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People Analytics

Here's how the HR function can be transformed with Data Analytics

Three suggestions for a smarter, data-driven HR function — get creative with data sources, use the full analytics toolbox bottom-up, and put approach before tools.

3 min read
HR ANALYTICS START AT THE BOTTOM OF THE PYRAMID CREATIVE DATA SOURCES HR systemsBiometricsIntranet logsSmart-office sensorsExternal data DESCRIPTIVE what happened DIAGNOSTIC why it happened PREDICTIVE what will happen PRESCRIPTIVE how to influence it BOTTOM-UP OUTCOMES Reduce hiring biasFind performance driversImprove relationshipsPredict attrition EXPLAINABLE RESULTS · INTERPRETABLE MODELS APPROACH > TOOLS

Data analytics can be an effective tool to understand employees and their engagement levels in a better way. It has the power and potential to transform the length and breadth of the HR function. It can help reduce hiring bias, find drivers of performance, improve employee relationships and help manage attrition.

A wealth of data is captured through HR processes. Right from initial contact to long after employees move out of active engagement, the collected data can provide useful insights. And it can turn into a goldmine when supplemented with the right external sources.

Here are 3 suggestions for a smarter HR function:

1. Get creative with your data sources

A common complaint in organizations is the lack of data or curated sources. This is often the reason behind slow progress in making HR more data-driven with people saying that the entire function is driven by Excel sheets. Or how it’s difficult to pull together a single employee view, despite being in the age of digitization.

In every such situation, solutions emerge if one gets creative. Organisational systems generate numerous data trails. Data from biometrics, CCTV feeds, intranet logs or sensor-enabled smart offices can be harvested without treading on privacy or ethics.

2. Make the most of the full Data Science toolbox

When you mention analytics, people invariably start with AI-driven algorithms. Predicting behaviour comes even before understanding what employees want. Even in the analytics toolbox, there is a parallel to the concept of the fortune at the bottom of the pyramid. It is important to start simple and discover ‘what happened’ and ‘why it happened’ for maximum business impact. One can then analyse what will happen in the future or how it can be influenced. There’s value in employing the full analytics spectrum, bottom-up.

3. It’s all about the approach, tools don’t really matter

When starting analytics initiatives, don’t dash straight to deciding on the right tool. The tool is not going to dictate the extent of magic that can be created. The good news is that you don’t need huge investments or fancy tools. The bad news is that the real value of analytics is determined by the quality of approach. So it’s far more people-dependent, as well as subjective. Getting the method right can be an enabler, but it also depends on the quality of your team.

The value from HR analytics is all about the solution approach, just like any application of Data Science. The algorithms need as much framework as computing power. For example, if you want to build a model to predict employee attrition, you should start with all available factors. Then carefully prune the list by investigating relevance and relationships.

Realizing the true potential of HR Analytics

Organisations have a lot to gain from the power of HR Analytics. To make the most of this power and realize its true potential, one must:

  • Get creative in detecting newer sources of data for business intelligence
  • Leverage the entire analytics toolbox, starting with descriptive insights
  • Place business context and approach at the centre, in order to drive the initiatives

To ensure that the suggestions actually see the light of day, keep the analytical results explainable and models interpretable. This is critical in order to win over the confidence of decision-makers. It may also help HR stakeholders become conversant in leading insight-driven interventions.

  • HR Analytics
  • People Analytics
  • Data Science
  • Attrition