Bridging the Gap Between Software Product Management and Software Development Product Data Practices

Abstract

Software product management and software development are both key functions for creating and managing software products. Still, these functions are largely seen as separate and most frameworks do not include each other, especially in terms of product data. Software products generate vast amounts of complex data that is largely untapped. Effective management and use of product data could create efficiency and improve decision making leading to higher value creation and better products.

This thesis is a literature review aiming to find out the current product data practices in software product management and software development and how the functions could be better aligned in terms of product data. The research finds that in general the data practices in software product management are high level, focusing on decision making and actionable insights, while software development focuses on big data and testing. Both functions suffer from the same challenges related to the collection, management, and especially the effective use of product data. Various emerging practices, mainly machine learning, are proposed to solve these issues and overall improve the management and use of product data. Frameworks integrating the two functions and involving clear product data practices are required as well as data tools that effectively visualise data and create actionable insights.