The Customer is a US-based machinery maintenance entity with 25,000+ employees.
With the goal to improve HR and performance management, the Customer wanted a BI solution that was to:
- aggregate employees’ personal and work-related information
- analyze the headcount, FTEs and hours paid
- track employees’ working time
- monitor task performance
In the course of the project, Pine Analytical’s BI implementation team delivered the following:
- Data warehouse on Microsoft SQL Server
- ETL using Python
- Data cleaning
- Analytical cube with 18 dimensions and 26 measures
- Integrated analytical reports based on Microsoft Power BI
As the BI system was to bring together data from 4 sources, it was tuned to discover any discrepancies (such as typos, mismatches, different abbreviations, etc.) between them.
Since the Customer wanted to improve HR management processes, Pine Analytical developed specific reports with the following information:
- Employee’s first and last name, as well as ID
- Contact information (such as address, phone number, etc.)
- Job position
- Contract type
- Hire date
- Contract termination date
- Full-time equivalents
- Hourly rate per job position
- Paid hours
- Not paid hours
The reports allowed the Customer to watch over contract termination dates and organize relevant HR procedures on time. Additionally, the Customer got an overview of personnel costs and was able to use this information for budgeting.
To allow the Customer to control the headcount, Pine Analytical implemented the dashboards that show:
- The number of employees per job position
- Actual headcount
Two modules were designed for monitoring employees’ performance: the one was to track employees’ working time and the other their task fulfillment.
The delivered BI solution allowed the Customer to make HR and performance management more controllable thanks to monitoring task fulfillment, tracking employees’ working time and watching over important milestones (for example, a contract termination date). Besides, the system ensured data integrity by spotting discrepancies in it.
Technologies and Tools
Microsoft SQL Server (DWH), Python (ETL), Microsoft Power BI (Reporting).