Olsson, HelenaBosch, JanHelferich, AndreasPetrik, DimitriStrobel, GeroPeine, Katharina2023-07-062023-07-062023978-3-88579-728-91617-5468https://dl.gi.de/handle/20.500.12116/41810For decades, product data has been collected and used for quality assurance, for post-deployment defect detection and for informing the next generation of products. Across industry domains, and with the online domain leading the way, companies have adopted experimentation and data driven practices such as A/B testing to evaluate product performance, customer behaviors and for determining what adds value to customers. However, with the rapid changes that new digital technologies bring, companies are moving towards continuous value delivery and monetization models in which they offer their products as-a-service or offer services to complement and extend their existing products. In this transition, the traditional way of post-deployment data collection and use is no longer sufficient. While companies realize this, they experience difficulties in making the changes they need to transition towards continuous practices and new ways of working with data. As a result, companies risk wasting development efforts on functionality that have little or no customer value and they lose out on the competitive advantages that come with insights derived from continuous collection and use of data. In this paper, we explore the challenges companies experience in the transition from traditional towards continuous practices and the implications this shift has on their ways of working with data.enDigitalizationdigital transformationdata practicescontinuous practicescontinuous customer value delivery.Text/Conference Paper10.18420/icspm2023-005