Purpose: This study evaluates companies' constraints in market data collection and analysis for developing accurate marketing strategies. It explores how fragmented data sources, inconsistent data quality, technological limitations, and organizational challenges hinder effective data-driven decision-making. Research Design and Methodology: This research employs a qualitative approach through a systematic literature review (SLR), synthesizing insights from peer-reviewed journals, books, and credible databases. The analysis identifies common themes, challenges, and best practices in marketing strategy development related to market data management. Findings and Discussion: The study identifies key challenges, including fragmented data platforms, inconsistent data standards, limited adoption of advanced analytical technologies, and organizational silos. These constraints lead to ineffective marketing strategies due to poor data integration and limited insights. The findings emphasize the importance of adopting advanced technologies, such as artificial intelligence (AI), machine learning (ML), and cloud-based platforms, alongside strengthening cross-functional collaboration and data literacy within organizations. Implications: This study provides valuable insights for both academia and industry. For practitioners, it offers actionable strategies for overcoming data-related challenges, including investing in integrated data platforms, promoting cross-departmental collaboration, and enhancing employee analytical skills. Academically, it enriches existing literature by bridging theoretical frameworks with practical applications. Future research should focus on empirical validation and industry-specific case studies to further advance understanding in this field.
Abdul Haris (Tue,) studied this question.