Business intelligence (BI) platforms aggregate, analyze, and visualize data to enable data-driven decision making. However, research shows high failure rates for enterprise BI implementations due to challenges across people, process, and technology dimensions. This thesis investigates best practices and common pitfalls in BI execution. The analysis reveals recurring issues around lack of strategic alignment, poor user adoption, data quality concerns, mismatched skills, and inadequate change management. To address these gaps, the thesis synthesizes a comprehensive BI implementation framework. It offers an audit tool and structured roadmap for planning, executing, and continuously improving BI solutions tailored to business contexts.