An Integrated Multi-Dimensional Data Warehouse for University Payroll Management

Authors

  • journal manager

Keywords:

Data warehouse, University payroll management, Payroll system, Talend open source data integration

Abstract

Abstract

Payroll is traditionally the most data driven function relating to employee information. The challenge for many large organizations lies in the fragmented nature of payroll. Payroll data generally sits siloed or isolated in a multitude of different local systems. The implication of this is very frustrating: Given the time- consuming manual labour involve in aggregating the payroll data. The lack of integrated consolidated payroll data in a single location as well as the functional arrangement of these data makes it difficult to access timely information and carry out effective analysis to support management decision. This work presents an integrated multi-dimensional data model for a University Payroll System. The model integrates all disparate silos of payroll data sources into a single location for ease and speed of reporting and efficient analysis. The model was simulated and tested using Talend Open Source Data Integration tool while MySQL was used for data storage. Reports were developed from the integrated multi-dimensional data warehouse using a reporting tool, and performance was evaluated and compared relative to same reports generated directly from the various disparate payroll systems. The time it takes to obtain reports from the integrated data warehouse was a lot faster and lot easier than having to obtain same reports from each payroll system and manually aggregating the reports from each of the multiple disparate payroll systems. One obvious reason is that all data in are now integrated in a single location instead of wasting time traversing various payroll data sources. In conclusion, the integrated multi-dimensional data warehouse for payroll system will improve information access, reduce drastically the time to reconcile and obtain reports and provide for far more efficient information analysis for decision makers in the University to make effective strategic decisions based on facts

Published

2021-01-18