New data from the United Kingdom revealed that nurses of Black African heritage are referred to the Nursing and Midwifery Council (NMC) for disciplinary action at a rate nearly twice the size of their overall representation within the profession. NMC, the United Kingdom's regulator and licensing board for nurses and midwives, reported that nurses of Black African heritage, who comprise 7% of all licensed nurses, made up 13.3% of disciplinary referrals across 2019-2020.

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However, the real significance of this data lies in what it says about how NHS Trusts (local health care systems) treat employees of color—particularly in comparison with their white colleagues. As Roger Kline, a workforce and diversity expert for the NHS, put it, "I'm not against nurses, where it's appropriate, being referred to the regulator. But when there is a big difference, that either says something about what the trust's disciplinary process is or it says something about the trust's culture, recruitment, training and support, because there's nothing innate about the different abilities of Black nurses and white nurses."

This predicament speaks to the prevalence and power of implicit bias: attitudes toward or stereotypes about groups of people that are made without consciously thinking about them. And because these biases are implicit, rather than explicit or overt, it's hard for organizations to identify their existence in the first place. That's where disaggregating workforce data can help.

Leverage workforce data to combat implicit bias

Organizations looking to boost diversity, equity, and inclusion (DEI) should reflect on how individuals and processes can be set-up to advantage some groups over others, oftentimes inadvertently. Equity at work is more than just ensuring that your overall workforce demographics reflect the population. To commit to equity in your workplace, you must be willing to look at your own data to uncover inequities in your employee experience. Ask yourself: what does your workforce data tell you as you compare trends between minority and non-minority staff?

If organizations commit to not only collecting workforce data, but probing deeper to surface comparative trends between minority and non-minority staff, they can surface areas where notable disparities are prevalent. Examples of such areas include disaggregated data related to:

• Disciplinary action;
• Employee grievances;
• Employee engagement surveys;
• Staff recognition;
• Merit increases or bonuses;
• Annual performance review ratings;
• Turnover rates;
• Promotion rates; and
• Secondment or stretch opportunities.

By surfacing areas where there are notable disparities in the data, organizations will have a more nuanced understanding of the depth of their DEI challenges. Depending on the data found, organizations can follow-up with a targeted review of policies and procedures that may be contributing to those disparities. For example, if minority staff are noticeably absent from promotions or merit increases, consider conducting a review of how employees are regularly put up for promotion. A further step would be to conduct bias training before performance reviews are launched.

Uncovering racial disparities in your organizational data can be an uncomfortable experience, but it provides tangible evidence and creates a solid foundation for improving equity. After all, if you can't see the problem, you can't fix it either.

For more information about how your organization can address implicit bias, see our cheat sheet.