How Inclusive Practice And Data Help Reduce Bias In Decision-Making

It’s easy for biases to creep into different aspects of decision-making—even when you think you’re basing your decisions on objective facts. So how can you limit bias when it comes to making decisions? What exactly is data-informed decision making? And how can you keep bias from infiltrating your data?

There’s a lot to unpack here, so let’s reflect for a moment.

First, we must address the elephant in the proverbial room: Everyone has biases. Bias is not innately bad or something to be ashamed of—it’s a natural human impulse. Oftentimes, people avoid addressing and exploring bias because they think it’s a weakness or flaw. However, it is something leaders should be aware of to make intentional, informed decisions. Being intentional about practicing empathy and decentering yourself from your decisions can lead to more inclusive outcomes.

Data-informed decision-making uses facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. The emphasis here is on “guide.”

Data is not a silver bullet to negate all bias. However, it can create the space to decenter from your own assumptions and begin to see the range of ways that a particular situation can be viewed, understood, or addressed.

Here’s how to limit biases when making decisions for your business.

1. Embrace data-informed decision-making—just make sure your data itself isn’t biased. Data is meant to be the start of the conversation—not the entire conversation. (Learn more about what data-driven decision-making looks like here.)

When we analyze data, we first look at it in aggregate to get to reasonable sample sizes. However, we can get more insight into different variables and how respondents from different backgrounds responded to a survey by disaggregating the data. Slicing and viewing data according to different variables such as age, gender, race, location, year, etc. can reveal other implications and patterns. Once you start to unpack the data and filter it for different considerations, the story it tells will become more nuanced. For example, if you are looking at employee well-being across your organization, you could look specifically at gender identity and see how and if that influences perception. Make sure you stay cognizant of sample sizes and keep your pools of respondents anonymous.

If you only ask superficial questions, or if you’re not thoughtful in regards to how your research is designed, how you’re gathering the data, or what data you’re collecting, your data will not be as good. To get as close to a full picture as possible, look at all of the information you have, disaggregate the data, and don’t make assumptions about what you’re seeing. Before you do this, try to reduce the bias in your underlying data. Ensure that your company’s data analysts and business users know how to watch for bias at different stages of working with data; bias can come from the data collection and communication process itself. Here are some highlights from the Urban Institute’s Do No Harm Guide that explain how to do this:

Data collection stage. Diverse teams can help identify biases and make connections between different fields of study whose relevance may not be evident at first glance. They can also better reflect the demographics of the populations they wish to study. When possible, make the purposes of your data collection efforts explicit so respondents understand why their participation is important.

Analysis stage. Don’t completely separate your analyst and communications teams from the data collection teams—collaboration across the entire data workflow is always better than silos. When analysts and communicators receive the data, they should ask questions like: “How were these data generated? Who is included and who is excluded from these data? Whose voices, lives, and experiences are missing?”

Presentation stage. Don’t shy away from complexity and nuance in your visuals if that more accurately reflects the findings in the data. Consider how adding complexity—in the form of more data-dense graphs and charts—can help demonstrate that you and your teams have thought hard about the implications of your analysis efforts.

2. Recognize and mitigate bias—and understand how it influences your decision-making process. Unconscious bias, or implicit bias, refers to a bias that we are unaware of, and which happens outside of our control. This happens when we make quick judgments and assessments of people and situations, and it can be influenced by our background, cultural environment, and personal experiences.

Bias can prevent us from cultivating diverse talent, developing an engaged workforce, leveraging unique experiences and perspectives, and sparking innovation through collaboration. Bias at work can appear just about anywhere, but most often it appears in recruiting, screening, performance reviews and feedback, coaching and development, and promotions.

3. Incorporate inclusive work process practices. An example of an inclusive work practice is creating clear selection criteria for your decision-making process. This criteria should be aligned with your organization’s mission and strategy. Make sure you understand why you prioritize that criteria. Be consistent in how you evaluate everyone, and be intentional.

Consider the example of finding a keynote speaker for a company event. What message do you want to land at your event? Do you need this story to come from a company of a particular size with a certain level of brand equity? Is that as important or less important than the metrics you want to be able to highlight about their story? And what about sharing your platform with perspectives that come from a diversity of backgrounds?

In this scenario, we tend to say we want “everything!” or focus on certain criteria that are high value from our perspective as an individual or as part of a team. But what about when someone brings that low-hanging fruit of having a great title but lacking the right story to tell? Having clear criteria established ahead of time will ensure that the decision you make is true to the outcome you want.

If the decision will be informed by more people than just you, bring in people outside of your immediate network when selecting contributors to a particular project, program, or decision-making effort. The people in your immediate network—your “go to” people—are more likely to be similar to you than bring a different perspective. This is known as affinity bias.

4. Prioritize diversity (representation) and inclusion at your company. Data can help you see and explore concepts that are not your own. Ensuring diversity and inclusion—both in terms of the individuals providing the data as well as the individuals on your team interpreting the data—will result in your team having more interpretations and a greater understanding of what the data is saying. Research has demonstrated the positive impact of having more diverse teams with more diverse perspectives. According to a recent study, diverse and inclusive companies may be 60% more likely to outperform their peers in regards to decision-making.

Diverse, inclusive teams can disrupt bias by bringing in new ideas from unique points of view. According to Deloitte, cognitive diversity is estimated to improve team innovation by up to 20%.

When folks from different backgrounds explore data, your team can explore the data from different vantage points, uncover new information, and challenge your own ideas or preconceptions. The more you can do that, the more innovation will take place.

Another way to keep bias in check is by creating an inclusive atmosphere in which employees can feel psychologically safe. This way, they will feel comfortable enough to share their unique perspectives. If this isn’t encouraged, people will not be vulnerable and share their potentially ground-breaking ideas. Fostering an atmosphere of psychological safety and being able to work more productively together leads to innovation.

Other questions to consider: Are you creating inclusive teams? Is your organization thinking beyond the recruitment aspect of hiring individuals from different backgrounds?

5. Be intentional about challenging your assumptions throughout your decision-making process. Leverage a framework or tool such as the Do No Harm Guide to do so. Disaggregate your data and ask yourself inclusive practice questions.

Make sure your company’s data analysts and business users know how to watch for bias across their work processes from strategy to execution. Inclusive practice can create moments for disrupting bias—but if it’s only a reflection activity, you will be too late to course correct. Consider using a framework to create moments to reflect on if you’re incorporating inclusive practice into your workflow.

Start the decision-making process with data

Bias will never be completely eradicated, and data itself is not the answer. Rather, data is the beginning of a process to ask more questions that will eventually lead to an informed answer. By having more diverse, inclusive teams, you will be able to maximize interpretations of your company’s data, leading to more innovative insights and decisions.

Make better decisions with data

Learn more about how to use data to make informed business decisions.