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Understand Your Diversity Pipeline

I’ve worked on and led a number of diversity and inclusion projects, some relatively humble others well into the millions of dollars, regardless of your budget you can do the following with the people and tools you have already – the fact is I did the following alone using publically available data and MS Excel. What prompted this post was an article about an organisation putting out a target to employ X number from an EEO group by X date. And look I support these initiatives, however first some really basic analysis needs to be undertaken to ensure the effort is placed in the right direction.

Take a look at your local population, take a look at the population that you’re drawing talent from. So as I’ve mentioned before I worked in the telecommunications sector, and they were concerned about the number of women employed in technical roles, and senior management felt it should be more representative of the population – 50/50. They were right, except their concept of a population was all wrong. They were looking at the general population, when actually these roles required degree qualified people with majors in computer science and programming – quite a different population. When we took a closer look at graduate percentages from universities across New Zealand, we found that indeed the percentage of women employed in these roles within the organisation was very representative of that graduate population.

I’ve also worked in wider sectors such as Government, where rather than focusing on one job family (IT in the above example), the focus was across Government (Police, Teachers, Professionals, administrators, frontline staff, etc). One of my roles was working in a location where a large minority of the population was an EEO group underrepresented within the Government. However, on analysis, they actually weren’t underrepresented to the extent that the public and interested parties believed. If factors such as age distribution of the population, literacy and numeracy levels, and life expectancy were taken into account, the organisation was actually doing a great job as an employer.

In both these cases, senior management had both their hearts and minds in the right place, increasing the diversity of the organisation and being positive corporate citizens. However, the ways that they initially were going to undertake these programs (and indeed some did still go ahead), was fundamentally flawed. In the example of the telecommunications organisation, they were a good employer, so rather than changing the way they recruited they were better off putting some scholarships on the table and encouraging greater numbers of women to enroll in the degree streams they employed from. Similarly, the Government organisation was a good employer, it didn’t need to change its external recruitment methodology, rather it needed to address education and health outcome issues in the target population – thereby increasing the available talent pool. Unless your organisation is evil and has recruitment and selection practices which directly preclude groups, then chances are your diversity strategies need to be long term rather than quick wins. Your role as an HR practitioner is to ensure you provide an accurate analysis to your stakeholders, be it your executives or your board, so they can make informed decisions.

Related Podcast: Diversity Is More Than A Metric (with Anthony Clay from Indi)

Related Article: How To Write Your DEI Mission Statement (And How To Do It Justice)

By Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.

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