People Analytics

In people analytics, an organization's management uses data analysis on employee and job candidate issues to understand their impact on business goals. This approach allows the organization to collect and utilize employee data for making evidence-based business decisions with the hope of increasing effectiveness. 

There are four primary types of people analytics:

• Predictive Analytics: Organizations use predictive analytics to categorize past and present data to gain insights. They then use models to predict future outcomes.
• Descriptive Analytics: This is the most commonly used type of people analytics. Descriptive analytics, sometimes called decision analytics, involves analyzing historical data to understand past events. It is not used for future predictions.
• Diagnostic Analytics: Diagnostic analytics builds on descriptive analytics by providing explanations for observed correlations, trends, and data anomalies.
• Prescriptive Analytics: Prescriptive analytics translate predictive insights into actionable strategies and decision-making options.

People analytics is often used interchangeably with HR analytics, but they differ. HR analytics focuses solely on employee data within the HR department, while people analytics can include data from marketing, customers, finance, and other sources. 

Examples of data sources used in people analytics include:

• Demographic data
• Performance management
• CRM data
• Job architecture
• Learning management
• Recruiting
• Employee survey data
• Wellness and well-being
• Absence data
• Mentoring
• Talent development
• Exit interviews
• Succession planning
• Turnover rate
• Retention rate

People Analytics Examples

People analytics can evaluate the effectiveness of the HR department's recruiting, hiring, and training. For example, suppose a mid-sized technology business experiences high turnover rates among its software engineers. This turnover leads to increased hiring costs and disrupted project timelines.

To address these issues, the HR team could work to improve:

• The quality of new hires
• Training and employee development
• Employee satisfaction

First, the HR team collects data from past recruitment cycles, including candidate resumes, hiring sources, training records, and employee satisfaction surveys. HR analysts then use software to identify correlations and patterns.

The HR team may discover that candidates with specific job experiences perform better and stay longer. In this case, they should focus on hiring people with these skills or providing necessary training.