Adapted from Racial Justice At Work: Practical Solutions for Systemic Change (Berrett-Koehler, February 2023)
Data has become the powerful engine that drives decisions in every organization. With the rapid advances in technology, the ways in which data can be used to provide insights and intelligence has grown exponentially. Such capabilities give us better tools to uncover systemic inequities embedded in organizational systems.
Data scientists who specialize in DEIJ are emerging to support organizations in more robust analysis. Most of the commercial analytical frameworks on the market focus on quantitative reporting. We continue to live in a world that believes if it cannot be counted, it does not count. These tools may assess pay equity, reveal correlations between race and upward mobility, or the rate of voluntary to involuntary terminations by ethnicity. They have the capability of devising inclusion indices and predictive analytics.
If organizations are really going to hold themselves accountable to data-driven standards to correct racial inequities, we need both qualitative and quantitative methods. Thamara Subramanian, Equity Audit and Strategy Manager at The Winters Group, says, “to root out injustices, we need to give just as much credence to the stories and lived experiences of BIPOC that might not fit neatly into a statistical algorithm.” This requires us to interrogate and not just analyze the outcome data. We have to get to the root cause of how the system is working that continues to get the inequitable results.
Here are some examples of stories behind the data that help us to uncover the systems that continue to perpetuate inequities.
Leadership Development
Most of our clients lament that BIPOC are underrepresented in leadership roles. For example, Black professionals hold only 3.2 percent of all executive or senior leadership roles and less than 1 percent of all Fortune 500 CEO positions.
A client recently shared with me that her organization has a leadership development program where they hire people from outside of the company, engage them in a fast-track development program, and within a year they become first- and second-line supervisors in their manufacturing facilities. While there is little racial/ethnic diversity in those who are selected, the employees whom they manage are by and large Black, Indigenous, and people of color (BIPOC). Our client had asked why there were not more BIPOC hired into the program and the answer was: “We cannot find any qualified candidates.” She then asked, “Where do you source?” The answer to that question revealed that Historically Black Colleges and Universities (HBCUs) and Hispanic-Serving Institutions (HSIs) were not on the list. She then asked, “What are we doing to develop entry-level individual contributors to assume leadership roles?” The answer to that question was “nothing.” Barriers such as lack of computer and communication skills were offered as reasons. My client is encouraging HR leadership to reimagine the process with the possibility of a promotion-from-within program. She also recommended that they expand their sourcing options to include HBCUs and Hispanic and Indigenous-serving institutions.
As another example, The Winters Group conducted a cultural audit for a client a few years ago. This client had a goal of improving women of color in leadership. During our analysis period, they had 100 openings that they filled with external candidates. Women of color had applied at the same rate as white women; however, only one woman of color was hired out of the 100 open positions. Twenty white women were hired. This is where the interrogation begins. Why was that the outcome if there was a stated goal to improve the representation of women of color? We recommended that the company review every resume received, as well as interview notes to understand this outcome. It is a reverse engineering of sorts — backtrack to understand what happened in the system that caused that result. What is the story behind the data?
Promotions
Some very large Fortune 500 companies that we have conducted audits for do not maintain reliable data on promotions. They change job codes or have inconsistent titling of jobs, making it difficult to analyze promotions data. The excuse that “our data is not in order” is a form of injustice in and of itself. Even those organizations that claim to be data-driven often impede equity auditing because of their lack of valid data.
For those organizations where we can examine promotion history and projections, analysis often reveals concrete ceilings and sticky floors. One large health care client that wanted to improve the promotion rates for people of color developed an elaborate process to identify employees who were “high potential” and “superstars” with at least a “satisfactory” performance rating. The question is: Who gets to decide who is a superstar? By whose standards? The narrative is often based on white dominant culture standards of presence, performance, and competence.
“She would be great if she wasn’t so loud.”
“He would benefit from communication training. His accent is hard to understand and will hold him back.”
“They don’t dress professionally.”
These (subjective) criteria often tell the story behind the numbers.
Terminations
In our audits, we often find that BIPOC are terminated involuntarily at disproportionate rates. The reason codes do not provide the whole story — “poor performance,” “violation of the rules,” and “misconduct” are popular categories. To understand the disparities, we need to interrogate what is happening in the system that creates this outcome.
A number of years ago, a client wanted to understand why their BIPOC employees were terminated at a higher rate for tardiness and poor attendance. Research revealed that many did not have reliable transportation to get to the work site, which was located outside of the center city where many BIPOC employees lived. Some had to transfer from subway to bus and then walk up to a mile to get to work. The client collaborated with the regional transit system in that community to extend bus service and started to provide more flexible work schedules to address the problem.
Commit to Seeking Out Stories Behind Your Numbers
Without the stories behind the data, we use our own limited lens to draw conclusions. “We did not hire more BIPOC into leadership because there is a lack of qualified candidates” tells a very different story from, “We did not look in the right places” or “We did not acknowledge our own biases and criteria derived from a dominant culture mindset.” Similarly, “BIPOC are more likely to be involuntarily terminated because of a poor work ethic” is a harmful story to continue telling when the reality is: “Our physical facility is located in a community where BIPOC employees do not live because of historical discrimination and redlining.”
Learning about the stories behind the data is a critical step in upending unjust systems.
