In 2025, organizations rely heavily on people analytics, HR analytics, HR data analytics, workforce analytics, talent analytics, and HR metrics and analytics to understand their employees and make better decisions. Companies are investing in people analytics tools, people analytics software, and HR analytics software because the importance of HR analytics has never been higher. These tools help leaders interpret employee data, improve performance, support inclusion, and create stronger workforce strategies.
But…
What are the four levels of HR analytics?
To use these technologies effectively, HR professionals must understand what HR analytics is and how the four levels of HR analytics guide the journey from basic reporting to advanced decision-making. These levels also explain how predictive HR analytics and workforce analytics support long-term talent planning. Below is a complete explanation of each level and how HR teams can use HR data and analytics to build a smarter, more equitable workplace.
Level 1: Descriptive Analytics
Descriptive analytics focuses on the question: What happened. At this stage, HR teams look at historical information such as attendance data, turnover rates, recruitment reports, and performance scores. This is the foundation of people analytics and HR analytics because it provides a clear snapshot of workforce behavior.
Most organizations use simple people analytics tools or dashboards within their HRIS to track key HR metrics and analytics. While this level does not explain the reasons behind the trends, it helps companies understand their current workforce reality and maintain consistency in reporting. Descriptive analytics is also important for compliance and audits, which is why almost all HR teams begin here.
Level 2: Diagnostic Analytics
Diagnostic analytics goes deeper and answers the question: Why did it happen. This level helps HR professionals uncover the root causes behind workforce challenges. If turnover increases, diagnostic analytics investigates why employees are resigning. If engagement drops, it identifies which teams or leaders are the most affected.
This level requires stronger HR data analytics practices and often includes more advanced HR analytics tools and workforce analytics platforms. HR can segment data by department, experience level, tenure, gender, or performance category to understand deeper patterns. Diagnostic analytics reveals what is really driving behaviour and provides the clarity needed for strong HR interventions.
Level 3: Predictive Analytics
Predictive analytics focuses on what will happen next. This is where predictive HR analytics becomes valuable. HR teams use statistical models and machine learning to forecast outcomes related to hiring, turnover, performance, and learning.
Examples include:
• predicting employees most likely to leave
• forecasting which roles will face future skill shortages
• identifying the probability of high performance among new hires
• estimating the impact of engagement levels on productivity
Predictive analytics requires advanced people analytics software or HR analytics software that supports modeling and forecasting. Companies that reach this level move from reactive to proactive HR strategy. The importance of HR analytics becomes clear as leaders begin relying on these predictions for long-term talent planning, budgeting, and organizational design.
Level 4: Prescriptive Analytics
Prescriptive analytics focuses on what HR should do next. This is the highest level of analytics maturity and combines all previous insights into actionable recommendations. Rather than only predicting outcomes, prescriptive analytics suggests the best actions to take.
Examples include:
• recommending personalized learning paths
• suggesting targeted retention strategies
• optimizing workforce schedules
• recommending ideal hiring sources
• identifying the best interventions to improve engagement
Organizations at this level usually use integrated HR data and analytics systems that combine people analytics, talent analytics, and artificial intelligence. Prescriptive analytics helps HR teams design more effective strategies and build a high-performance workforce.
Why These Four Levels Matter
Understanding the four levels of HR analytics helps organizations strengthen their people strategy, reduce bias, and improve decision-making. As companies adopt more sophisticated HR analytics tools, people analytics software, and workforce analytics systems, HR becomes a strategic partner in business outcomes.
The future of HR will depend heavily on strong data skills and the ability to interpret HR metrics and analytics. Teams that invest in HR data analytics today will be better prepared to handle rapid changes in workforce expectations, technology, and talent demands.
Mastering these four levels is the key to transforming HR into a data-driven, forward-thinking function.
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