Data drives our world. From research to implementation and operations, everything depends on high-quality data. The opposite is also true; low-quality data puts our businesses at risk of heading in the wrong direction or making tremendous mistakes.

In Performance Engineering, our bread and butter consists of drawing the correct conclusion based on the data we capture. Small mistakes or a lack of experience in this sometimes complex discipline can result in failing our mission of optimizing the systems.

Why Performance Engineering?

Product owners usually expect that their systems provide high-quality services to their customers. While functionality is essential, user experience, application performance, and resiliency become more dominant these days. Research shows that users are more likely to return after experiencing failed interactions than those who suffered due to the slowness of digital services. In Performance engineering, we work with developers, architects, testers, and operational teams to ensure implemented systems can handle production volumes and fulfill performance requirements. After working for more than 25 years in the performance engineering domain, it is still exciting because we are involved with a broad stack of technologies, can break things on purpose, and collaborate with the entire organization.

Data Analytics and Performance Engineering?

By drawing the correct conclusions from the captured data, performance engineering can save customers a lot of money. Imagine you simulate inappropriate load patterns on your new banking system. Everything goes fine, with no performance issues, but after a few hours, the application gets stuck and blocks your entire bank. The data analytics principles outlined below will help you avoid such situations.

Descriptive to understand what is happening

We use live data captured from observability or other monitoring systems to understand how our systems work. This is the most critical feedback for operational teams, and it decides the course of their actions. A lack of understanding of what is happening leads to system outages because teams will miss critical signals and fail to implement corrective actions.

Diagnostic to identify the root cause

Manual troubleshooting performance problems is time-consuming, which is why automated root cause analysis systems are so popular today. RCA (Root cause analysis) systems go one level deeper because they discover why things are happening. One typical use case that shows how descriptive and diagnostic data analysis are related is detecting slowness in a checking process (explanatory) and highlighting the responsible database query (diagnostic).

Predictive to forecast the future

Ideally, we identify the root cause of performance issues and predict how this would impact our business. Imagine the banking system serves 1 million logins every day. If you discover a bottleneck in the login process, you could highlight how this problem would impact your organization. When product teams prioritize issues, they rely on such predictions to solve the most critical problems immediately. Missing this crucial information could set you up for failure.

Prescriptive to recommend the best course of action

There are many ways to fix performance bottlenecks. When deciding how to solve these problems, we must balance plans, goals, and objectives. An advanced algorithm could test potential outcomes and recommend the best action. This domain holds enormous potential because it could bring further automated problem remediation capabilities and reduce manual work.

Keep up the great work! Happy Performance Engineering!

References

(V Kachchini, Y Kothiya, 2024). Data Analytics. https://medium.com/co-learning-lounge/types-of-data-analytics-descriptive-diagnostic-predictive-prescriptive-922654ce8f8f