Optimizing Performance Assessments for Developers with Data-driven Technology
Learn how AI revolutionizes data-driven performance reviews for fair assessments and higher engagement.
Kirimgeray Kirimli, president of Flatiron Software Co, discusses how tech companies can ensure unbiased assessments and foster higher engagement.
Industries worldwide understand the value of collecting and analyzing data for better business decisions. Although this practice has existed for centuries, technological advances and data accessibility via the cloud have now made data-driven decision-making the norm.
Surprisingly, some tech companies that utilize data-collecting software to gain insight into their business’s performance have yet to transition those same practices to assess the performance of their employees. Instead, many still conduct outdated, bias-prone reviews, ultimately hindering career growth and contributing to disengagement.
With employment for software developers projected to grow by 25% between 2022 and 2032, tech industry leaders need to assess job performance objectively. Industry leaders that provide career development opportunities will retain their top talent at a time when developers will be in demand— and it all starts with data-driven performance assessments. Today, managers can provide a faster, more transparent review process with data-driven feedback by leveraging tools that provide real-time insights through agreed-upon metrics. Here’s how.
Outdated Reviews Lead to Underrated Outcomes
A common belief is that managers will see and acknowledge contributions during a traditional review, eventually leading to career growth through recognized accomplishments. Relying mainly on employees and managers to assess the employee’s performance through score systems accompanied by making statements in a document to support their claims. Often, this results in emotional and biased evaluations that do not reflect the genuine contributions of the employee. Studies have shown that different types of biases exist in a work environment that can unintentionally shift focus in the wrong direction during this critical career moment.
For example, you can easily recall what you had for breakfast this morning. But can you remember what you had for lunch two weeks ago? Probably not. That’s because, as humans, we focus on what is fresh in our minds. This bias commonly occurs when evaluating work performance, with heightened attention given to recent accomplishments or fresh fumbles. It shifts importance to what’s easily remembered in the last few months rather than evaluating performance for the entire year.
Another is our habit of favoring people who mirror us. Studies show managers tend to rate employees with similar skills or backgrounds at a higher scale. This biased tendency can hinder an organization’s ability to grow a diverse workforce, providing opportunities not based on performance but rather on physical, academic, or cultural similarities one might have with their managers.
Similarly, if a performance review relies heavily on managers’ subjective assessments, it may overlook the contributions of engineers with different working or communication styles. Perhaps the manager is a team player, but the engineer performs best independently. It’s critical to account for these factors to provide a safe working space, fair recognition, and career progression.
Accomplishments can take different forms and might not always be as visible as we’d like. This is why transparency is essential in the review process; data-driven tools are built to provide that.
See More: How Can AI Developers Avoid Ethical Landmines?
Data-driven Leadership: Empowering Development through Insights
Performance review tools are built for software teams that use emerging technology to pull data that paints a clear picture of an engineer’s performance so managers and employees don’t have to. Solution-based software that integrates with existing tools in an engineer’s toolbox and provides real-time insights through agreed-upon metrics.
Consistency is key, and traditional reviews are often conducted annually or semi-annually, failing to provide timely feedback. Access to real-time insight as projects are underway allows managers to provide assessments regularly. This is effective for organizations that value weekly or monthly one-on-one meetings to review and discuss an employee’s progress. Whether it’s a teammate who needs assistance or an element of the project that requires more resources, identifying areas of improvement sooner gives teams the capability to react and create a plan to address them faster.
The ability to view progress visually removes ambiguity and subjectivity by ensuring that developers and their managers know exactly where they are on their progression. This creates higher-performing teams through accountability. It allows leaders to spend less time trying to identify where a developer may need improvement and instead focus efforts on how they can help an individual grow a limited skill. The tools do this through visual representation and metrics.
Measuring metrics, often intertwined with a company’s key performance indicators, is a common practice in the classic review process. Every company will align performance metrics with objectives; however, analyzing those metrics independently can be more difficult and time-consuming. More so in a coding environment, where performance is based on activity, collaboration, and quality of work. Software performance tools streamline monitoring the quality of code commits, pull request activity, and issue resolution time to gauge the development progress and code quality. By doing so, managers can identify bottlenecks, track milestones, and ensure tasks get completed efficiently.
Because most of these review programs easily integrate with Slack, JIRA, and Github, real-time insight can be leveraged to track progress from existing tools and effectively review developers’ work. For instance, managers can pull data to assess productivity levels among developers working on group projects, which is ideal in a hybrid working environment.
How AI Is Revolutionizing Data-driven Review Tools
As of today, generative AI guides the next generation of data-driven performance reviews. Its ability to access GitHub and JIRA to analyze raw data and go deeper into every line of code written by each team member has been a game-changer. The emerging technology adds precision when reviewing workload, including gauging ticket complexity and evaluating team member efficiency.
AI can also aid managers in creating an employee development plan. Some AI tools can pinpoint areas of improvement and suggest how to strengthen specific skills related to the employee’s job and career growth direction by analyzing patterns and trends in the employee’s work that would have otherwise been easily missed through a standard evaluation. AI can present that data in a way that allows managers to confirm where someone excels and where they require additional attention.
But it’s important to remember to use AI as a tool to guide the review process, not dictate it. A healthy work culture relies heavily on solid work relationships built through effective communication and empathic leadership. A human-centric element that can only be fostered in a working environment by its managers, not its technology. Eliminating the time needed in a typical review process in which managers are expected to collect evidence of performance highs and lows gives leaders more time to focus on other critical tasks, like building development plans for employees who show interest in growing in the company.
These tools create a more level playing field, eliminating ambiguity in forced rankings and subjective feedback. There is value in investing in a process that creates more opportunities for higher employee engagement, transparency through agreed-upon metrics, and the ability to implement career development strategies. While no evaluation process is perfect, emerging tools allow teams to bring performance reviews into the future to evolve how we communicate and grow.
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