What is this meeting about?
In this one-hour online session, we explore how HR teams can move from reactive to proactive decision-making using predictive analytics.
Listen to Kevin O'Driscoll from Highberg on this important topic. He will be addressing key challenges of using HR data for predictions, introduce core methodologies and use cases (such as absenteeism and attrition), and showcase how predictive models work in practice - including automated retraining and identifying key drivers.
We'll close with a discussion on how organizations can translate insights into targeted interventions and more effective people strategies.
Presentation:
Stream:
Key take-aways:
Organizations are increasingly realizing that traditional HR reporting—focused on what has already happened—is no longer sufficient in a fast-changing environment. This session highlighted the shift toward predictive analytics, where HR moves into a more strategic role by anticipating workforce trends and enabling earlier, better-informed decisions. However, the transition is not purely technical—it requires strong data foundations, organizational readiness, and a clear understanding of how insights translate into action.
A key message is that predictive analytics is not a one-off initiative but a maturity journey. Success depends on combining high-quality data, transparent modeling, and a culture that embraces forward-looking insights. Organizations that start small, focus on a clear use case, and build trust step by step are far more likely to succeed than those aiming for immediate large-scale transformation.
- Data quality is the true starting point
Predictive analytics cannot succeed without reliable, consistent, and well-integrated HR data. Issues such as retroactive changes, inconsistent employee identifiers, and fragmented systems must be addressed first. Investing in data quality creates a stable foundation that ensures any future insights are trustworthy and actionable. - Think in terms of maturity, not tools
Moving from reactive to predictive HR follows stages: reacting, noticing, anticipating, and intervening. Most organizations are still mid-journey, meaning the real challenge lies in processes, governance, and adoption—not just technology. Treat predictive analytics as a capability that evolves over time, not a quick implementation. - External data is a hidden advantage
Incorporating external sources such as public health data or labor market indicators can significantly improve prediction quality. These datasets often explain trends that internal HR data alone cannot, offering a broader and more accurate understanding of workforce dynamics. - Insights only matter if they drive action
Many organizations can generate dashboards and forecasts, but struggle to act on them. Closing the gap between analytics and decision-making requires clear ownership, defined interventions, and alignment with business objectives such as cost, continuity, or workforce stability.
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