How diversity data can LOOK good but BE bad

*Please give yourself time this week to process the murder of Daunte Wright, a young Black man killed by a police officer on Sunday, April 11th. I shared my perspective here.

Welcome to Simply Diversity. This bimonthly letter breaks down key diversity concepts and explains how you can apply them... simply. There are plenty of terms heard ‘round the Internet as we prioritize diversity, inclusion and belonging in everything we do. This newsletter explains what these concepts are, why they’re important, and how to practice them. 

I admit: I was not prepared for Equal Pay Day this year.

Equal Pay Day for women in the U.S. was on March 24th — the earliest it’s been since it was first observed in 1996. 

Were you as surprised as I was? We just got through a year of doom-and-gloom headlines about the regression of women’s economic progress thanks to the pandemic. How then is it possible that progress was made on equal pay?  

Break it down:

This isn’t actually about equal pay. It’s about KNOWING YOUR DATA. 

To understand diversity and make strides towards true inclusion and belonging, you must take a deep, thorough look at what your data is telling you. 

An earlier Equal Pay Day sounds like a good thing, but what does a look beneath the surface data tell us? That Black and Latina women are overrepresented in low-wage jobs — jobs they are more likely to have lost because of the pandemic (source). 

In other words, the wage gap may shrink because women have left (or been forced out of) the workforce (source)!

There are many ways “good” diversity data can hide deeper issues. Let’s say you have a workforce with a higher-than-average proportion of women and nonbinary folx, BIPOC folx, and LGBTQ+ folx. Great, right? 

Well, what does the data tell you about who’s on performance improvement plans? What are the demographics of folx who were laid off or furloughed during Covid-19? How does compensation compare between different groups? Who’s getting promoted? (For example, Women in the Workplace shows that attrition alone cannot explain the underrepresentation of women of color and white women in senior management.)

This is a concept I go into detail about in my book, UNBIAS. It was a #1 Release on Amazon the week it came out! Order the book for yourself or your team here.

Do Something Different:

I cannot emphasize this enough: diversity is in the data. 

Have you taken more than a surface look at your company’s data? Here are some ways to start:

• Geography. I once worked at a place that had "great" diversity. But when you looked at the numbers by location, it was homogeneous: Black people all worked out of Inglewood, Asian people in Koreatown, and white people in Pacific Palisades. These are all in Los Angeles, but a closer look at neighborhood data revealed the truth about diversity.
• Family status. What does compensation look like between women and men, but also between mothers and fathers? 
• Department. Is one department better resourced than another because the head of that department has a better relationship with the CEO? Or simply because he’s male? 

My call to action for you is to look deeper. Understand what the data is telling you about the sneaky places where unconscious bias is alive and well, and holding back your employees and your company’s progress.

When have you taken a deeper look at data? What unexpected stories did it tell you? I’ve provided examples from my experience above, and I would love to hear from you. Share your examples in the comments!