Organizations aiming to use modern data analytics face several severe challenges that were not evident or visible beforehand. In this paper we study a Finnish manufacturing company’s data empowerment and information management processes and practices, particularly from the viewpoint of data utilization. We answer to questions 1) what challenges the case organization faces and, more generally, 2) what are the lessons learned from advancing such endeavour. We have a case study of a Finnish globally operating manufacturing company which was carried out in 2016. The empirical data is consisted of group discussions, relevant data sets acquired from the case company’s information systems, and lastly, 12 thematic interviews of the key actors in the company in relation to service development. Interview themes were related to the following issues: information needs, managerial practices, knowledge concepts, information technology and information systems, and knowledge and network dynamics. We analysed qualitative data by using Choo’s (2002) information management model as a framework. Based on the analysis we point out challenges related to information needs, information gathering, sharing and utilization. Afterwards we analyse the requirements data and information empowerment faces and summarize our lessons learned how to create value and actionable knowledge from data. Understanding data-based value creation helps organizations to enhance its decision-making and knowledge processes. However, most research focuses either on knowledge and its management, or data and information quality issues. Quite rarely the value chain from data to knowledge and its utilization are illustrated. In this paper we present the data-based value creation pipeline and its concrete challenges and solutions. Our focus is on data-based value creation in general. Consequently, our study illustrates practical challenges in relation to research literature from several disciplines increasing our awareness of intertwined nature of issues and path dependency between details. This understanding and lessons learned also open up new research avenues. The approach provides valuable insights for managing service development and decision-making and creates understanding on data-based value creation. Achieved understanding provides meaningful knowledge for organizations utilizing or having plans to utilize, for example, data analytic methods in their businesses.