Everyone in HR sees data as the crown jewels.
There’s a big problem with that, according toTim Payne, head of people and change practice at professional services firm KPMG UK. Knowing data is extremely valuable is all very well, it’s just HR (and other departments too for that matter) aren’t really sure what to do with it.
What people recognize is the potential for data, worked on by analytics, to be transformational to the HR department. Unfortunately, observes Payne:
There’s a very big gap between the promise of data analysis and the ability to do it. Most companies don’t have a single instance of data – even if they have data, it’s not in one place. The big challenge is getting data control and getting it clean.
The reality is there’s a piece of work first to clean up the data and create a culture where data is kept clean.
One thing that has fuelled the flames of big data take up is the ability to use cloud-based HR systems. Interestingly, Payne thinks that HR is actually probably only second to the sales team in its adoption of next-generation cloud technology:
The finance function is not in the cloud and we’re seeing lots of examples of old systems in finance, but Workday and SuccessFactors in HR.
Cloud has opened up opportunities, as it’s cheaper and easier to implement. Because it doesn’t need a year-long implementation, as systems of old, it’s more likely to gain approval.
Another benefit, says Payne, is that:
It forces you not to customize the hell out of it – it takes away the temptation and you focus more on the value add.
Payne points out that there are two ways of beginning the analytics journey. Most (KPMG included) are project based. Organizations embark on a series of one-off pieces of research into areas the HR director feels where HR can contribute most to the business.
This approach enables people to get used to analytics, begin the data clean up, but not be stymied by inaction because of the size of the cross-functional data clean-up required.
The other approach is to use real-time, always on predictive analytics at the touch of a button using a single source of sparklingly clean data. Not surprisingly, says Payne, “very few are doing that”, as it requires a massive data clean-up and cultural change.
Internally, one of the HR projects KPMG is using analytics is in the area of diversity. The company went back through past data and created a strategic workforce plan with an emphasis on diversity – looking at different grades in the business and the diversity levels in those grades.
Then they asked questions about will happen in the future. The HR team found that if the company were to do nothing, then the diversity situation would worsen. So, they asked what would happen if they recruited more at certain grades, and again the analytics pointed that this would make little difference.
Instead, the analytics showed that what would create a “marked difference” in diversity was tackling retention rates of existing staff.
Another area where analytics has influenced decisions is in performance management. KPMG used to give people a rating of their performance, the idea being that this would motivate them to improve or retain that grading. In reality, notes Payne, the rating had a very different effect:
We looked at data on the proportion of people who actually had a change in rating over the years. The data said that it doesn’t change much. If you’re a ‘three’ you generally stay a three.”
In fact, it found that the whole process of rating people and taking the time to explain to people why they received that rating was having a negative impact on morale. The rating system has since been scrapped, as it was getting in the way of real conversations about performance.
The HR team also correlated employee employment data with sales growth, profitability and staff turnover to see if they could predict outcomes. They found a correlation with answering certain questions on the employment survey (particularly to do with the quality of their team leader) and sales growth and profitability.
To use analytics effectively HR doesn’t need to grapple with statistics themselves, but they do need to “think like a scientist” a bit more, suggests Payne, as well as having a good grasp of the kind of questions that can be asked:
It’s more of a mind-set than necessarily having a PhD in statistics.
Forging links and encouraging secondments from research or marketing, where these skills may already be in use, can help.
KPMG, for example, is working with university PhD students, to work out if there’s a way these students could help KPMG clients with analytics – the clients would gain the benefit of expertise at a cheaper cost than calling in consultants, while the students would gain valuable real-world experience.
Payne does warn that there is a “dark side of analytics”. If analytics predicts someone is likely to leave the company, do you train them or let them go?
As our insight into employees and their likely behaviour grows, more and more ethical issues will surface and no one has really addressed that as yet.
It’s only a small caveat, however. There’s no doubt that analytics plays a key role in what Payne calls a “journey towards a more evidence-based HR function”.
And that really is the crown jewels for HR.
Analytics is a big deal for HR – there are few HR experts who would dispute that. It’s just the doing part that’s tricky.
What’s great is that along with the theory, in this interview KPMG’s Payne is able to share some real-life examples of the company has applied analytics to answer some of its own HR and workforce-related issues.