Integration of IBM Knowledge Catalog into a highly complex analytical workflow and comparison with Data Governance tools for enterprise data management

In an era increasingly driven by data, a robust Data Governance framework is paramount to ensuring data quality, regulatory compliance, and strategic alignment with business objectives. This is particularly critical in data-intensive sectors such as the fashion industry. This thesis investigates the integration of IBM Knowledge Catalog (IKC) into a sophisticated analytical workflow, providing a comparative analysis with other Data Governance tools for enterprise data management. The study explores Business Intelligence (BI) and ETL (Extract, Transform, Load) processes, which are fundamental in converting raw data into actionable insights that support strategic decision-making. It examines established Data Governance frameworks, key performance indicators (KPIs), and industry standards such as DAMA-DMBOK and COBIT. A structured approach to enterprise data integration is proposed, focusing on metadata tiers—Bronze, Silver, and Gold—which serve as essential mechanisms for standardizing and managing data flow efficiently. Furthermore, the functionalities of IBM Knowledge Catalog are analysed, with a particular focus on its benefits, implementation challenges, and economic implications regarding cost optimization and risk mitigation. The research documents the implementation of an advanced Data Governance framework at a fashion company, aiming to elevate data quality, regulatory adherence, and strategic cohesion. The project encompasses a SSIS flow, metadata-driven integration, the application of Data Refinery for data optimization and quality assurance, the deployment of an Enterprise Knowledge Catalog, and the utilization of Watson Studio to harness governance frameworks for strategic intelligence. In conclusion, this thesis underscores the necessity of balancing centralization and decentralization in Data Governance, advocating for a data-driven paradigm that quantifies benefits through empirical performance indicators. It critically addresses challenges associated with initial resource constraints and proposes solutions for a sustainable and adaptable governance model.