Leveraging Data To Promote DEI In The Workplace

Quantitative thinking and decision-making tend to be characterized as rational and logical and, by extension, largely divorced from social or cultural concerns. By contrast, qualitative thinking and decision-making are generally viewed as more socially and culturally attentive. While there is some truth to these characterizations, this doesn’t mean leaders committed to promoting diversity, equity and inclusion (DEI) should necessarily over-index qualitative approaches. In fact, adopting a quantitative and data-driven approach can be a powerful way to promote DEI in the workplace and, more importantly, an effective way to get buy-in from team members who may not otherwise be inclined to embrace this work.

Here are four reasons you should lean into data.

1. Promote DEI to different audiences.

Just as different people learn in different ways, different people are persuaded by different types of storytelling. While a compelling first-person testimony may do the best job of convincing one team member that there is an equity problem in the workplace that requires attention, for another team member, a spreadsheet showing pay gaps or vastly different histories of being promoted based on gender or race may be far more compelling. Assume that not everyone on your team will be persuaded by the same types of storytelling or evidence, and be prepared to convey your DEI reality using both qualitative and quantitative approaches.

2. Protect individual team members.

In addition to recognizing that some team members are more likely to be persuaded by quantitative arguments, it is important to recognize that in some cases, it is also important to avoid anecdotal evidence for other reasons. For example, while anecdotal evidence can be a powerful and effective way to talk about DEI issues, in some cases, anecdotes and case studies may compromise confidentially or open minority team members up to potential forms of retaliation. This is especially true in small to mid-size workplaces and workplaces where anonymity isn’t always possible.

3. Promote fairness in decision-making.

While there are exceptions, sometimes the fairest decisions are those that are data-driven. Take, for example, key decisions about where to invest in new hires. Rather than simply listening to pleas from managers who may just happen to be most likely to speak up and ask for additional resources, using quantitative measures is often a much fairer and ultimately more equitable way to make decisions about key investments. Being able to show that your decisions are grounded in data is also generally an effective way to mitigate complaints that you’re simply playing favorites.

4. Measure to manage.

If a company is worried about its sales volume, it will inevitably know where it stands because it is already measuring its sales volume and setting benchmarks to increase it. When it comes to DEI, however, many companies still don’t measure progress or set benchmarks that can be easily measured. As C. Williams and Jamie Dolkas observe in a 2022 article in the Harvard Business Review, “That makes no sense. The fact is, without metrics to measure their current status and monitor progress, DEI efforts will always amount to shooting in the dark. And that can be very costly, as CFOs are starting to realize.” By one estimate, U.S. companies spend roughly $8 billion a year on DEI training but often have little or nothing to show for their investment. Williams and Dolkas contend, “A company that’s committed to solving its problems uses metrics to identify trouble spots, establish baselines, and measure progress.” It follows that DEI should not be an exception to the rule.

Bring a qualitative perspective to bear on quantitative data.

While there are many ways in which a more quantitative, data-driven approach promotes DEI in the workplace, there is no question that qualitative approaches have a critical role to play in promoting DEI discussion. Among other things, qualitative approaches help raise critical questions about data. After all, how data are collected, how datasets are sometimes “scrubbed” and how data is eventually presented and interpreted are also structured by biases, and the best way to uncover these biases is to bring a qualitative perspective to bear on quantitative approaches and claims.