Over the past decade, the Service Oriented Architecture (SOA) approach has become a preferable way of building information systems. This is largely due to its ability to enable rapid changes in systems by recombining and scaling existing services. However, the more complex the SOA becomes, the more likely are data quality (DQ) issues to be encountered. Despite numerous SOA projects failing due to data quality problems, many organizations and individuals are still ignoring the importance and necessity of data quality. In spite of the large number of studies that have been done on SOA, findings in literature and practice have shown that very little has been investigated about the DQ aspect. Most of the DQ evaluation approaches to date do not consider the services’ context and semantic accuracy of the data. The objective of this research is to investigate challenges in data quality within service composition. More specifically, the goal is to create a method of detecting and analysing semantically inaccurate data within a specific Service-oriented context. In order to reach the given objective, a DQ methodology was proposed which suggests techniques and methods for profiling and assessing data. The proposed approach was developed by following the Data Quality Management (DQM) model. Additionally, to conduct this project, Design Science (DS) oriented methodology for conducting research, which focuses on the development of artifacts, was used. The application and usability of the proposed DQ approach was demonstrated in a home automation system – a specific type of SOA environment. The contribution of this research is that application of the approach allows practitioners to detect poor data within SOA environment, preventing damages and reducing expenses. It also provides researchers with methods that will serve as a foundation for research in improving data quality and the decision making in SOA field.