Data governance in big data : How to improve data quality in a decentralized organization

Abstract: The use of internet has increased the amount of data available and gathered. Companies are investing in big data analytics to gain insights from this data. However, the value of the analysis and decisions made based on it, is dependent on the quality ofthe underlying data. For this reason, data quality has become a prevalent issue for organizations. Additionally, failures in data quality management are often due to organizational aspects. Due to the growing popularity of decentralized organizational structures, there is a need to understand how a decentralized organization can improve data quality. This thesis conducts a qualitative single case study of an organization currently shifting towards becoming data driven and struggling with maintaining data quality within the logistics industry. The purpose of the thesis is to answer the questions: • RQ1: What is data quality in the context of logistics data? • RQ2: What are the obstacles for improving data quality in a decentralized organization? • RQ3: How can these obstacles be overcome? Several data quality dimensions were identified and categorized as critical issues,issues and non-issues. From the gathered data the dimensions completeness, accuracy and consistency were found to be critical issues of data quality. The three most prevalent obstacles for improving data quality were data ownership, data standardization and understanding the importance of data quality. To overcome these obstacles the most important measures are creating data ownership structures, implementing data quality practices and changing the mindset of the employees to a data driven mindset. The generalizability of a single case study is low. However, there are insights and trends which can be derived from the results of this thesis and used for further studies and companies undergoing similar transformations.