The increasing competitiveness of the textile industry necessitates the adoption of advanced technologies to optimize processes and enhance decision-making. In this context, Big Data emerges as a strategic tool for improving product development by enabling the analysis of large data volumes and anticipating market trends. This study introduces the ESPOS (Scalable, Simultaneous, Standardized, Ordered, Secure) model, designed to facilitate Big Data implementation in the textile sector. A systematic literature review, following the Methodi Ordinatio approach, was conducted to identify relevant studies. The analysis highlights the key benefits, demands, and barriers associated with Big Data adoption, emphasizing the need for a robust technological infrastructure, standardized data management, and professional training. The ESPOS model is structured for progressive implementation across three levels—basic, intermediate, and advanced—allowing companies to gradually enhance their analytical maturity. Its application leads to cost reduction, improved operational efficiency, and enhanced trend forecasting. Future research should validate the ESPOS model through case studies to assess its practical effectiveness and identify opportunities for improvement. The adoption of this model positions the textile industry at the forefront of Industry 4.0, fostering innovation and competitiveness.