Using Data for Unbiased Decision Making

Feedback is crucial for fostering a thriving development and performance culture, but how can team leaders ensure that reviews are fair and objective?

Traditionally performance reviews happen once or twice a year, where managers give a formal review based on the employee’s performance over that period, and pre-establish some goals.  Although somewhat useful, these methodologies mostly rely on subjective signals such as perception and memory of all of those involved, often failing to provide a holistic view of each member’s performance.

In 2016, Harvard Business Review published an article on the future of performance reviews. In this article, they argue that  the biggest limitation of annual reviews was the companies’ “heavy emphasis on financial rewards and punishments and their end-of-year structure,  they hold people accountable for past behaviour at the expense of improving current performance and grooming talent for the future, both of which are critical for organizations’ long-term survival.” 

These methods focus only on quantitative data, such as financial results and tangible outcomes, and overlook any qualitative contributions. For example, in software development, traditional approaches might measure performance by counting the number of tickets closed in a given period, with the top performer being the one who closes the most tickets. But this doesn't take into account the complexity of the tickets. Is it then really fair to judge a senior engineer who handles fewer but much more complex tickets the same way?

The article further emphasizes the inherent flaws in traditional performance reviews, highlighting that they are often biased and ineffective.  On the other hand, they showcase four main challenges that persist: aligning individual and company goals, rewarding performance, identifying poor performers and managing the feedback.

Most interestingly, 8 years after the publishing of this article, the problems highlighted surrounding performance measurement are still relevant today.  So, how can we actually solve those issues?

The answer lies in leveraging advanced AI and real-time data from multiple sources. In today’s digital world, every action taken by a company and its teams is tracked on platforms like Jira and GitHub. These platforms already provide a solid quantitative foundation for managers to assess software performance, measuring significant metrics such as coding frequency and pull requests. However, although these metrics are useful, they do not reflect the whole development process, echoing the Harvard article in terms of inherent shortcomings in traditional performance assessments.  

To effectively overcome the inherent limitations of traditional methods, it's imperative to incorporate qualitative data into performance assessments. This approach enables managers to gain richer insights and context, allowing them to better interpret the quantitative data and make more informed decisions. By integrating both qualitative and quantitative data, managers can achieve a more comprehensive and accurate evaluation of their team's performance, limiting bias and subjectivity.

That’s when AI plays a pivotal role in performance assessments. Quantitative metrics such as code frequency and ticket resolution rates offer objective measures of productivity, providing a foundational understanding of output. On the other hand, qualitative insights from peer feedback and assessments of code complexity provide deeper context into the quality and impact of work. By synthesizing these diverse data types, AI can analyze patterns, offer contextual insights, and evaluate not just the quantity but also the complexity and quality of work. 

With that team leaders can easily understand how team members are spending their time, the issues they are struggling with, and their strengths, all in real-time. Allowing to build an ongoing system based on a 360º review, delivering tailored feedback that combines performance data with company and individual goals at the time the action happened, or at each end of the cycle, gaining not only agility but also helping the development of its team members. 

How to manage all that data and feedback?

That’s when Snapshot Reviews springs into action. Our platform seamlessly integrates with engineering platforms such as Jira and Github, to continuously collect real-time data and offer essential features such as 360º Feedback and Sprint Retrospectives. By synthesizing both qualitative insights from peer feedback, reviews and quantitative metrics like code frequency and ticket resolution rates, Snapshot Reviews provides a comprehensive view of the teamwork. 

Going one step further, Snapshot Reviews includes an advanced feature that trains an LLM based on the integrated data. Unlike traditional methods that rely on subjective assessments, our AI ensures objective and data-driven performance evaluations that take various types of data into account. It analyzes patterns, identifies trends, and provides actionable insights empowering managers to make informed decisions.

By leveraging this holistic approach, Snapshot Reviews eliminates ambiguity and subjectivity in performance evaluations. Enabling managers, engineers, and their peers to gain clear visibility into project progress and individual contributions. Enhancing not only accountability but fostering a culture of continuous improvement. 

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