Predictive Analytics - Using Your HRMS to Get More out of Your HR Data

The bigger the dataset, the more sophisticated and accurate the insights. Given the old adage that past behaviour (and current behaviour) is the best predictor of future behaviour, it’s no surprise that predictive analytics have a been a hot topic in HR circles for a while. Building on the classic KPI, which is a backward-looking measure, HR analytics seek to inform strategies with data-driven insights into the future, increasingly more often using an HRMS capable of handling HR predictive analytics.

Commentators have suggested that there are four levels of analytic reporting. The first is the basic operational type of report of the ‘number of this,’ ‘number of that’ variety, providing easily measured pockets of data, isolated and therefore difficult to draw too many conclusions from. The next is similarly operational but is a little more advanced, taking advantage of broader information sources to benchmark and analyse performance in wider context. Third is the more strategic level, taking current information and subjecting it to deeper segmentation, statistical analysis and developing ‘people models’ as a result that can inform decision-making. Finally, comes the fourth predictive level of analytics which allow for scenario planning, risk analysis and genuinely contribute to forward-thinking strategies.

Recommended reading: use this comprehensive guide to boost your predictive analytics activities and get more out of your HR data

Unfortunately, some of those same commentators note that the first two levels make up the vast majority of the analytical reporting in current use. That as soon as wider and external data sources come into the mix, HR people tend to shy away, feeling ill-equipped to understand and use more complex HR predictive analytics data. It’s not that HR folks don’t want to use predictive analytics, more that they just don’t know how.

The UK’s CIPD has recently looked at this issue of HR’s seeming inability to engage with predictive analytics (and Big Data in general) and found that HR staff are still on the whole more comfortable with a more traditional skillset. The classic HR person is comfortable with interpreting ambiguity and understanding organisational context, able to talk knowledgeably about changes in corporate culture and how people interact. However, measurement, statistical analysis, and evidence-based decision-making are still somehow seen as more the province of departments such as Finance, Logistics and Marketing.

What is indicated is a more future-focused alignment. By all means using data about what has happened in the past, but only to predict the answers to questions about the future. Part of the solution is to break down the structural and systemic silos that people have traditionally worked within. A greater need for cross-disciplinary collaboration is called for and again, it is technology that offers the solution: HRMS technology offering social collaboration software that breaks down communication barriers and creates the ground for a broader and less specialised understanding of what influences are acting on the workforce.

The other half of the solution is to develop (and recruit) a more analytical skillset within the HR function. To value and encourage the ability to process and analyse HR analytics in a complex and qualitative manner that will facilitate a genuine introduction of HR predictive analytics and also into the boardroom.