Antecedents of Data-Driven Product Management – Data Taxonomy and Cluster & Software Categories

Product management (PM) spans the entire product lifecycle and requires effective navigation through evolving customer demands, volatile markets, and uncertain competitive landscapes. In the face of these challenges, industrial companies must adopt new strategies. Data-driven product management has emerged as a critical solution, enabling companies to transform vast amounts of data, such as customer feedback and usage data, into actionable insights for informed decision-making. However, a clear framework for identifying and categorizing relevant data sources is still lacking. This paper develops a comprehensive taxonomy to address this gap by systematically characterizing data types relevant to data-driven product management. Using the empirical-to-conceptual and conceptual-to-empirical approach based on Nickerson Et Al., we identified and classified 47 distinct data types, grouped into 10 data clusters through cluster analysis. These clusters provide a structured and actionable overview of the data landscape, making it easier for organizations to operationalize data-driven strategies. We also analyzed typical software categories, such as ERP systems, where these data types are stored and managed. Our findings are critical for organizations seeking to improve their data-driven product management capabilities by providing a systematic framework for structuring data and an understanding of crucial software tools. This research fills a significant gap in academic literature and practical implementation, providing organizations with a clear path to use data in product management effectively.