Efficiency is an ongoing battle for software programmers. The end goal is increasing productivity and delivering better software faster, whether teams use DevOps methods or scrum sprints.
A surprising number of development teams still struggle to completely integrate data into their operations 19 years after Michael Lewis’ publication of Moneyball, which introduced millions of daily readers to data-driven decision-making. Only 5% of survey respondents have a mature observability practice, showing that these firms haven’t fully mastered how to use data to improve their performance.
Even if adopting a data-driven strategy is beneficial for any firm, software developers in particular can gain by depending less on intuition and more on facts. Future tool development teams should serve as role models for other business units by showing them how a data-driven strategy may improve productivity and ease of use.
Five components of a data-driven strategy
Here are five strategies for increasing software developers’ productivity when it comes to data:
Promoting inter-team cooperation
Only when teams are able to establish common ground will they be able to collaborate effectively. Engineering leaders may give staff members a common vocabulary to discuss opportunities and challenges while also measuring the results of the choices they make during such discussions by investing in data.
Instead of attempting to communicate using isolated information, developers can work from the same foundation when interactions are supported by data coming from the complete tech stack. This shared understanding makes it simpler for teams to collaborate on projects throughout the product lifecycle, which is a useful strategy in hybrid and remote work contexts.
Proactively identifying and avoiding issuesÂ
Working more quickly is just one aspect of productivity. According to a recent Rollbar study, 38% of developers devote up to 25% of their time on fixing software issues. Errors are unavoidable, but if teams don’t quickly identify defects and their core causes, technical debt will eventually overwhelm them.
It is feasible to identify patterns and spot concerns well before they affect the end user thanks to an AI-powered, data-driven strategy. Instead of spending time putting out fires, developers may fix problems and resume producing creative code with the help of these proactive tools.
Overcoming preconceptions and biases
Organizations are compelled to make educated guesses about a problem’s cause or the best course of action when they lack solid data to base their decisions on. Teams frequently choose the loudest voice in the room (or the highest-ranking employee) in these situations, even if that person’s opinion is only supported by a hunch. Applying data can help level the playing field among IT teams, ensure that all views are heard, and ensure that decisions are made based on each individual’s merits. This lack of data can also reinforce some of the unconscious prejudices that continue to plague the tech sector.
Developers and technical leaders can use past data and important metrics to guide their decisions by taking a data-driven approach. Long-held beliefs have been disproved everywhere from boardrooms to baseball dugouts by analytical findings. Decision-makers can no longer rely on speculation because they have the numbers at hand, and developers may feel secure about the course of their work.
Accelerating problem discovery and resolution
Even while it’s not always possible to identify and solve every problem a development team encounters, there are steps you may do to lessen the impact of the problem. Developers spend less time resolving issues as they arise because data-driven operations and observability shorten the mean time to detection (MTTD) and mean time to resolution (MTTR).
According to New Relic’s 2022 Observability Forecast, businesses that have fully implemented observability have the quickest mean time to detection and resolution (less than five minutes). Furthermore, 68% of respondents who claimed to have prioritised or attained full-stack observability indicated it took them less than 30 minutes to find disruptions with a significant negative impact on their organisation. Only 44% of respondents who hadn’t prioritised full-stack observability were able to identify disruptions with significant business effect thus fast.
Organizations benefit from fewer outages when they spend less time finding and addressing issues. In comparison to responders who hadn’t yet attained full-stack observability, those who had already prioritised or had done so reported fewer frequent outages.
Creating value and driving innovation
Above all, developers aim to add value to the company. Developers can save time monitoring and managing low-priority procedures by incorporating data into their workflows, allowing them to focus on new projects that will drive top- and bottom-line growth. When asked which technologies will require observability in the next three years, respondents typically mentioned cutting-edge technologies such as artificial intelligence (47%), 5G (33%), and blockchain (32%). Observability is becoming a requirement or entry ticket for developers who want to create new solutions for the future.
No reason not to use data
Organizations now have access to more data than ever before, and there is no justification for not using it. The advantages of a data-driven strategy are obvious, from laying the groundwork for constructive collaboration to hastening the resolution of customer-impacting problems. There is no final state for data-driven operations, regardless of whether a company has an established observability approach or is just being started. Every firm can make use of data to streamline processes and produce better outcomes.
