2.1 The Business Problem

Workplace absenteeism — employees missing scheduled work — is one of the most persistent operational challenges organizations face. In the United States alone, unplanned absences cost employers an estimated $3,600 per hourly worker and $2,650 per salaried worker annually, according to the U.S. Bureau of Labor Statistics. Beyond direct costs, absenteeism disrupts team coordination, delays deliverables, and places additional burden on the employees who do show up (Cucchiella et al. 2014).

For managers, the challenge is not simply that people miss work — some absence is inevitable and legitimate. The challenge is understanding why absenteeism occurs, who is most at risk, and what the organization can do about it. These are exactly the kinds of questions that Business Intelligence is designed to answer.

Our case study centers on a courier company in Brazil that collected detailed records on employee absences over a three-year period, from July 2007 through July 2010. The company tracked 36 employees, recording not just when and how long each person was absent, but also a rich set of contextual information: the reason for each absence, the employee’s commute, workload, health indicators, and personal demographics (Martiniano et al. 2018).

The company’s core questions were:

• What drives absenteeism? Are certain reasons, seasons, or employee characteristics associated with higher absence rates?
• Can we predict it? Given what we know about an employee and their work context, can we anticipate who is likely to be absent and when?
• Can we intervene? If we identify the key drivers, can we design policies or support systems that reduce unplanned absences?

These questions will frame our work throughout the book. Each analytical technique we learn — data cleaning, visualization, regression modeling, anomaly detection, dashboarding — will be applied to this dataset to help answer them.