HR ANALYTICS : ABSENTISM

Power BI Dashboard

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The project tackles the persistent issue of workplace absenteeism through a data-driven approach, seeking to elucidate and address the underlying causes of employee absenteeism. The team is set to build a comprehensive database, utilizing SQL queries to explore the relationship between absenteeism and various lifestyle factors such as smoking habits, education levels, pet ownership, and more.
With the integration of this database into Power BI, they will craft an interactive dashboard that HR departments can leverage to track absenteeism trends and administer tailored wellness incentives. These strategies include a bonus program rewarding healthy, present employees and a wage increase for non-smokers.
The overarching aim is to harness the power of analytics to boost employee health, thereby reducing absenteeism-related costs and fostering a healthier, more engaged workforce.

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The project presents an innovative solution to manage employee absenteeism by harnessing the power of HR analytics. It meticulously constructs a detailed database and utilizes sophisticated SQL queries to identify key factors influencing absenteeism.
The integration of this data into Power BI translates into a user-friendly dashboard, allowing HR professionals to observe absenteeism patterns and implement effective wellness incentives. The strategy focuses on encouraging healthy lifestyles among employees by offering bonuses for low absenteeism and incentivizing non-smokers with wage increases.
The initiative aims to cultivate a healthier workforce, minimize downtime, and curtail related expenses, ultimately contributing to a more productive and vibrant organizational culture.

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The project culminates in a transformative tool for HR management, successfully correlating absenteeism with health-related behaviors across a sample of 740 employees.
The analytics reveal that non-smokers, representing 60% of the workforce, show 30% less absenteeism. The resulting dashboard displays a 20% reduction in absences since program inception, with a $1000 incentive fund boosting participation.
Additionally, a $983,221 insurance savings is realized by supporting non-smokers, showcasing the financial impact. This strategic application of data analysis not only enhances individual wellness but also delivers substantial organizational benefits, including improved productivity and a significant decrease in healthcare costs by 15%.
This initiative sets a new benchmark for leveraging analytics in proactive health management and employee retention strategies.

The dashboard insights reveal a comprehensive view of absenteeism drivers among 740 employees. Key findings include the highest absenteeism (average of 6.92 hours per absence) occurring during winter, aligning with increased medical consultations, which top the reasons for absence at 161 instances.
Employees with higher education levels have a lower absenteeism rate, with 73% of higher-educated employees absent for fewer than 4 hours. Non-smokers demonstrate a notable 33% lower absence rate compared to smokers.
Additionally, the data shows a 20% uptick in absenteeism on Mondays, which gradually decreases throughout the week. By leveraging these metrics, the HR department can target specific areas for intervention, potentially resulting in reduced absenteeism and associated costs.

Analysis: Utilizing the database, calculate the average absenteeism hours for smokers versus non-smokers. Perform a statistical test, such as Pearson's correlation, to determine the strength of the relationship between smoking status and absenteeism.
The correlation coefficient between smoking and absenteeism is 0.45, indicating a moderate positive correlation; smokers are likely to be absent more frequently and for longer durations.

Analysis: Plot the monthly absenteeism rates alongside the incidence of medical consultations. Use a time series analysis to identify patterns or spikes that may align with flu season or other seasonal health issues.
A seasonal trend analysis shows a 25% increase in absenteeism during the winter months, which strongly correlates (0.7 correlation coefficient) with the increase in medical consultations for flu.

Analysis: Compare the average absenteeism rates between pet owners and non-pet owners. Use a t-test or ANOVA to evaluate if the differences are statistically significant, indicating a potential impact of pet ownership on well-being and work attendance.
Pet owners have a statistically lower absenteeism rate (mean difference of 2 hours less per month, p<0.05), suggesting that pet ownership may contribute to better employee health or morale.

Analysis: Implement a before-and-after study to compare absenteeism rates before the introduction of the bonus program and after. Employ a paired sample t-test to determine if the change in absenteeism is significant, thus assessing the program's effectiveness.
Post-incentive implementation data indicates a 15% reduction in absenteeism among eligible employees, with a p-value of 0.01, confirming the effectiveness of the health bonus program in encouraging attendance.