More and more organisations are taking their first steps with HR analytics. A recent panel debate hosted by the London HR Connection looked at where they should set their priorities and the key issues that need to be addressed as HR builds its competence in this area.
Every HR professional knows that analytics are becoming must-have capabilities – helping the function to do anything from identifying employees at risk of quitting to detecting potential fraud. A recent survey by Deloitte found that 36% of companies are now using people data to predict business performance.
HR analytics: further reading
At this month’s London HR Connection debate: If HR analytics are the answer, what are the questions?, a panel of HR professionals at different stages of the HR analytics journey debated how HR can up its game even further in analytics, and what should be measured.
Dr Martin Edwards, author of Predictive HR Analytics and a statistics lecturer at King’s College London, challenged the frequently cited view that HR lacks the capability to exploit analytics.
He said: “If we look at the research that has been published in recent years, the perspective is that HR will fail to rise to the analytics challenge, that it will be a fad that passes us by. I personally don’t agree – it’s a developing field.”
Edwards pointed to an innovative project at pharmaceutical company Astra Zeneca, which has mined thousands of pieces of historical data to predict the likelihood of someone resigning.
The company has also used data to discover that in one area of the business, external candidates were more likely to be shortlisted for a vacancy, but if an internal applicant was interviewed, they were far more likely to be offered that role.
Christian Cormack, head of HR analytics at AstraZeneca, explained how this has begun to change hiring managers’ perceptions: “We’re not trying to turn HR professionals into statisticians, what’s important is to get HR to understand the nuances of the metrics, to interpret them correctly to start a conversation.”
How effectively the data insights will be used can depend on how well they are explained and communicated, he added: “You really have to know the data to be credible and to tell the story with confidence.
“[When presenting data], we try to make the title of each slide a bold statement that connects with the business narrative and tells people what’s going on in the business – it takes bravery to put an opinion in the title.”
Nathan Adams, HR director, partnering global risk and audit at insurance company Aviva, said the best advice is to start out small.
“Perhaps surprisingly, for an analytical and risk driven organisation, we are only just starting out on the analytics journey in HR.
“Don’t try to rule the world and say you’ll pick everything and anything to analyse. Make sure there is a business problem to solve.
“You could end up spending a lot of time trying to work something out, so make sure it’s a priority to sort and you can actually implement something off the back of the analytics. We use this as a provocation – if we found something, could we actually do something about it?”
At Aviva, using analytics has helped to challenge assumptions. Data insights revealed, for example, that the highest performing employees in its life insurance business were not older workers – who you would assume would be able to understand and empathise with the customers better – but 20- to 30-year-olds.
While the panellists agreed that it was useful to recruit data analysts to help crunch the data, it was up to HR to know how to ask the questions, and ultimately have a sense of what to do with the answers.
Mark Sheridan, global head of HR for HSBC Commercial Banking, said: “You don’t need to be a data analyst to be curious, you just need that human urge to explore what goes on around you. Or, if you’re presented with incongruity, can you use the data to try and explain?”
He conceded, however, that the quality of data could often get in the way. “You might have multiple reporting tools from different sources, systems might have a different definition of ‘surname’, and information that you acquired during the recruitment process might end up being corrupted.”
One solution might be to drop data into an operational data store, he added, rather than trying to get every system to talk to each other.
Degree of control
Edwards warned that HR should not get too constrained by trying to find tangible evidence when so much of the people function is subjective.
“When you do a business case, you can say you can reduce turnover by x% if we invest £y, but the degree over which HR has control over certain metrics can be hard to predict,” he added.
“Income generation is not the only thing to measure; there are so many things HR does that have an impact on the business.”
And with good data and the appropriate skills and tools to analyse it, the potential insights HR can derive seem infinite. But just because you can ask a question, does that mean you should? Where should HR stand on the ethical use of employees’ data?
“I think we should learn about this from customer organisations,” said Aviva’s Adams. “We all know that there are elements of personalisation that annoy us or switch us off, but we can also personalise an experience for someone’s benefit.
“There are several pros and cons that organisations need to consider, but I think it’s important to draw a line and be clear up front about what you’ll do with the data, looking at it in aggregate rather than on an individual level.”
As with everything in the fast-growing field of analytics, getting to grips with the ethical implications will be as much of a learning curve as refining and crunching the data.