Employee experience, data, and decision-making

Covered within this video are two interconnected reasons why organisations adopt people technologies: improving the employee experience through self-service functionality, and enabling better workforce decision-making through data and analytics. The video addresses Assessment Criterion 1.2 of the FREE02 People Professional Technologies unit and examines both themes as strategic drivers of technology adoption rather than purely administrative conveniences. Self-service is discussed in relation to specific transactional activities — including holiday requests, payslip access, benefits enrolment, and training bookings — alongside consideration of which employee groups gain the most from this kind of direct system access, such as remote workers and those spread across multiple sites.

The video also treats the employee experience as a broader concept encompassing every technology touchpoint across the full employee lifecycle, from initial job application through to departure. It addresses how the usability and accessibility of people systems shape employee perceptions of their employer, and how poorly designed or fragmented systems can undermine engagement even when other efforts are in place. This framing positions technology not merely as a functional tool but as a contributor to how an organisation is experienced day to day.

People analytics receives substantial attention, with the video covering the shift from intuition-based to evidence-based decision-making in the people profession. Specific workforce metrics discussed include headcount, turnover rates, absence levels, recruitment source effectiveness, early attrition prediction, and the relationship between learning and development investment and performance outcomes. The video also examines how data-backed evidence strengthens the credibility of the people function when building business cases for initiatives or presenting recommendations to organisational leaders, and how programmes such as wellbeing initiatives can be assessed against measurable indicators such as absence data and engagement scores.