The need to proactively manage diversity and inclusion has become an accepted business practice. Historically, companies have used fairly simplistic workforce analytics to support their D&I initiatives. Today, because few companies have blatant forms of discrimination with regards to their workforce, more insight into the effectiveness of D&I interventions is required by the use of more comprehensive Workforce Analytics.
The historic reporting of D&I has been simplistic and has not provided the level of insight that is possible. Using gender as an example, we have made some strides in moving beyond simply reporting the gender mix of companies. Over the past two decades we have seen organizations reporting gender by grade or level, average salary by gender by grade/level, and average percentage salary increase by gender by grade/level. We have also analyzed gender diversity by occupational group, by organizational unit and by location to look for variations. This gives us some additional insight into potential areas of gender inequity, but in order to drive action and to make sustained, concrete changes in this area it is now time to add another level of sophistication to D&I reporting and analytics.
What kind of information and insight would the use of more sophisticated analytics provide?The first level of “upgraded” analytics be to analyze performance ratings, irrespective of whether its an annual assessment or continual assessment, by diversity group with promotion rates. We would want to be able to answer questions like:
Is the same ratio of females, or other diversity groups being promoted relative to males if they have similar performance ratings? If 10% of females are given “Outstanding” performance ratings compared to say 10% of males, then do these groups have similar promotion rates in a given year. The key issue here is that the analytics are segmented to provide real insights into our D&I initiatives. To know the number of females promoted in a year relative to the number of males promoted in a year is basically useless information. To know the percentage of females promoted in a year to the percentage of males promoted is not much better. However, if we can compare the experiences of employees that are similar in job-related ways but different in demographic characteristics, we can start to understand where we truly need to focus our D&I efforts.
As a minimum from a D&I analytics perspective, we want to know measures such as :
* The percentage of promotions by gender at each grade/level within the organization.
* The percentage of voluntary terminations by gender by each grade/level
* The percentage of external recruits by gender and other diversity levels by grade/level.
We would then want to analyze this information by each main occupational group, by organizational units, by lines of business band by locations. From this analysis we can look for variations in our D&I performance that we want to address in a proactive way
This type of analysis can then be extended further. One of the challenges around gender equality within the workforce is the impact of career breaks on promotion rates. In most of the Western world, with the exception of Scandinavia, maternity/ paternity leave is taken almost exclusively by females. What negative and unintended consequences does this have on their careers? An important analytics within the area of D&I is the relationship between gender, tenure (position and organizational), performance rating and promotion rate. If we were to investigate this relationship within an organization my initial hypothesis would be that individuals, male and females, who return to work after an extended career leave (eg maternity leave), receive lower performance ratings than the average they had in the previous two years before taking the extended career leave. Extending this, individuals who receive lower performance ratings than their average longer term ratings are less likely to be promoted. My view is that females returning from maternity leave will take longer before they are considered for promotion than males of similar performance who did not take an extended career break with paternity leave.
Is this a level of subliminal gender bias within organizations? How do we ensure that anyone returning from maternity/paternity leave is not discriminated against solely because they look extended leave? Should we ensure that we have HR that put equal weighting, or more weighting, on the performance assessment before taking an extended career break when we are evaluating candidates for promotion? These are the kinds of questions that such insights would enable, driving targeted actions or interventions towards reducing subtle and unintended discrimination.
If companies want to maximize their performance then they need to ensure they are the maximum return from their own internal human capital. To do this they need to ensure that there is no subtle form of institutionalized discrimination which prevents the organization from maximizing the contribution of their entire workforce. I believe very few or no companies have conscious discrimination on the basis of non-objective performance criteria. Foe most companies D&I discrimination is very subtle and unconscious. Workforce Analytics has a important and fundamental role to play in highlighting this subliminal discrimination in order to ensure companies maximize the contribution of their entire workforce.