People Analytics: The HR Revolution
Advances in technology and the way we collect and use data is rapidly changing the way organisations manage their people. But is it for the better?
Human Resource (HR) Management has come a long way in the last two decades. A range of new information systems has enabled the automation and digitisation of human resource management tasks and activities, from basic record keeping, to hiring and onboarding, and keeping track of workers’ experiences and emotions.
The increasing volumes of data generated by these systems has created the scope for greater use of analytics in HR decision-making.
Advances in data science are also creating new ways of analysing these data, creating scope for automation of HR activity that previously demanded large amounts of human time and effort. For example, machine learning can analyse large volumes of text responses to open questions in employee surveys that would have been too time-consuming for people to do.
The value of this innovation can be seen with companies like Microsoft, who have used new technologies to support their employees during the Covid-19 crisis.
By replacing their annual staff attitudes surveys with short daily surveys sent to smaller samples of employees, Microsoft could track in real-time how their employees were doing, rapidly analyse responses, and adapt their policies and practices to support staff who were struggling with working from home.
The net effect of this is that HR is currently undergoing something of a revolution. Yet, despite the buzz, coverage of this revolution is uneven.
The 2011 film ‘Moneyball’ dramatised the adoption of HR Analytics by the Oakland Athletics Baseball team, and their general manager, Billy Beane (played by Brad Pitt). This film tells us a lot about the reasons why some organisations are ignoring the analytics revolution in HR.
Beane experienced strong resistance from talent scouts and coaches who were sceptical of the power of analytics to tell them anything new. They thought knowledge of what makes a good baseball player came from expert intuition, something a good coach knew in his gut.
We can see the same dynamics at work in other organisations. Successful managers think they know how to manage people and are sceptical of whether analytics can tell them anything they don’t already know.
In baseball, the results spoke for themselves and other teams were soon copying Beane’s methods. However, in baseball, the task of quantifying human performance is a relatively straightforward one. In other types of organisations, applying analytics can be a more complex task.
“Successful managers think they know how to manage people and are sceptical of whether analytics can tell them anything they don’t already know.”
Quality over quantity
This complexity has a number of dimensions. The data that HR information systems and digital HR tools generate don’t always help to answer the most strategically important HR questions.
It is important for organisations to have a clear idea of what they want to achieve with HR analytics otherwise there is a risk that HR analysts simply get lost in data, churning out numbers and reports that add little value.
Asking the right questions requires people who have a keen understanding of what analytics can usefully achieve; ‘data translators’ who can frame business problems as analytics projects and identify the actionable insight arising from analysis.
Another key issue is that of data quality. The existence of large volumes of data is a by-product of automation and the use of IT and digital systems, so if different parts of an organisation use these tools in slightly different ways, the data may not be directly comparable.
This necessitates labour intensive efforts to clean up data anomalies and introduce new data governance procedures to prevent their reoccurrence.
Moreover, if organisations start out trying to answer low value questions with poor-quality data, the results will be disappointing and those sceptical of using data to inform people management decisions will be emboldened to reject new ideas and initiatives.
A question of ethics?
Using HR analytics also raises ethical questions; just because something can be done, it does not mean it should be done. New digital tools can dramatically increase the ability of employers to monitor employee behaviour both inside and outside of work – and the widespread shift to home working during the pandemic has led some employers to monitor their workers’ activities within their own homes.
Using HR analytics in ways that employees find unacceptable may cause problems with employee morale and motivation. There are also legal and ethical risks with the automation of decision making about people. If data used to make or inform decisions reflect existing discriminatory behaviour, it is likely to reproduce that discrimination.
The upshot of this is that HR analytics is transforming the way some organisations manage their people. But such transformations only happen when organisations and those who manage them have a deep appreciation of the practical, technical and ethical dimensions of using data and analytics to inform practice.
In that sense, there is still a way to go before analytics is the dominant decision-driver in people management and the human resource revolution is fully realised.