人材データ & 分析 • 6 最短読了時間
欠勤データを活用して欠勤率を下げる方法
MAY 12, 2026
欠勤データは、HRが利用できる最も豊富で、最も十分に活用されていないインサイト源の一つです。以下に、それを行動へつなげる方法をご紹介します。
欠勤データが示していること
Absence data contains multiple diagnostic signals that HR rarely reads systematically. High overall absence rates signal a general wellbeing or management problem. High rates of short-term absence (one to three days) with frequent occurrences per individual signal the Bradford Factor problem: employees who are frequently absent in short bursts have a disproportionate operational impact compared to employees with the same total days absent in fewer, longer episodes. Clusters of absence on Mondays and Fridays signal presenteeism followed by avoidance, often associated with burnout or work-related stress.
分析を踏まえた対応
Once you have identified the patterns, the intervention is typically at the manager level. High absence in a specific team, particularly short-term and Monday-Friday-clustered absence, is almost always partly explainable by management quality. Require managers of high-absence teams to review the absence data with HR and identify the root causes. Implement structured return-to-work conversations after every absence, regardless of length: they are the most effective single intervention for reducing short-term absence rates because they signal that absence is noticed and that manager support is available.
