Enterprise customer data storage strategies in the IIoT context: the impact of data security and quality

Purpose The proliferation of Industrial Internet of Things technologies has driven original equipment manufacturers to seek custodianship of enterprise customers’ operational data. However, entrusting data storage to Original Equipment Manufacturers introduces significant risks, such as data breaches and reduced data quality, which may undermine the strategic value of enterprise commercial intelligence. Therefore, this study examines how enterprise customers decide between in-house and entrusted data storage to original equipment manufacturers in the Industrial Internet of Things context. Design/methodology/approach This study has developed an analytical model to delineate several strategies that enterprise customers ought to contemplate when determining whether to entrust operational data of equipment to original equipment manufacturers for storage. It incorporates variables like data security levels, data quality, breach probabilities and cost coefficients. Profit functions for both strategies are derived to assess optimal decisions under varying conditions of data value, security risks, data quality and OEM service offerings. Findings The research findings indicate that when enterprise customers have low levels of data security and data quality while storing data in-house, entrusting storage not only provides technical advantages but also offers certain data services. However, when simultaneously considering data security and data quality, the value of the data and the value of data services are not the sole determining factors in the enterprise customer’s decision-making. Enterprise data management strategies must therefore be adjusted based on data security risks and data quality. Originality/value This research integrates data quality with security in Industrial Internet of Things storage decisions, a previously underexplored area. It introduces a novel model for evaluating entrusted storage feasibility, extending economic theories by incorporating data quality as a key variable. The study offers actionable insights for enterprise customers and original equipment manufacturers on optimizing data management strategies.