Visualization and Analysis of Employee Performance Data Using a Power BI-based Business Intelligence Dashboard
In the current digital and competitive era, the utilization of Business Intelligence (BI) technology has become crucial in supporting data-driven decision-making. This research aims to develop and analyze a Power BI-based Business Intelligence dashboard focused on visualizing employee performance. This study was conducted by collecting performance data from the Human Resource Information System (HRIS), which was then processed and visualized in the form of key metrics such as attendance rates, individual target achievements, productivity per division, and periodic performance evaluations. Power BI was chosen for its ability to integrate various data sources and present interactive visualizations that are easy for management to understand. The methodology used involves the ETL (Extract, Transform, Load) process, data model design, and the development of visual reports that support descriptive and comparative analysis. The results of this study indicate that the use of BI dashboards significantly helps the company in monitoring employee performance in real-time, identifying trends in productivity decline, and designing data-driven improvement strategies. In addition, this dashboard also serves as an effective communication tool between management and the HR division. Thus, the use of Power BI as a tool for visualization and performance analysis adds significant value to the strategic and data-driven management of human resources
Authors
- Eka Pandu Cynthia (ORCID: https://orcid.org/0000-0001-5918-5773)
- Aaqib Saeed (ORCID: https://orcid.org/0000-0003-1473-0322)
- Imeldawaty Gultom (ORCID: https://orcid.org/0009-0005-6784-5133)
Institutions
- Kootenay Association for Science & Technology (CA)
- Universitas Islam Negeri Sultan Syarif Kasim Riau (ID)
- Hasanuddin University (ID)
Publication Details
- Journal
- Journal of Computer Science Artificial Intelligence and Communications
- Published
- 2024-11-30
- DOI
- https://doi.org/10.64803/jocsaic.v1i2.19
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00