According to industry data, more than half of all developers would be open to new opportunities if the right one came their way. This makes developer recruiting teams think: what do developers care about when they evaluate new opportunities? And how do you attract and keep top developer talent?

There are many issues that can contribute to developer dissatisfaction on the job — inadequate pay and work-life imbalance, for example. But increasingly there's also a troubling and growing sense of lacking ownership and feeling out of control. As a developer, even if you produce the best code in the world, there's always a dependency on other things you didn't build that will ultimately impact how your code performs in the real world.

One key way to increase job satisfaction is to ameliorate this sense of ownership and control whenever possible, and approaches to observability offer several ways to do this. For instance:

All Data Matters

Observability is the task of collecting raw telemetry data — logs, metrics and traces — to achieve deep visibility into distributed applications and systems. With observability, organizations can proactively monitor application and system health and troubleshoot when necessary to get to the root cause of issues, ultimately improving performance.

Traditional observability follows a "centralized" or "store and explore" model — data is collected and filtered into one main central repository for analysis. The challenge with this approach is that in order to keep costs in line, many organizations put a cap on how much data can be kept, forcing developers to neglect certain datasets which can leave them with significant blind spots. If a problem occurs, developers may not have access to the raw data showing the full context of the issue.

Decentralized observability — applying distributed stream processing and machine learning at the source so all data sets can be viewed and analyzed as they're being created — changes this paradigm. When observability is decentralized, developers are empowered in several ways.

First, they always have full access to all the data they need to verify performance and health as well as make necessary fixes whenever a problem is detected.

Second, the concept of data limits becomes null, enabling all data to be collected and analyzed — including pre-production data, which offers a wealth of actionable insights to help developers avoid production problems in the first place.

Don't Make Them Have to Ask

As noted above, developers often lack access to their own observability data. Further inhibiting the developer experience is the notion that many observability platforms are complex and hard to master. We find that frequently, this expertise lives in the operations side of the house, making developers dependent on DevOps and SRE team members to verify the health and performance of production applications. When observability is highly automated, developers don't have to make the ask and can fix their own problems — which can save time and boost morale. With an industry standard 1:10 SRE-to-developer ratio, forcing developers to over-rely on already stretched thin SREs can certainly create bottlenecks and job frustration.

In this way decentralized observability brings down barriers, reduces friction and infuses the entire end-to-end software lifecycle with greater agility, harmony and collaboration. For example, developers can move quickly without fear of making simple, common errors like leaving debug on, which can lead to storage costs overflowing and getting into trouble. DevOps and SRE professionals also benefit by only having to be brought in to handle the most pressing and complex challenges.

Staying One Step Ahead

Many observability tools are overly manual when it comes to configurations and onboarding new services. Specifically, every time a feature is deployed or updated, developers must build or update alerts and dashboards to ensure the service is working in production. Such an approach becomes problematic as organizations adopt microservices and shift to a continuous delivery model. With systems being spun up so quickly, any lag time in achieving real-time visibility into mission-critical production systems can be a real competitive disadvantage.

In addition, without this up-front work, unknown problems or issues an organization hasn't yet built rules to catch — known as "unknown unknowns" — can go undetected. Production environments are the wild wild west where anything can happen – unpredictable errors, bugs, slowdowns, scale and performance issues, to name a few. This inability to track "unknown unknowns" out of the gate is a type of people and process problem accounting for up to 80 percent of end-to-end site availability glitches.

In a continuous delivery environment, observability tools must feature autodiscover capabilities so newly deployed applications and systems can be included and real-time visibility obtained instantaneously. This means automated onboarding and setting up of queries, alerts and dashboards, as well as applying machine learning to automatically detect anomalies for which rules haven't yet been built — and may catch an organization off guard. In addition, log data is incredibly noisy and unstructured, making it unrealistic to expect developers to sift through humongous data volumes to find what they need to proactively understand service behavior and troubleshoot issues. Automatic surfacing of contextual raw data and insights can be the key to developers spending less time monitoring and troubleshooting, and more time on their core function of innovating.

Conclusion

For many organizations today, software development is a mission-critical process in and of itself, which makes attracting and retaining top developer talent an utmost priority. There are many ways to increase developer job satisfaction, but one key method is to increase developers' sense of command by fostering self-reliance. Observability techniques and tooling offer ample opportunities for this, by enabling a constant eye on all data, increased independence on the job and reduction of mundane, time-consuming processes that leave developers in a reactive position. Traditionally, observability tools haven't been built to prioritize the developer experience, but fortunately this is changing and making developers' lives better.