrics in one place offers numerous advantages, including improved visibility, enhanced collaboration, and better decision-making. When teams can access a single source of truth for their data, they can align their strategies and efforts more effectively.

Rely.io’s Engineering Performance capability exemplifies this benefit by providing a comprehensive view of all metrics relevant to a team’s goals. This centralization not only streamlines communication among team members but also fosters a culture of transparency and accountability. Teams can easily share insights and collaborate on data-driven initiatives, ultimately leading to improved outcomes.

The same applies towards service operational excellence where Engineering leaders may benefit from having visibility across all the services they own and understand how they are behaving in production. This allows them not only to drive changes that impact the way that teams work but also the way that services perform.