Data Governance aims to establish a framework of rules, policies, and standards that guide how data is managed, used, and protected within an organization.

Data Quality Management is a crucial aspect of Data Governance, as it focuses on ensuring the accuracy, completeness, consistency, and validity of data.

Data Quality Management encompasses a range of activities aimed at improving data quality, including identifying and correcting errors, establishing standards and metrics, and monitoring and improving data quality over time.

Accuracy, Completeness, Consistency, and Timeliness are all fundamental dimensions of data quality.

Data Lineage and Provenance play a vital role in Data Governance by providing a comprehensive understanding of the origin, transformation, and movement of data, enabling data auditing, compliance, and transparency.

Data Integration and Interoperability aim to facilitate seamless data sharing, ensure data consistency, and improve data accessibility and usability across different systems and applications.

Establishing clear roles and responsibilities, implementing data quality processes, and promoting data stewardship are all essential best practices for effective Data Governance.

Data Governance plays a crucial role in modern organizations by enabling data-driven decision-making, improving data security and compliance, and promoting a culture of data quality and accountability.

Lack of executive sponsorship, resistance to change, and limited resources are common challenges organizations face during Data Governance implementation.

Data Governance plays a critical role in ensuring data privacy and compliance by establishing policies, monitoring compliance, and educating employees on data privacy and security.

Effective Data Governance leads to improved data quality, enhanced data security, and increased data accessibility and usability.

The Data Governance Council is responsible for overseeing and guiding Data Governance initiatives, establishing data standards and policies, and monitoring and assessing data quality.

DMBOK, ISO/IEC 38500:2019, and COBIT 2019 are widely recognized Data Governance frameworks.

Data Quality Assessment aims to evaluate the current state of data quality, identify issues and root causes, and recommend improvements to enhance data quality.

Data Profiling Tools, Data Validation Tools, and Data Cleansing Tools are commonly used for Data Quality improvement.