Design Principles and Process Model for Planning Data Analytics in Product Management

Product management in manufacturing companies is undergoing a significant transformation. While decisions were traditionally driven by experience and intuition, the increasing availability and variety of data is creating new possibilities for data-driven decision-making. This shift is particularly important in the planning of data analytics to support product management tasks. Despite the growing recognition of the potential benefits of data analytics, a systematic approach to planning its use in product management decision-making is still lacking. This paper introduces 21 design principles to guide the planning of data analytics in product management. These principles were developed through a systematic literature review, complemented by five pilot projects with industrial companies and a focus group meeting with product management experts, focusing on aligning analytics efforts with the needs of product management tasks. The principles offer concrete guidance to ensure actionable and targeted data analytics. Building on these principles, a process model was derived to provide structured guidance for systematically planning data analytics initiatives in product management. The findings provide valuable insights for organizations looking to incorporate data analytics into their product management practices, offering guidelines for using data to improve decision-making.