Sequential pattern mining is a critical task in various domains, such as e-commerce, text mining, and e-learning, where the identification of frequent subsequences in ordered datasets is essential. The efficiency of sequential pattern mining algorithms, such as Generalized Sequential Pattern (GSP), Sequential Pattern Discovery using Equivalence classes (SPADE), and a bitmap-optimized variant of SPADE (bit-SPADE), is influenced by the underlying data storage strategies. This paper investigates the impact of different data storage strategies–the horizontal strategy used in GSP, the vertical ID-list strategy (a form of inverted list) used in SPADE, and the bitmap-based strategy used in bit-SPADE–on computation speed and memory usage. We conduct a detailed empirical analysis using synthetic datasets, varying key dataset characteristics such as dataset size, item universe size, sequence length, and itemset size variability. Our results provide valuable insights into the trade-offs between speed, memory efficiency, and scalability, offering practical guidance for selecting the most appropriate storage strategy for sequential pattern mining tasks. The findings also contribute to a deeper understanding of how dataset properties influence algorithmic performance, with implications for future research and optimization.