Using Predictive Analytics and Artificial Intelligence with SQL Databases.

EasyChair Preprint 15033

Abstract

Leveraging the vast amounts of structured and unstructured data available within organizations, this approach uses AI algorithms to predict potential stressors, burnout risks, and factors that contribute to employee dissatisfaction. SQL databases serve as the backbone for data storage, facilitating the efficient handling of real-time employee data related to work patterns, project timelines, and feedback. Predictive models analyze these datasets to identify trends, allowing management to proactively implement tailored interventions, from workload adjustments to mental health support. The study demonstrates how AI-driven insights can lead to more personalized and timely well-being initiatives, resulting in a healthier work environment, improved employee retention, and enhanced overall organizational performance in the IT sector.

Keyphrases: 1. **Employee Well-being**, 10. **Real-time Monitoring**, 11. **Productivity Metrics**, 12. **Data Privacy**, 13. **Employee Satisfaction**, 14. **Predictive Modeling**, 15. **AI-driven Insights**, 2. **Burnout Prevention**, 3. **Predictive Analytics**, 4. **Artificial Intelligence (AI)**, 5. **Work Pattern Monitoring**, 6. **SQL Databases**, 7. **IT Sector**, 8. **Workload Management**, 9. **Centralized System**

@booklet{EasyChair:15033,
  author    = {Wayzman Kolawole},
  title     = {Using Predictive Analytics and Artificial Intelligence with SQL Databases.},
  howpublished = {EasyChair Preprint 15033},
  year      = {EasyChair, 2024}}