In today's digital landscape, data is akin to oil, pivotal for decision-making and innovation, especially in retail with its vast customer data. However, accumulating data presents challenges, notably in costeffective management. This thesis explores strategies for retail firms to optimize data management without sacrificing the data’s potential benefits. Drawing insights from interviews with five retail companies and implementing a product recommendation model. The study reveals that while storage costs are perceived as low, the prevalent "store it all" approach results in storing vast amounts of unused data, incurring unnecessary expenses. Furthermore, compliance with GDPR primarily shapes companies’ data retention policies, with some companies opting for automated deletion or anonymization to align with regulations. However, inconsistencies exist in practice regarding data storage intentions. The thesis culminates in a strategic framework to enhance data management. A four-step framework is proposed: assessing data lifespan, implementing archiving routines, anonymizing and aggregating data, and evaluating cost versus utility. The research underscores the need for deletion strategies to prevent data overload and maintain cost-effectiveness. This thesis contributes to understanding data value and offers practical guidance for retail firms to navigate data management efficiently while complying with regulations like GDPR. Future research could delve into the long-term impacts of retention policies on business operations, assessing data deletion or archiving over extended periods. Longitudinal studies with company data access would enrich this exploration.