Data warehouses (DW) are a key component of business intelligence and decision-making. In this paper, we present an approach that combines Grounded Theory and System Dynamics to develop causal loop diagrams/models for data warehouse quality and processes. We used the top 51 data warehousing academic papers to arrive at concepts and critical success factors. A simple data warehouse quality causal model and a Data Warehouse Project Initialization Loop Analysis, Data Source Availability & Monitoring Loop Analysis and Data Model Quality and DBMS Quality Analysis models were developed. Visualization of the cause-effect loops and how data warehouse variables are interrelated provide a clear understanding of DW process. Key findings include data quality and data model quality that are more important than DBMS quality for ensuring data warehouse quality, and the number of data entry errors and the level of data complexity can be major detriments to DW quality.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications