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Software engineers are in a constant fight for efficiency. Whether teams are conducting scrum sprints or implementing a DevOps approach, the end goal is improving productivity and delivering better software, faster.
Just about every industry is using data to achieve that end, but surprisingly — 19 years after Michael Lewis published Moneyball and brought data-driven decision-making to millions of everyday 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 haven’t fully grasped 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 should set an example for other business units, demonstrating how a data-driven approach can make life easier and more efficient.
5 elements of a data-driven approach
Here are five ways doubling down on data can make software engineers more productive:
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Facilitating cross-team collaboration
Teams can only work together efficiently when they’re 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 outcomes of the decisions they make in those discussions.
When conversations are backed by data sourced from the entire tech stack, developers can work from the same foundation instead of trying to communicate using siloed 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 detecting and preventing problems
Productivity isn’t just about working quicker. 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 become overwhelmed by technical debt if they fail to locate bugs and their root causes in real time.
A data-driven approach backed by AI makes it possible to recognize patterns and identify problems before they become issues and well before they reach the end user. With these proactive tools, developers can resolve issues and return to writing innovative code rather than devoting time to putting out fires.
Overcoming opinions and assumptions
When organizations don’t have concrete data to rely on, they’re forced to guess at the cause of 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 isn’t backed by anything more than a hunch. This lack of data can also reinforce some of the unconscious biases that continue to plague the tech industry; applying data can help to level the playing field across tech teams and ensure that all voices are heard and evaluated on their merits.
Using a data-driven approach, developers and engineering leaders can lean 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 are no longer guessing and developers can be confident in the direction of their work.
Accelerating discovery and resolution of issues
It’s not always possible to catch and prevent every issue a development team faces, but there are steps you can take to minimize the issue’s effect. Data-driven operations and observability reduce both mean time to detection (MTTD) and mean time to resolution (MTTR), meaning that developers spend less time solving problems when they do occur. New Relic’s 2022 Observability Forecast found that organizations that have achieved full-stack observability experience the fastest mean time to detection and resolution — less than five minutes. Additionally, 68% of respondents who said they had already prioritized or achieved full-stack observability reported needing less than 30 minutes to detect high-business-impact outages. Among those who hadn’t prioritized full-stack observability, only 44% of respondents could detect high-business-impact outages this quickly
When organizations spend less time identifying and fixing errors, they also benefit from fewer outages. Those respondents who had already prioritized or achieved full-stack observability were less likely to experience frequent outages than those who hadn’t yet achieved full-stack observability.
Driving value and innovation
Above all, developers want to drive value for the business. By incorporating data into their workflows, developers can slash the time spent monitoring and maintaining low-priority processes, freeing them to focus instead on the innovative projects that will generate top- and bottom-line growth. When asked what technologies will need observability over the next three years, respondents frequently cited leading-edge technologies including artificial intelligence (47% of respondents), 5G (33%) and blockchain (32%). Observability is becoming a prerequisite or entry ticket for engineers with aspirations 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 that impact customers. Whether an organization has a mature observability practice or is just getting started, there’s no end state for data-driven operations. Every organization can use data to refine operations and deliver more efficient results.
Peter Pezaris is SVP of strategy and user experience at New Relic.
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