Analyzing Diversity & Inclusion Data

When companies conduct Employee Surveys, they often include questions on Diversity & Inclusion. In fact, some companies regard the issue so important that they conduct dedicated studies on this topic (see example). According to Towers Watson’s Talent Management & Rewards Survey 2014, 39% of the over 1600 companies we spoke to have formally structured programs to manage diverse employee populations. The number is very similar at 40% in Asia Pacific. Predominantly, these programs are targeted at employee groups by gender, people with disabilities, racial and ethnic groups, age, sexual preference etc.

In a conversation with a fellow professional, I heard them say “Over 80% of the employees in my company feel that we have created a work environment which respects differences between gender, race / ethnicity, age etc. We have done our bit on diversity management.” But, to me, this was not necessarily the conclusion one can draw from the data.

When analyzing employee opinion data on diversity & inclusion, we should not look at data at an aggregated organizational level. Instead, subject to data privacy issues, we should aggregate it by variables towards which our diversity programs are targeted. For instance, we should slice the data by minority races or genders to see how the “affected” groups are feeling. That will help paint the real picture and help us draw better conclusions. We should also look at segmenting data further to see where trends and anomalies develop. For instance, we could analyze data by tenure / age to see at what points do gender gaps emerge.

If we simply look at the overall organizational data, we will simply be looking at the views of the majority of the workforce, who are actually not affected by absence of diversity / inclusion practices. Such a segment may also underestimate the nature, size and impact of the problem.

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