Data governance: Banking Sector case

Data governance has been on a rising trend in enterprise data management in recent years. Although most of the large scale companies make significant investments as technological and human resources, they cannot have end-to-end manageable data which is at the desired level of quality. It is necessary to move out of the traditional approach to growing data which is getting more and more complicated. When the traditional data management approach is observed, it is seen that there are missing points. In the researches conducted, it is seen that there is a need to implement data governance to the firms of the institutional scale. The aim of this thesis is to show the advantages of data governance to large scale companies with complex data structure and to explain what should be done in order to ensure that the data used in decision support systems are obtained in a healthier and more accurate way in the long term. Within the scope of this thesis, after the general definitions related to data governance, the organization of data in the banking sector (identification of critical data elements, data domain selection), creating a data governance framework (data governance organization, policy, procedures and standards), data governance implementation (establishment of data dictionary, identification of data lineage, establishment of data quality structure, data profiling), and data governance measurement (defining data governance metrics, creation of data governance scorecard) have been made.