Advancing Your Career in Data Governance and Stewardship

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This training program is designed for data stewards and data governance practitioners who want to advance their careers and take a more active role in shaping and coordinating data governance practices within their organizations. The program focuses on developing practical professional skills rather than theoretical knowledge, helping participants understand how data governance activities are organized and executed in real-world organizational settings. It explores how governance responsibilities are structured, how different roles collaborate across business and technology teams, and how governance practices support reliable data for reporting, analysis, and decision-making. Throughout the program, participants gain practical perspectives and approaches that can be applied directly in their professional work while strengthening their ability to contribute to more effective data governance.

Select a fit-for-purpose data management framework aligned with organizational goals.
Define a feasible, value-driven scope for data initiatives that support business priorities.
Formulate strategic directions and objectives that guide the entire data management function.
Design enterprise-wide governance, including a DM operating model, roles' hierarchy, and governing bodies.
Develop business architecture governance that links business structure with data and AI practices and defines corresponding policies, processes, roles, and IT tool requirements.
Establish governance for data modeling that defines modeling standards, processes, roles, key artifacts, and requirements for supporting tools.
Design governance for data and application architecture that aligns systems and integrations with business needs and specifies required policies, processes, roles, and architectural artifacts.
Define governance for metadata and data lineage that covers metadata categories, required artifacts, processes, roles, and tool specifications.
Develop data quality governance that includes quality rules, processes, controls, roles, required artifacts, and tool requirements.
Create a performance measurement system for capabilities and the overall data management function.
Assess maturity levels for the data management capability and its components to guide improvement.