Data Quality in a Hybrid MDM Hub
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Data and its quality play a large role in the success of a modern organization. Master data represents the most important data objects of an organisation. Its poor quality leads to problems in the business processes which lead to overhead and loss of business. The core problem is the alignment of complex business processes to information processes in complex system environments. The goal of this study is to determine the most important factors affecting the master data quality in a specified context that is the context of MDM hybrid hub. The research was done to reach the understanding of the mentioned subjects in the general level. It was not restricted to one specific organization. The aim was to find a list of most critical factors to data quality that need to be assessed when working with MDM hybrid hub. The research had two parts which were the theoretical literature review and an empirical assessment in the form of an interview. In the literature review the relevant research was assessed and summarized to support the empirical part of the research. In the empirical part a multitude of professionals of the area of MDM were interviewed and the results were analyzed and reflected with the theory. The most important factors found were the people in the form of responsibilities and roles, the data quality governance which helps forming processes to support the business processes, the streamlining the data quality management and assessment with data quality tools and automation. The result also shows how MDM hybrid hub supports the high quality of data by addressing the factors with relevant tools. These tools help in the assignation of roles and responsibilities. It enables the related workflows which support the data quality process. It also gives tools for metadata and data dictionary management and offers tools for assessing data quality and automating its management.