Gartner define the purpose of a diversity and inclusion manifesto, as a means of ensuring that the organisation is comprised of diverse individuals (based on individual characteristics, values, beliefs, and backgrounds) and to foster a work environment in which all employees feel respected, accepted, supported and valued.1
From that perspective, we understand that diversity and Inclusion (D&I) is a major part of our business culture that needs to be embraced by our organisation.
There are many good reasons for this to come to mind and we would like to highlight three of them:
1. More diverse and inclusive organisations will attract and retain a wider pool of talent (check-out our article about data analytics and talent retention)
2. A diverse workforce will foster innovation and productivity.
3. As a result, diverse and inclusive organisations will help entities to perform better financially.
Whatever the reasons might be, promoting Diversity and Inclusion in the workplace is something that organisational leaders are acutely mindful of. Progressively, we notice that more and more organisations are making Diversity and Inclusion (D&I) a core corporate goal.
From our own experience, within a people data warehouse that brings together people data from different parts of an employee lifecycle, we have demonstrated that this is an invaluable source of insights for D&I initiatives. We also noticed that there were areas of the organisation affected by potential unconscious bias. We then decided to run some projects to:
- Identify any hidden issues that could hamper the organisation in achieving it’s D&I targets over the course of the next five-years, and
- Address any unconscious biases that have been found to improve employee engagement (3x), reduce employee flight risk (3x) and drive innovative actions (2.5x).
The company approach:
- Descriptive analysis of representation across the organisation
- Applications and hiring
- Turnover and leavers
- Career progression
Some of the data used in the analysis is shown in figure 1.
Analysis of the data demonstrated how representation has been changing over time. In addition, issues have been quantified at a macro-level (affecting the entire organisation) as well as at a micro-level within a specific area of the business. For example, the analysis can uncover potential hiring bias against women in specific grades within a specific function and/or geography. This is deducted using benchmark analysis of hiring rates and application success rates across the entire organisation.
Beyond a static report – the Drivers of Representation metric
We also realised that a breakdown of the factors and drivers to changes made over time is also essential to gain insight into the trajectory on which an organisation is aiming for. It would also help organisations to determine specific areas of concern and work on them with appropriate initiatives on an on-going basis. This was the driving force behind the development of the Drivers of Representation (DoR) metric, designed to measure the following:
- Is an organisation on track to meeting a D&I purpose over time?
- Which are the key drivers for helping the organisation reach their objectives?
- How much progress on equity can be seen on internal policies regarding the representation of the subgroups?
The DoR metric is based around the view that an employee group is a closed ‘stock and flow’ system, as illustrated below in figure 2. Once an employee group is defined, all possible channels (or flows) in and out of this group can be identified and the metric is calculated for each.
Within an employee population, inflows are made up of; hiring, promotions in and lateral movement in the organisation. Outflows on the other hand are made up of; employees leaving the business, promotions out, lateral movement outside. The DoR metric for each flow is a proxy for its contribution to a D&I objective. The sum of all the components is referred to as the “total deviation” from target.
All inflows are tied up to the strategic corporate objective for representation (such as 50% female board representation). To do so, we have set the expectation that any inflow, such as hires or promotions, should respect the target representation. The outflows, however, were benchmarked against the existing representation at the start of the period. The assumption in this case is that outflows such as leavers, promotions and/or transfers out of the population should be evenly spread across the population. Any variation from the existing proportion indicates an unequal impact between the groups.
The DoR metrics help evaluate whether the organisation is heading towards achieving its corporate D&I targets.
To illustrate the third figure, we chose a made-up example of a function within an organisation over a two-year period where female representation dropped from 29% to 26%. As demonstrated in figure 3, the number of females hired and moving into the function are aimed to a target of 50%, while the target number of females leaving and moving out of the function should be above 29% to be aligned with the representation of the gender stream at the start of the period. The total deviation (sum of DoR contributions) is -12%; but if all targets were met, the female representation in the function would have been at 38% at the end of the two-year period. The current representation is 26%, which is approximately -12% away from 38%. From the contribution DoR metrics, we can note that the major issue is that hiring is way off a target while other inflows and outflows are less taken into consideration.
Ultimately, we believe that the DoR metrics can be filtered and benchmarked across different business dimensions to provide organisational leaders a valuable tool to track progress towards D&I objectives, identify problem areas and make some actions towards them when the need arises.
If you want to learn more about how to improve diversity and inclusion in your organisation? Check-out our article on the role of data in achieving diversity, inclusion and equality in the workplace here.
Author: Darshan Baskaran – People Analytics Lead, TrueCue