This article was originally published in devmio.
The roar of engines is deafening. The crowd screams in sheer excitement.
While thousands of data points are coming to your display every second, your drivers are shouting into their radios, your engineers are informing you about an electrical issue, and your analysts have created five different potential new strategies. And you need to figure out what to do next.
While this is the life of a Formula 1® (F1) racing team principal, it’s not too far off from that of an app developer (okay, maybe a little, but stay with me here).
During the software development lifecycle, massive amounts of data are always available to provide insights into errors, performance, and user experience. Whether it’s how to optimize tire degradation on a sweltering asphalt racetrack or ensuring that millions of users can complete your checkout flow without a hitch, there are strategies and processes that need constant updating based on the data that you have amassed.
Let’s look deeper into what developers can learn from F1.
Running an F1 team is no easy feat – every team principal is responsible for the success and performance of their cars and drivers, as well as managing hundreds of team members. In addition to handling internal functions like financial management and creating team culture, team principals are also in charge of external communications with the press and governing bodies.
During a race, team principals and their engineering teams are in charge of monitoring and interpreting vast amounts of data. On each team’s pit wall, dozens of computer monitors display track temperatures, tire degradation rates, drivers’ race pace, and many more points of data.
This data comes directly from their cars, which can collect up to 10,000 data points per second. Most grand prix races last around 90 minutes to two hours, meaning that teams receive upwards of 50 million data points per race.
In F1, every millisecond counts. It’s impossible to comb through every single data point coming in, so team principals, engineers, and analysts use a number of computing tools to prioritize the most important metrics and insights. As a result, they make critical decisions about their race strategy, driver management, and how to run their cars in order to extract the highest level of performance.
In this modern era of F1, teams constantly implement new technologies like machine learning, artificial intelligence, and cloud-based data storage to streamline their workflows and increase the chances of success.
Much like F1, it’s important to have visibility across the software development lifecycle, especially since it’s all about data – how you receive it, how you interpret it, and how you act upon those findings.
Practicing observability is one way that your software development team can use data to analyze key insights, prioritize the most important issues, and figure out what needs to happen next.
Observability has become especially critical to software organizations as applications become more complex and decentralized. Using observability tools and practices, developers can gain insights into the infrastructure behind their applications. Only then can developers easily identify the true source of problems. By streamlining error and performance resolutions, companies can focus on beating the competition without sacrificing user experience.
One of the most critical aspects of observability is prioritization. Many monitoring tools and processes can be extremely noisy and overactive, causing developers to waste time fixing issues that should be deprioritized in favor of creating new features or resolving more important problems.
When 500 errors or performance issues come in at the same time, your developers need tools to figure out their next step quickly. Should they focus on fixing a minor bug on the home page? Or should they fix a critical error in your app’s e-commerce checkout flow?
Any developer would be able to make the correct choice, but a great observability tool prioritizes these issues for you, streamlining the process and saving everyone’s time. This allows developers to implement aspects of progressive delivery, following a model of Continuous Integration and Continuous Delivery (CI/CD).
The high-octane world of F1 racing reveals some invaluable lessons for software developers and organizations seeking to up their game.
Under the guidance of team principals, F1 teams exemplify the role of data in achieving peak performance. In the chaos of a race, the meticulous analysis of real-time data empowers critical decisions and fosters a culture of continuous improvement. F1 teams are a great example of practicing the fusion of data-driven insights and innovative tools to optimize outcomes.
In the world of software development, the concept of observability emerges as a pivotal strategy. Practicing a strategy of observability enables developers to conduct efficient prioritization, ensuring that critical problems receive prompt attention while fostering an environment where innovation and progress can thrive.
Just as F1 teams depend on data to make informed decisions about strategy and vehicle performance, modern developers can harness observability in the same way. It lets them find the right data to streamline workflows and enhance the user experience.
At the end of the day, software development may be slightly less exhilarating than F1 races. But don’t let the lack of cheers and revving engines fool you – developers are certainly on the forefront of innovation and continuous optimization just the same as any F1 organization. And by practicing observability and prioritization, you can much more easily achieve your goals.
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