A Structured Tool Landscape for Data-Driven ProductManagernent

In the era of digital transformation, data-driven product management (DDPM) plays a crucial role in optimizing decision-making across the product lifecycle. However, selecting the right software tools to support data-driven decision-making remains a significant challenge. The overwhelming variety of available solutions and the lack of structured evaluation criteria often lead to inefficient tool utilization and missed opportunities for optimization. This paper presents a systematic tool landscape that categorizes relevant software solutions for DDPM into three main areas - Data Analytics, Product Management, and Enterprise Infrastructure - facilitating more effective comparison and selection. To provide deeper insights, detailed tool profiles are introduced, describing key functionalities, integration capabilities, and practical applications for product managers. Furthermore, this research develops a set of evaluation criteria - including functional scope and usability - to assist product management departments in selecting tools that best fit their specific needs. The results highlight the heterogeneous use of tools in companies, showing that many organizations do not fully leverage the potential of available IT systems, especially for product management. By providing a structured overview and evaluation criteria, this research supports product managers in making more informed decisions regarding software selection for the implementation of DDPM use cases.