Research in the data warehousing domain has primarily focused on issues related to extracting, transforming, and loading data from source systems to build integrated data warehouses and maintaining views. However, research on data utilization within data warehouses remains limited. Moreover, although various commercial OLAP tools that support the utilization of data warehouse data have been introduced, many companies seeking to build data warehouses face considerable difficulties in selecting OLAP tools appropriate for their business purposes. Therefore, this study presents specific evaluation criteria for selecting OLAP tools and verifies the validity of these criteria by examining the results of two rounds of evaluation conducted on representative OLAP tools available in the domestic market. Additionally, the implications of the verification results are examined. The first verification was conducted on three representative ROLAP tools (Brio Enterprise, InfoBeacon, and DSS Agent), and the second verification was conducted on the MOLAP tool with the highest market share (EssBASE) and the ROLAP tool selected from the first verification (DSS Agent). The first verification results confirmed that functionalities such as various advanced statistical analyses, provision of APIs for connecting to client development tools, support for user-defined functions built into the tool, aggregation functions, multi-user access management on the client side, error handling and debugging, Korean language support, and metadata management were inadequate. The second verification results showed that the ROLAP tool had achieved more functional improvements compared to the first verification, but aggregation functions and Korean language support remained insufficient, and the MOLAP tool also had problems with aggregation functions, Korean language support, and compatibility with other metadata. In summary, the verification results indicate that ROLAP tools may be considered for enterprise-wide data warehouse implementations processing large volumes of data, while MOLAP tools may be considered for tasks requiring diverse analyses or data mart-type applications. The evaluation criteria presented in this study can serve as guidelines for selecting OLAP tools.