There is a shift in the manufacturing industries in which original equipment manufacturers (OEM) are gaining increasingly large portion of their revenue from services rather than the manufacturing of goods. This change is called servitisation. Additionally, the advancements in information technology are opening new possibilities and opportunities, such as in how data can be processed, analysed and used to create data-driven applications to support the business functions. The possibilities are, however, still largely unexploited especially in the field of maintenance services. The data-driven prognostics could not only enhance the existing maintenance activities, but also create new ways of partnership and service development between the OEMs and their clients. This could induce further growth and increase in the servitisation level. However, there is lack of insight of how the methods could be applied to practice; especially case studies are few in quantity. Hence, this study aims to increase understanding of the practical application of the data to support maintenance service business. This study examines the application of data-driven methods, mainly machine learning, to aid valve maintenance business of a service providing OEM. The aim is to create a data-driven system to forecast failures in devices and generate automated service recommendations. The forecasting was based on idea that the failures would induce a detectable pattern in the measured data prior a failure. The chosen machine learning method, the neural networks, excel in this kind of task and hence can predict failures. The study is conducted in practical setting as a case study with real data. Various systems and processes were examined, and data was extracted for analysis. With this data several models for prediction were built. However, the accuracy of these was ultimately deemed insufficient for generation of service recommendations and hence all the set goals were not fully reached. As the greatest contributing factors for the poor performance of the forecasts, the data itself and the operations related to it were identified. The data was hard to access and lacking both in quality and quantity as it is recorded, stored and managed with day-to-day operations in mind. As result, we found that significant portions of data were deleted or were recorded with accuracy insufficient for this research. However, through the analysis of these factors several concrete points of development emerged. The outcome of this study also confirms the inherent challenges regarding service partnering and intercompany data-transfer presented in literature. A need for standardised and light-weight legal frameworks and methods of data sharing was identified. Without these, the potential may not be fully realisable in practice and hence more case practically oriented studies on the subject are required. To conclude, the OEM had too optimistic view of the availability, quality and quantity of data, which resulted in an attempt, which did not reach all the set goals. On the other hand, the academic literature shows that there is great potential in these methods. Data refined into wisdom which may support decisions and actions can facilitate value generation in services. The findings encourage OEM to improve the collection, storage and management of data and other organisations to carefully evaluate whether their capabilities are sufficient.