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Software engineers are in a constant struggle for efficiency. Whether teams are conducting Scrum sprints or implementing a DevOps approach, the end goal is to improve productivity and deliver better software, faster.
Almost every industry uses data to achieve this goal, but surprisingly – 19 years after Michael Lewis published silver 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 survey respondents have a mature observability practice, demonstrating that these organizations have not fully understood how to use data to improve their performance.
While taking 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, demonstrating how a data-driven approach can make life simpler and more efficient.
5 elements of a data-driven approach
Here are five ways data doubling can make software engineers more productive:
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Facilitate inter-team collaboration
Teams can only work together effectively when they are able to build on common ground. By investing in data, engineering 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 draw on data from across the technology stack, developers can work from the same base instead of trying to communicate using siled information. This common ground makes it easier for teams to work together on collaborative projects throughout the product lifecycle, an especially 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 bugs. Errors are inevitable, but teams will soon be overwhelmed with technical debt if they fail to locate bugs and their root causes in real time.
A data-driven approach backed by AI helps recognize patterns and identify issues before they become problems and long before they reach the end user. With these proactive tools, developers can fix problems and get back to writing innovative code rather than spending time putting out fires.
Overcome opinions and assumptions
When organizations don’t have hard data to rely on, they’re left to guess what’s causing a problem or the best way forward. In these cases, teams often default to the loudest voice in the room (or the highest ranked employee) even if that person’s opinion is only supported by intuition. This lack of data may also reinforce some of the unconscious biases that continue to plague the tech industry; Data application can help level the playing field between technical teams and ensure that all voices are heard and evaluated on their merits.
By using a data-driven approach, developers and engineering managers can rely on historical data and key metrics to inform their decisions. Analytical insights have shattered long-held assumptions everywhere, from baseball dugouts to boardrooms. With the numbers at their fingertips, decision makers take the guesswork out of it and developers can be confident in the direction of their work.
Accelerate problem discovery and resolution
It’s not always possible to detect and prevent every problem a development team faces, but there are steps you can take to minimize the effect of the problem. Data-driven operations and observability reduce both mean time to detection (MTTD) and mean time to solve (MTTR), meaning developers spend less time resolving issues when they arise . New Relic’s 2022 Observability Forecast found that organizations that have achieved full observability are experiencing the fastest average time to detection and resolution – less than five minutes. Additionally, 68% of respondents who said they had already prioritized or achieved full stack observability said they needed less than 30 minutes to detect high-impact outages. Among those who had not prioritized full-stack observability, only 44% of respondents could detect high-impact failures as quickly
When organizations spend less time identifying and correcting errors, they also benefit from fewer outages. Respondents who had already prioritized or reached full stack observability were less likely to experience frequent outages than those who had not yet reached full stack observability.
Generate value and innovation
Above all, developers want to create value for the company. By incorporating data into their workflows, developers can reduce the time spent monitoring and maintaining low-priority processes, freeing them to focus on innovative projects that will drive revenue and bottom line growth. . When asked which technologies will need observability over the next three years, respondents frequently cited advanced technologies, including artificial intelligence (47% of respondents), 5G (33%) and blockchain (32%). Observability is becoming a prerequisite or an entry ticket for engineers who aspire to build the innovative solutions of the future.
No excuse not to use the data
Organizations have more data at their fingertips than ever – and no excuse not to use it. The benefits of a data-driven approach are clear, whether it’s a foundation for successful collaboration or faster resolution of issues that affect customers. Whether an organization has a mature observability practice or is just getting started, there is no end state for data-driven operations. Every organization can use data to refine its operations and deliver more efficient results.
Peter Pezaris is SVP of Strategy and User Experience at New Relic.
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