Gartner defines 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
As such, diversity and Inclusion (D&I) is becoming increasingly important for employers and employees alike.
There are at least three good reasons why this is that come to mind:
- More diverse and inclusive organisations will likely attract and retain a wider pool of talent (check-out our article about data analytics and talent retention)
- A diverse workforce will therefore foster innovation and resilience
- As a result, diverse and inclusive organisations will perform better financially.
There is plenty of research that backs-up the above claims. Nevertheless, whatever the reasons, it is evident that promoting Diversity and Inclusion in the workplace is something that organisational leaders are acutely aware of. Increasingly, more and more organisations are making Diversity and Inclusion (D&I) a core corporate objective.
A people data warehouse that brings together people data from different parts of an employee life cycle becomes an invaluable source of insight for D&I initiatives. We have demonstrated this in projects, where we identify whether there were any areas of the organisation affected by potential unconscious bias. The objectives are twofold:
- Identify any hidden issues that could hamper the organisation in achieving it’s ambitious D&I targets over the course of the next five-years
- Address any unconscious biases that have been found as this can improve employee engagement (3x), reduce employee flight risk (3x) and drive innovation (2.5x).2
We have developed hypotheses covering four key topics;
- 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 clearly demonstrates how representation has been changing over time (hopefully, improving). In addition, problem areas are quantified at a macro-level (affecting the entire organisation) as well as at a micro-level within a very specific area of the business. For example, the analysis can uncover potential hiring bias against women in specific grades within a specific function and geography. This is inferred using benchmark analysis of hiring rates and application success rates across the entire organisation.
Beyond a static report – the Drivers of Representation metric
A findings report with analysis provides actionable insight. However, we also realise that a breakdown of the contributing factors and drivers to changes in representation over time is also essential, so as to gain insight into the trajectory on which an organisation is heading. It would also allow organisations to determine specific areas of concern and address them with targeted 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 target over time?
- Which are the key drivers for helping the organisation meet / miss the targets?
- How equitable have internal policies been on the representation of the subgroups?
The DoR metric is based around the view that an employee population is a closed ‘stock and flow’ system, as illustrated in figure 2. Once an employee population is defined, all possible channels (or flows) in and out of this population can be identified and the metric is calculated for each.
Broadly speaking, within an employee population, inflow is made up of; hiring, promotions in and lateral movement in. Outflows on the other hand are made up of; employees leaving the business, promotions out, lateral movement out. The DoR metric for each flow is a proxy for how it contributes to a D&I target. The sum of all components is referred to as the “total deviation” from target.
All inflows are tied to the strategic corporate target for representation (For example, 50% female representation). In doing so, we have set the expectation that any inflow, such as hires, should have at least the target representation, as this should be the ideal case in an equilibrium. The outflows, however, were benchmarked not against the corporate target, but 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 deviation from the existing proportion indicates an unequal impact between the groups.
The DoR metrics help evaluate whether the organisation (or a part of it) is heading towards achieving its corporate D&I target. It also helps uncover the underlying contribution of each inward and outward employee flow.
To illustrate, let’s take the made-up example of a function within an organisation over a two-year period where female representation dropped from 29% to 26.0%. As demonstrated in figure 3., the number of females hired and moving into the function are compared to a target of 50%, while the target number of females leaving and moving out of the function should be no more than 29% to be representative of the gender representation at the start of the period. The total deviation (or 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 see that the major cause of the problem is that hiring is way off target while other inflows and outflows are less of a concern.
Ultimately, we believe that the DoR metrics, that can be filtered and benchmarked across different business dimensions, provide organisational leaders a valuable instrument to track progress towards D&I targets, identify problem areas and surgically address them when the need arises.
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.
Darshan is a Senior Consulting Manager and Analytics Lead at TrueCue. He is passionate about empowering organisations to see and understand data with modern analytics tools such as Tableau, Power BI and Alteryx.
Prior to transitioning into Analytics Consulting 7 years ago, Darshan received a PhD in Molecular Biology from Oxford University. Partially down to this academic background, his project work has focused in the Healthcare and Pharmaceutical sectors. Data is literally and metaphorically in Darshan’s DNA.