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Team Building2024-05-14

How to Identify and Reduce Bias Within Engineering Teams?

Engineering teams often face the challenge of unconscious bias. In this blog, learn how a data-driven strategy can help predict, prevent, and reduce bias and ensure the best ideas prevail.
How to reduce bias in engineering teams?

Ever feel like your engineering team hits a wall of silence when brainstorming? Or maybe you've noticed some amazing ideas getting brushed aside for reasons that don't quite add up? Well, there might be a sneaky culprit at play – bias

Bias, whether conscious or unconscious, can act like a barrier, blocking new ideas, processes or technologies before they even get an opportunity to prove their worth.

The good news? There are ways to reduce bias and set your engineering team up for success. In this blog post, we'll explore the different types of unconscious biases that can impact engineering teams, but most importantly, we’ll discuss how to overcome and reduce them. 

But first, let's start with a basic question: what does bias look like in an engineering team?

How Does Bias Look Like Within An Engineering Team? 

Bias exists in every decision-making. It comes from our past experiences, and previous situations while dealing with various teams and diverse team members. In certain cases, bias towards particular subjects and situations is a part of our intrinsic thought process. , And hence engineering teams are no exception. 

While seemingly harmless, these unconscious biases can have a significant negative impact on your engineering team's effectiveness and innovation. While companies are actively addressing identity-based biases with DE&I programs, another layer of bias can significantly hinder innovation: experience-based bias, which often goes unnoticed, and most of the time, they’re barely even addressed.

Experience is a double-edged sword within any engineering team. Past experiences and intuition equip teams with valuable knowledge, but they can also create a "comfort trap” and can stifle innovation in several ways:

1. Overconfidence in Past Successes

Sometimes, engineering teams get too comfortable with what's worked before. Take, for example, a team that's always used a monolithic architecture and seen great results. When a new project comes up—perhaps one that's complex and could scale quickly—they might still shy away from considering a microservices architecture, even though it could be more suitable. This hesitation is often due to a preference for sticking with what’s familiar and proven, rather than exploring potentially better options designed to tackle the new challenges.

2. Fear of the Unknown

New ideas and methodologies can be perceived as risky, especially compared to the comfort of proven approaches. This fear of the unknown can lead to dismissing potentially groundbreaking solutions that can deliver more favourable outcomes, simply because they haven't been used before. 

3. Sunk Cost Fallacy

The sunk cost fallacy is a cognitive bias where we cling to past decisions – even if they're no longer the best option. Within engineering teams, the time and resources invested in learning a specific technology can create a bias towards using it, even if a better option exists. Imagine this: your engineering team has spent months developing a new feature based on a specific technology. Now, a newer, more efficient technology emerges. However, some team members are hesitant to give up all the work done and adapt to the newer, more efficient technology.

This is a classic example of the sunk cost fallacy in action.

These experience-based biases can also creep their way into resource allocation decisions.

Resources like budget and development time might be directed towards solutions that align with pre-existing biases, even if they're not the most effective approach for the current challenge. 

These lead you to pay the hefty cost of bias. We explore this in the next section of the blog.

What is The Hidden Cost of Bias? 

Think about it: engineering teams are already under huge pressure to deliver and keep up with new features and updates. Now picture a team so set in their ways that they won't even entertain trying something different, even if it could yield better results. That kind of inflexibility is a disaster waiting to happen. 

Bias in engineering teams is like a sneaky thief and extends far beyond simply overlooking a fresh idea or neglecting a promising new technology.

And it doesn't stop there. Bias infiltrates team dynamics, creating echo chambers where everyone reinforces each other's existing beliefs. This stifles critical thinking and the exploration of new perspectives. Having set processes is good, but imagine a team so fixated on its rigid processes and execution that it completely overlooks the potential of a feature or product, thereby hindering its success.

And the damage persists. Bias can also influence funding decisions, directing resources toward familiar areas even when more impactful projects lie outside the comfort zone. This results in a stagnant research landscape with limited exploration of new possibilities.

But despite all these challenges, there's hope. Biases are indeed part of being human, but unlike some challenges, they're not insurmountable. We've always found ways to overcome obstacles, and fostering a more equitable and innovative engineering environment is no exception. The good news is that there are concrete steps you can take within your engineering team right now to mitigate bias and unlock its full potential. 

One of the most effective ways to do so is through data. How? We explore this below. 

How to Reduce Bias Within Your Engineering Team with Data? 

Bias within engineering teams is a subtle yet pervasive issue that can skew decision-making and impact team dynamics and performance. However, we're not completely defenseless against these biases. By adopting a data-driven approach, engineering leaders can promote fairness and objectivity in their processes.

The tricky thing about biases is that they are often hiding beneath the surface and influencing decisions unconsciously. In such a scenario, data can come into the picture and help you identify biases that you didn’t know even existed.

Data serves as a critical tool to unveil these hidden biases, providing a clear, unbiased lens through which we can assess and adjust our actions.

Data can come in handy and give any engineering leader the clarity they need to fix situations, reduce bias and approach solutions objectively. Here’s how:

1. Identifying and Rewarding Shadow Work

Sometimes, engineering leaders or managers are unaware of the hard work their developers are putting up behind the scenes, especially if they’re not in the habit of regularly tracking engineering metrics. In such a scenario, their unconscious biases might cause them to underestimate the importance of team members who handle crucial but less visible tasks. 

