HR Analytics: An Introduction

Human Resource analytics is a data-driven approach to managing people at work. HR analytics, also known as people analytics, workforce analytics, or talent analytics, revolves around analyzing people problems using data to answer critical questions about your organization.

HR analytics is the application of statistics, modeling, and analysis of employee-related factors to improve business outcomes.

Human Resource analytics (HR Analytics) is defined as the area in the field of analytics that deals with people analysis and applying analytical process to the human capital within the organization to improve employee performance and improving employee retention.

Definition – Predictive HR Analytics

The systematic application of predictive modelling using inferential statistics to existing HR people-related data in order to inform judgement(s) about possible causal factors driving key HR related performance indicators.

Predictive HR analytics relies completely on good data; we cannot look for patterns in data when the available data is limited and sketchy. Thus the success of HR analytics is completely reliant on the availability of good people-related information. Once we have sufficient HR-related data, one of the biggest challenges is getting that data into the right format for analysis.

HR Analytics – Maturity Model

Pointing to an analytics model made popular by Deloitte, the authors describe the four levels of HR analytics "maturity“ - in other words, the complexity of the data analytics the company uses to solve problems.

Here's how the levels are interpreted:

Level 1: Operational Reporting:

Level 1 HR analytics is defined by using data to understand and reflect on what happened in the past and maybe going further to draw conclusions as to why past events played out in the ways they did.

The fundamentals of this level of HR analytics are understanding already available data and eventually coming to an agreement as to what the data mean for the company.

Level 2: Advanced Reporting

The significant difference that separates Level 2 from Level 1 is the frequency of the data reporting. The authors define this level of reporting as proactive, routine or even automated. The top functionality at this level is simply looking at relationships between variables.

Level 3: Strategic Analytics

HR departments operating at Level 3 are at the beginning of thorough analysis. These analyses may occur in the form of developing causal models, or looking at how relationships between variables effect outcomes.

Level 4: Predictive Analytics

The highest level of the HR analytics maturity model is defined by making predictions. HR departments functioning at Level 4 are gathering data and using it not only to predict what will happen in the future, but also to plan for it. An example of Level 4 operations is "using turnover, promotion, and market data to model scenarios that help with workforce planning," the authors write.

Increasing importance of HR Analytics

Ten disruptions identified by Josh Bersin from Deloitte should be on every CHRO’s mind as they move to incorporate analytics and the cloud systems that support it:

• Shift from automation to productivity
• Acceleration of HRMS and HCM cloud solutions
• Continuous performance management
• Feedback, engagement, and analytics tools
• Reinvention of corporate learning
• The recruiting market is thriving with innovation
• The well-being market is exploding
• People analytics matures and grows
• Intelligent self-service tools
• Innovation with HR itself

Benefits of HR Analytics

HR analytics will move from an operational partner to a more strategic center of excellence. Companies are now realizing company success is built on people, and HR analytics can light the way from intangible theory-based decisions to real ROI through the following:

• Better hiring practices
• Decreased retention
• Task automation
• Process improvement
• Improved employee experience
• More productive workforce
• Improved workforce planning through informed talent development

Challenges of HR Analytics

The road to actionable HR analytics is not always easy. There are several challenges organizations need to overcome so they can reap the rewards:

Finding people with the right skillset to gather, manage, and report on the data

• Data cleansing
• Data quality
• Too much data to parse or not knowing what data is most important
• Data privacy and compliance
• Proving its worth to executive leadership
• Tying actions and insight to ROI
• Identifying the best HR technologies to keep track of the data