Design and Implementation of Data Warehousing for Small and Medium Sized Enterprises (SMEs): A Cost-Effective Approach in Online Stores

The storage of historical data for reference purposes has been a fundamental practice since the advent of relational databases. Despite their importance, small and medium-sized enterprises (SMEs), particularly online merchants, often rely on rudimentary methods for data storage and retrieval, thereby limiting their ability to utilize data analytics for informed decision-making. This study aims to establish a cost-effective and low-complexity data analytics infrastructure for SMEs to facilitate their transition into data-driven organizations. It examines the effective use of data warehouses (DWs) through office automation tools and open-source software solutions. A comparative analysis of data warehouse schemas-specifically the Star Schema and Snowflake Schema-was conducted to evaluate their effectiveness in preserving data integrity and supporting analytics. A practical implementation using freeware and office automation tools was executed to validate the proposed methodology. The findings indicate that the Star Schema is more suitable for dynamic data environments requiring frequent updates, whereas the Snowflake Schema is optimal for static data contexts. The proposed infrastructure effectively supports decision-making processes, particularly for online enterprises, by maintaining data integrity and streamlining analytics. This study provides SMEs with a pragmatic and economic framework for implementing data warehousing and analytics technologies. Adoption of the proposed infrastructure enables online enterprises to enhance decision-making and transition towards a data-driven strategy, thereby sustaining competitiveness in the digital marketplace.