The Equity Lens: How Misinterpreting Data Hurts Underrepresented Populations

Data, both qualitative and quantitative, tells us a story. A story of our subjects, the populations for which we often intend to help through research. In my field of work I have witnessed academic researchers and professionals alike use data to support disadvantaged populations and to severely harm them (intentionally and unintentionally).

Recently, I was at a talk that was discussing data in the modern age and I couldn’t help but think one thing: what the hell are these people doing? Let me explain. The manager of an engineering department (a White male) was highlighting the progress they had recruiting racially diverse candidates in the last two years. He made the statement, “before our recent hires, minority populations only accounted for 2% of our turnover rate. Sounds like a small number right, let’s look at why this statement is and can be very misleading. In my head I was asking the following:

1. OK homie…. What’s the percentage of minorities in your company and your department?
2. What are the annual averages for your turnover rate?
3. Which minorities are more likely to leave your company/department?
4. What is their average length of employment in your company/department?
5. How are you tracking turnover/exit rates?
6. What process do you use to understand the reasons that they leave?

These are just a few of the questions that flew through my mind as I listened to him speak and his words started to sound as confusing as the teacher from the Peanut cartoons!

The issue here is that whenever there are minority numbers, there is more than likely a bias that leads to misinterpretation of data. For example, if you only have two black women in your company of 100 people, the account for 2% of the population. If they both leave within a year, then they also account for 100% of the turnover rate for Black women in your company! Even if your company only lost 4 people that same year, 50% of your turnover rate is accounted for by Black women. This is a trend as well as an overlooked number by many companies due to the fact that companies usually stop at the narrative of we’ve only lost 4% of our staff. It makes you feel good and look good. But heed the statement, that everything that makes you feel good, is not good for you or the people whose stories are being erased through your unconscious blasphemy. Instead of digging deep into the counter stories of underrepresented populations, you use a narrative that favors a majority of your workplace identities and this is a problem. In order to truly understand our stories, you have to dig deeper into the roots of your systemic discrimination even in the formulation and interpretation of data, this is called an equity lens.

An equity lens is the ability to interpret data through both a majoritarian narrative as well as a counter narrative and understanding that for certain populations, you have to adjust your lens to interpret their circumstances and barriers that impact inequitable outcomes. And this is needed more than ever, especially now as companies are ramping up their recruitment practices. DO NOT ramp up the recruitment strategies to include racial minorities and other diverse (visible and invisible) populations if you haven’t ramped up your ability to understand why they’re not there in the first place. You have work to do, but first, approach it equitably.