Is this the answer to improving diversity outcomes?

The biggest breakthroughs in improving diversity outcomes might come down to a more sophisticated use of HR data

Is this the answer to improving diversity outcomes?

The biggest breakthroughs in improving diversity outcomes might come down to a more sophisticated use of HR data.

According to Kim Cheney, head of HR, Paypal Australia, data is ideal for helping pinpoint the areas that need to be improved.

“What problem are you trying to resolve? If you know the answer then data can absolutely help. It enables you to pinpoint where you've got areas to improve on, where you're doing really well and can give you goals to strive for,” said Cheney.

The next step in HR’s data evolution is to ‘cross-pollinate’ data-sets from different areas of the business, in much the same way marketing and finance teams have been doing for some time.

Indeed, this capability could have a “profound effect” on D&I outcomes, according to Thomas Hedegaard Rasmussen, GM people analytics, insights & experience at NAB.

“There’s no straight line from diversity to better performance or more innovation, as there are other factors that have an influence, such as how inclusive your organisation’s leaders are,” he said.

However, he added that the data models are getting more sophisticated so the correlations are becoming easier to identify.

“You can look at gender or age or ethnicity, one variable at a time, but it’s super artificial because that’s not what the real world is like,” said Hedegaard Rasmussen.

“Modern organisations will look at each of these at data components combined, and will also have data sub-groupings within various organisational teams, and so on.”

However, a word of warning about D&I data was voiced by Jason Laufer, senior director, talent and learning solutions at LinkedIn. Laufer said that the outcomes from data-led initiatives will only be as good as the data put in.

“Data in D&I is dependent on people identifying themselves within a particular group,” Laufer said.

“When it comes to areas like LGBTI or religion it becomes trickier as it’s more difficult for people to self-select into a particular group or sub-group.

“Part of HR’s role is to ensure they are creating an environment where it’s ok for people to self-select; that people feel safe in doing so. From there, you can roll out D&I initiatives that you know are going to resonate with the individuals you have in your organisation.”

Looking more broadly at technology, Laufer firmly believes technology will provide solutions – if not all the answers – to address disability discrimination.

“An example would be around flexible work arrangements,” he said. “How do you help someone with a disability who might only be in the office one or two days a week because it's physically difficult? So what are we doing about video conferencing, file sharing, those sorts of technologies to make it easier, or to ensure they have the right facilities at home to support them when they need to work remotely?”

So how can D&I data be used to improve outcomes?

  1. To influence top leaders. Data can be presented in graphic and statistical reports in ways that are easy for leaders to understand. When armed with facts that are clear and actionable, decision makers can tackle pinpointed issues, and use resources appropriately.
  2. For multi-dimensional analysis. When tackling complex questions, organisations can use in-memory analytics, which are designed to deliver multi-dimensional analysis. For example, to tackle pay gaps, you may want to know if your minority employees receive raises at the same rate as the rest of the population. This question has many layers such as rate of change across populations, whether performance standards are equal among populations, market pay rates, employee tenure and more.
  3. Identify who’s a flight risk. Use predictive analytics to determine who is at risk of resigning. Recruiting diverse talent is one thing, but if your minority talent resigns, you haven’t done much to improve the diversity of your workforce. Predictive analytics can look at specific gender or ethnic populations to determine who is likely to resign. This information can be used to create initiatives to improve the work experience of those populations more likely to leave.

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