Data Governance Roadmap & Quizzes

Articulate the business drivers and value proposition for data governance using scenarios from Ladley and Reichental.

Compare the core philosophies of DAMA-DMBOK, John Ladley's programmatic approach, and Robert Seiner's Non-Invasive Data Governance.

Describe the 11 knowledge areas of the DAMA Wheel and explain how they interrelate within a governance program.

Assess which governance philosophy (or hybrid) is most suitable for a given organizational culture (e.g., startup vs. large enterprise).

Define the key principles of effective data governance, including accountability, transparency, and stewardship, in the context of the chosen frameworks.

Compare common data governance operating models: centralized, decentralized, and federated.

Define the composition and responsibilities of a Data Governance Council/Board.

Differentiate between the roles of Data Owner, Data Steward (business, technical), and Data Custodian.

Outline the strategic responsibilities of a Chief Data Officer (CDO) and their position in the executive team.

Design an accountability matrix (RACI) for a common data governance scenario, such as defining a new business term.

Explain how to integrate governance roles into existing organizational structures using Seiner's non-invasive approach.

Design a process for establishing and enforcing data quality rules from a governance perspective.

Develop a data quality dashboard concept with KPIs for monitoring and reporting to stakeholders.

Outline a data quality issue resolution workflow, including root cause analysis and remediation planning.

Design the governance structure for a Business Glossary, including workflows for term creation, approval, and maintenance.

Apply data lineage analysis to assess the business impact of a proposed change to a critical data element.

Develop a phased, multi-year data governance roadmap with key milestones and deliverables.

Create a detailed implementation plan for the first phase of the data governance roadmap.

Draft a high-level data policy for a specific domain (e.g., Data Quality or Data Access).

Develop a set of specific data standards to support the sample data policy.

Outline a procedure for enforcing a specific data standard and handling non-compliance.