5 ways a data-driven approach can help engineers

Software engineers are constantly competing for efficiency. Whether teams are running scrum sprints or implementing a DevOps approach, the end goal is to improve productivity and deliver better software faster.

Virtually every industry uses data to achieve that goal, but surprisingly – 19 years after Michael Lewis published money ball and brought data-driven decision making to millions of daily readers – many developer teams still fail to fully integrate data into their operations. According to New Relic and ETR’s 2022 Observability Forecast, only 5% of respondents have mature observational practices, showing that these organizations have not fully understood how to use data to improve their performance.

While a data-driven approach is a healthy exercise for any organization, software engineers in particular can benefit from relying more on data and less on guesswork. The teams responsible for building the tools of the future must set an example for other business units and show how a data-driven approach can make life easier and more efficient.

5 elements of a data-driven approach

Here are five ways doubling data can make software engineers more productive:

Enabling collaboration between teams

Teams can only work together efficiently if they can build on common ground. By investing in data, technical leaders can provide employees with a unified language to discuss challenges and opportunities — and measure the results of the decisions they make in those discussions.

When conversations are backed by data drawn from across the tech stack, developers can work from the same foundation instead of trying to communicate using silo information. This common ground makes it easier for teams to collaborate on collaborative projects throughout the product lifecycle, a particularly valuable approach in hybrid and remote work environments.

Proactively detect and prevent problems

Productivity isn’t just about working faster. A recent Rollbar study found that 38% of developers spend up to a quarter of their time fixing software errors. Errors are inevitable, but teams will quickly become overwhelmed by technical debt if they fail to pinpoint bugs and their root causes in real time.

A data-driven approach supported by AI makes it possible to spot patterns and identify problems before they become problems and well before they reach the end user. With these proactive tools, developers can solve problems and write innovative code again instead of spending time putting out fires.

Overcoming opinions and assumptions

When organizations lack concrete data to rely on, they are forced to guess at the root of a problem or the best way forward. In these cases, teams often use the loudest voice in the room (or the highest-ranking employee), even if that person’s opinion isn’t supported by anything more than a hunch. This lack of data may also reinforce some of the unconscious biases that continue to plague the tech industry; applying data can help level the playing field between technical teams and ensure that all voices are heard and judged on their merits.

Using a data-driven approach, developers and technical leaders can rely on historical data and key metrics to inform their decisions. Analytical insights have shattered longstanding assumptions everywhere from baseball dugouts to boardrooms. With the numbers at hand, decision makers no longer have to guess and developers can have confidence in the direction of their work.

Discover and solve problems faster

It’s not always possible to catch and prevent every problem a development team faces, but there are steps you can take to minimize the impact of the problem. Data-driven operations and observability reduce both the mean time to detection (MTTD) and mean time to resolution (MTTR), meaning developers spend less time resolving issues as they arise. New relic Observability forecast for 2022 found that organizations that have achieved full-stack observability experience the fastest average time to detection and resolution — less than five minutes. In addition, 68% of respondents who said they had already prioritized or achieved full-stack observability reported that it took less than 30 minutes to detect outages with a high impact on the business. Of those who did not prioritize full-stack observability, only 44% of respondents were able to detect high-impact outages so quickly

When organizations spend less time identifying and resolving errors, they also benefit from less downtime. The respondents who had already prioritized or achieved full-stack observability were less likely to experience frequent dropouts than those who had not yet achieved full-stack observability.

Driving value and innovation

Developers mainly want to create value for the company. By incorporating data into their workflows, developers can reduce the time they spend monitoring and maintaining low-priority processes, allowing them to focus on the innovative projects that will drive top- and bottom-line growth. When asked which technologies to observe in the next three years, respondents often cited advanced technologies, including artificial intelligence (47% of respondents), 5G (33%) and blockchain (32%). Observability is becoming a prerequisite or ticket for engineers with ambitions to build the innovative solutions of the future.

No excuse not to use data

Organizations have more data at their disposal than ever before – and no excuse not to use it. The benefits of a data-driven approach are clear, from a foundation for fruitful collaboration to faster resolution of issues affecting customers. Whether an organization has a mature observational practice or is just starting out, there is no end state for data-driven operations. Any organization can use data to refine operations and deliver more efficient results.

Peter Pezaris is SVP of Strategy and User Experience at New Relic.