Data-driven decisions in software product management: case studies from startups

Abstract

During the development processes, the innovative startups companies face multiple challenges. The lack of human resources, project documentation, low level of operation history has a significant effect on product success. In these conditions to get effective decisions, Product Manager has to rely on data. This thesis is aimed to identify main resources for decision making in software startups. The literature investigation of the existing approaches to product management reveals the differences among roles carried out duties of PM in Scrum, Extreme Programming, Kanban, Scrumban. The investigation of current practices of software management allows building the requirement gathering models and defining the main patterns of product management in startups. The main results of research are a matrix and a KPI board. The first one describes the data and sources that needed to Product Manager, while the second one illustrates the main metrics displayed the product state.