HR Analytics Basics

HR analytics simply refers to the application of analytics and statistics for

• identifying HR data patterns
• deduce perspectives on those patterns, to
• locate opportunities in existing HR operations
• optimize existing HR operations
• find errors in the workflows

HR is a precious harbinger of employee data. Any information about any employee is present in the HR database. Motivated employees can have huge impact in the business and analysis of employee data can do wonders by analyzing the data and identifying insights for, enriching and motivating work environment.

HR analytics can make HR related decisions more analytical based and data intensive. HR analytics can access employee data like

• work history
• attendance record
• job completion statistics
• error-free deliverance
• attitude towards job and seniors, etc

With these analyses of data, important human resource issues can be resolved and more productivity could be brought in.

Issues usually includes

• office politics
• cold wars between departments
• ego clashes
• other psychological factors disrupting productivity

HR analytics can also make compensation more rewarding and data-driven, as it is not just based on experience and eligibility and finer details of the employees can be considered thus, removing inequality in compensation at the grass root levels.

Metrics in HR Analytics

HR analytics is mostly focused on few important metrics and various others can also be considered, as per business need.

Few crucial metrics are

• resignation rate
• performance appraisal involvement rate
• time taken to recruit to hire
• Rate of resignation–number of employees resigning in a period
• Time to recruit–time between closing vacant position to absorption of into workforce.
• Rate of Staff turnover–Recruits leaving after a year/ five years/ etc
• Workforce diversity – percentages of women/men/religious groups/ethnicities

Challenges for HR Analytics

• Data flood – Flood of data makes it tougher to use it appropriately. Identify relevant data. Metrics should be properly defined and categorized and define questions to solve with data.
• Data quality – Focus on data quality by stressing data integrity and security. Data used, comes from multiple departments and usually leads to issues. Some data is ignored/dropped/lost/cannot be joined, resulting in inadequate analysis.
• Analytical skills – HR professionals are usually not conversant with analytical skills.
• Management support for HR analytics may be short of – Management support is needed for implementation and taking appropriate decisions based on data analysis and insights discovered.

HR Analytics Process

HR Analytics involves

• Identifying business challenge like laggard performance in a branch
• Developing Hypothesis like branch performance and employee motivation
• Identifying relevant data like employee and financial performance data of branch
• Analyzing collected data
• Validating findings
• Putting insights from analysis in business decision making like branch timings, leave policies, etc
• Continuing the process again, till business goal for the branch, is achieved