For instance, simply having the data on who consistently offers valuable code reviews can reveal those quietly making a big impact on code quality, even if they're not the loudest voices in the room.

Hatica's review collaboration dashboard
Developer PRs Review and Merged

Hatica's Review Collaboration tool helps track your team's code review processes. This dashboard displays Pull Request collaboration metrics, spotlighting areas of inefficiency and exceptional performance. These insights not only demonstrate a strong understanding of coding best practices but also show their practical application in projects.

These reviews often show a solid grasp of coding best practices and their effect on the project.

By tapping into data like this, you get a full picture of each developer’s efforts, helping to dispel any assumptions that might not match up with reality. This clear view empowers you to tackle challenges with precision, without being clouded by biases or missing out on important contributions.

2. Tracking Complex Task Completion 

Identifying top talent within an engineering team can be challenging. However, valuable insights can be addressed by analyzing which team members consistently take on complex tasks. These tasks often require strong problem-solving skills and the ability to handle intricate technical challenges.
By examining data on who consistently manages complex bug fixes, integrates various systems, or plays a key role in architectural decisions, valuable patterns can emerge.

Activity Log Dashboard

Hatica’s Activity Log Dashboard provides a comprehensive view of the team’s daily activities. It integrates data from multiple sources like Jira and Git, allowing engineering managers to see everything from PRs to commits and tasks at a glance. This 360-degree visibility is crucial for spotting potential work blockers and assessing burnout risks, enabling managers to drill down into specific tasks, commits, or PRs to understand the context and make well-informed decisions.

Tracking these data points over time helps engineering leaders develop an objective perspective on each member's contributions. This approach ensures that recognition and rewards are aligned with demonstrated technical skills and overall impact, rather than subjective factors like visibility or communication style.

3. Optimizing Resource Allocation 

Assigning the right people to the right projects is crucial for any engineering team's success. Data can be a powerful tool in this regard, helping to optimize resource allocation and ensure everyone is working on their most impactful tasks.

Resource Allocation Dashboard

By analyzing historical data, engineering leaders can gain valuable insights into project complexity and the time required for different types of tasks. This allows for more accurate project scoping.

Imagine being able to predict, with data backing it up, exactly how many developers are needed to deliver a specific feature within a desired timeframe. This data-driven approach prevents over-ambitious projects from draining resources and ensures that talent isn't misallocated to less impactful initiatives.

4. De-Emotionalizing The Process of Idea Selection

Choosing which ideas to pursue can be a subjective process. Sometimes, passionate advocacy can sway decisions, even if the proposed solution isn't the most effective. Data analysis can help engineering leaders navigate these situations by introducing an objective element to idea selection.

Here's how data empowers a more impactful approach:

  • A/B Testing: This allows you to compare different versions of an idea and see which resonates better with the target audience. This data-driven approach ensures the chosen solution directly addresses user needs.
  • User Data Analysis: By analyzing user data (engagement, feature adoption, conversion rates), you gain clear evidence of an idea's effectiveness. This allows logic to take center stage, reducing the influence of personal preferences or "pet projects."

Essentially, data becomes the arbiter, guiding decisions toward the solutions with the greatest potential to achieve your goals.

In essence, data is our best ally to fight the war against bias within engineering teams. It provides stakeholders the clarity and insights needed to navigate complex choices with confidence, ultimately leading to better outcomes for both engineering teams and the project.

Closing Thoughts

Building a truly fair and innovative engineering team is a continuous journey. But the key to success lies in two crucial steps: acknowledging our own biases and leveraging data for rational decision-making (to eliminate biases). Think about it: engineering relies on facts and figures. So, shouldn't your choices be grounded in real data, rather than relying solely on gut feelings or past experiences? 

Leading with clear, data-driven insights empowers us to make informed decisions, steering us away from vague assumptions that lack grounding in reality.

Bias can be a hidden barrier to individual and team growth. So it becomes an engineering leader's responsibility to tackle this challenge head-on and reduce bias within their teams, if they wish to build high-performing engineering teams.

By actively building awareness of bias, we empower our teams to break through limitations and achieve more than ever before.

Let’s keep talking and come up with strategies to build teams that are not only skilled but truly inclusive and fair.

If you're interested in continuing this conversation and finding ways to create a more inclusive and effective workplace, our team of productivity experts is here to help. We can assist you in identifying, understanding, and reducing bias within your engineering teams.

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Table of Contents
  • How Does Bias Look Like Within An Engineering Team? 
  • 1. Overconfidence in Past Successes
  • 2. Fear of the Unknown
  • 3. Sunk Cost Fallacy
  • What is The Hidden Cost of Bias? 
  • How to Reduce Bias Within Your Engineering Team with Data? 
  • 1. Identifying and Rewarding Shadow Work
  • 2. Tracking Complex Task Completion 
  • 3. Optimizing Resource Allocation 
  • 4. De-Emotionalizing The Process of Idea Selection
  • Closing Thoughts

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