Predictive Analytics: Using your HRMS to get more out of your HR data

The larger your dataset, the more nuanced and actionable your insights become. Predictive HR analytics has evolved from a niche concept to a strategic necessity. By shifting from traditional KPIs to forward-looking metrics, HR teams can use predictive workforce analytics to plan smarter strategies for the future.

The four tiers of HR analytics

Experts categorize HR analytics into four progressive levels:

1. Descriptive analytics: Basic operational reports detailing metrics like headcount or turnover rates.
2. Diagnostic analytics: Contextual analysis using broader data sources to understand performance drivers.
3. Predictive analytics: Utilizing statistical models to forecast future trends, such as employee attrition or hiring needs.
4. Prescriptive analytics: Advanced simulations and scenario planning to guide strategic decision-making.

Predictive people analytics help HR professionals move beyond reactive measures toward proactive workforce planning and risk management.

Recommended reading: Use this comprehensive guide to boost your predictive analytics activities and get more out of your HR data

Overcoming the barriers to advanced analytics

Despite the clear benefits, many HR teams stick to the first two tiers. A major challenge is the hesitation around complex data analysis, which is often seen as something only finance or marketing teams handle. However, predictive analytics tools for HR are becoming more accessible, making it easier to overcome these barriers.

Recent studies highlight a shift in HR competencies, emphasizing the importance of data literacy alongside traditional HR skills. For instance, the 2023 HR Insights report by Macmillan Davies found that 78% of HR professionals view AI and people metrics as central to the future of HR, yet many acknowledge significant skills gaps in people analytics and data analysis.

The role of HRMS in predictive analytics

Part of the solution is to break down the structural and systemic silos that people have traditionally worked within. A greater need for cross-disciplinary collaboration is called for and again. 

Modern HRMS platforms play a key role in bringing together HR and analytics. They provide:

• Real-time data integration: Allowing for immediate insights into workforce dynamics.
• AI-driven analytics: Enhancing the accuracy of predictions related to turnover, recruitment, and employee engagement.
• User-friendly dashboards: Making complex data accessible to HR professionals without a background in data science.

Leaning into these tools makes it easier than ever to identify patterns and trends that inform strategic initiatives, from talent acquisition to succession planning.

Building a data-driven HR culture 

To fully realize the potential of predictive HR analytics, organizations must cultivate a culture that values data-driven decision-making. This involves:

• Cross-functional collaboration: Encouraging HR to work closely with IT and data analytics teams.
• Continuous learning: Providing training opportunities to enhance data literacy within HR. Notably, Insight222's research emphasizes the need for multi-year upskilling programs, highlighting that role-modelling by CHROs and HR leadership teams is essential for building data literacy at scale.
• Ethical considerations: Ensuring data privacy and addressing biases in predictive models.