Engineering inefficiency causes global losses of $300 billion every single year. In the United States alone, organizations lose an average of $29,332 per developer annually because they can’t get the most out of their software engineers’ talent. But data could save the day.
There’s a solution that can make developers more efficient, managers more accurate, and engineering processes less flawed without becoming an extra drain on resources. Git Analytics tools are poised to take the lead here. They can automatically track the performance of individuals by aggregating historical Git data and then feed that information back to teams in minute detail. Everything to do with your engineers’ work process, work quality and final output is tracked, digested and presented in simple visuals.
Git is used to track code and changes made to that code as it is developed. What Git Analytics tools do is create metrics to analyze your engineers’ output, how they’re working and the business value they generate.
Armed with this data, businesses can more easily and accurately identify areas where inefficiency is rife, allowing them to take necessary action. It also empowers managers to measure how changes in work processes influence team productivity and to give evidence-supported feedback to team members.
Today, these benefits are especially valuable. As businesses adapt to employees working from home on a regular basis, they’ll need tools to boost remote visibility and ensure inefficiencies don’t go unnoticed. That’s why the Git Analytics tools market will thrive over the coming year, and these are the main features driving its future success.
Maximizing Developers’ Productivity
Developers function best without interference, with the freedom to work according to their personal needs. Nonetheless, managers need to have an idea of what’s going on and how people are progressing.
Git Analytics provides managers with automatic data that creates a picture of how teams are working and tells them if targets are going to be met. Not only does this provide peace of mind for managers, but it also offers an in-depth look at team performance without disrupting the team in any way. Members don’t have to send updates about what they’re doing and therefore don’t risk being distracted or burdened with reporting duties; they can maintain their focus on the task at hand.
For example, Snoot delivers metrics around the composition and evolution of code for a project, with each developer having their own activity stats showing when they began contributing, how much they’ve contributed and trends in their contribution behaviors (e.g. projects they engage with more). All this data is readily available as interactive content on the platform, and it’s generated without developers taking action.
In comparison, a tool like Trello needs developers to create a card, add information and appropriate attachments and tag other members. This manual form of reporting is more time-consuming, less accurate and gives managers a mere surface-level understanding of what’s happening in their teams.
More Objective Insights, Less Biased Work Reviews
Without data, managers are far less capable of being objective when it comes to developer reviews. Git Analytics data is highly personalized, offering insights about how team members have worked in the past and present. Rather than comparing team members to each other — which will always be skewed because everyone has different work styles — or making subconscious judgments based on someone’s likability, managers can assess individuals against their own previous performance. This means managers can review the evolution of an employee’s work free from subjective factors that they may not realize are at play.
One helpful tool here is GitHub Insights, which displays data around an employee’s pull requests, reviews and commits in real time as simple and unbiased figures. With such data, managers can give more tailored and actionable feedback to developers, without sizing them up against coworkers. This can help lower unhealthy competition in the workplace. And managers who are objective leaders should forge stronger relationships with team members.
Reduced Work Stress for Developers and Managers
A recent survey revealed that 69 percent of employees are experiencing burnout symptoms while working from home during the pandemic. Mental well-being has to be a priority for managers if they want healthy and motivated teams in the long term. That includes seeking out solutions that will flag when developers are becoming overwhelmed.
Of course, managers are equally at risk from burning out, and they need to organize their duties in a viable way. Git Analytics tools help both parties — first by lessening everyone’s workload. Managers don’t have to gather and evaluate data on each team member themselves, and developers don’t have to set aside time to report upwards.
Second, the automated nature of Git Analytics makes developers feel less surveilled, because it is not as intrusive and requires little input from them. Git Analytics just focuses on code, unlike ethically dubious surveillance software that monitors employees’ browser activity and takes screenshots of their computers.
Furthermore, developers can boost their morale with Git Analytics tools’ visual displays, which clearly show people’s progress and impact. Research actually proves that visualizing goals and performance can increase the effort and commitment that people apply to their project.
Developers Can Self-Regulate and Drive Their Careers
Tech businesses will be striving for a greater hands-off management system, in which employees are encouraged to be their greatest source of motivation and take pride in their work. That means trusting developers to organize their working hours and set their own goals. Employees will also work better if they follow self-observing routines: not being watched over by a superior, but watching over themselves.
Git Analytics supplies an interface on which developers can easily monitor their own output, identify work habits they weren’t aware of, and fuel their motivation by realizing that they produce more (or less) than they thought.
Gitential is useful in highlighting areas for improvement and risks in code quality based on developer style and industry best practices. It can also identify knowledge gaps where developers need to grow, such as if the data shows they are producing less code or have a higher error rate.
Having this transparency and honest understanding of themselves — rather than only ever being told how to improve from above — will see developers strive to improve on their weaknesses but also to diversify their skill set and be more conscious of the steps they can take to further their careers.
Real Productivity Reporting, Not Just Time Stamps
Productivity is about much more than simply measuring when a developer clocks in and out. A big problem with current tracking tools is that they only provide superficial data in the form of time tracking and screenshots. Yet the reality is that, just because an employee has spent hours on a project, it doesn’t mean they have been productive.
What Git Analytics tools bring to the table is granular data that is more representative of how people are working, not just when they are working. Everyone is different, so they need to be assessed using different metrics. For example, one developer’s strength may be supporting other people’s pull requests, which can be tracked by the number of collaborations they participate in. If this number drops, it may be far more revealing about a drop in their productivity than just tracking their individual output.
Because the data from Git Analytics tools is so heavily personalized, you’ll get a truly informative view of a person’s production and work quality, as opposed to the broad strokes.
It's surprising that, in the tech space — where data is so central to all decision-making processes — analytics have yet to be fully integrated into team management. The move toward Git Analytics is already underway. By next year, we can expect to see this type of reporting as standard practice across teams.