The Five Purposes of Value Modeling
Paper in proceeding, 2020
Data driven and experimental development practices provide effective means for companies to adopt a customer and market-centric way-of-working. In online companies, controlled experimentation is the primary technique to measure how customers respond to variants of deployed software. Over the recent years, and due to increasing connectivity and data collection from products in the field, these practices are being adopted also in software-intensive embedded systems companies. In these companies, experiments are run on selected instances of the system or as comparisons of previously computed data to ensure value delivery to customers, improve quality and explore new value propositions. However, to utilize the benefits of data- driven and experimental development practices, companies need to define what value factors to optimize for. For highly complex embedded systems with thousands of parameters, and with people at different levels in the organization having different opinions about the value of features, this is a challenging task. In this paper, we report on longitudinal multi-case study research in which we explore value modeling as a technique to help people in development, in product management and on the business level to align interests and agree on value factors. Based on this work, we identify five purposes of value modeling and how this technique helps accelerate critical activities in an organization. The contribution of this paper is three-fold. First, we provide empirical evidence for how value modeling is an effective technique to help companies define what to optimize for. Second, we identify five purposes of value modeling. Third, we identify the key challenges that the case companies experience when applying value modeling.
data driven development