Data Governance Specialist

Define and Measure Data Quality Metrics:

• Establish metrics for accuracy, completeness, validity, consistency, timeliness, and reliability.

Continuous Monitoring and Remediation:

• Regularly monitor data quality, conduct audits, perform root cause analysis for recurring data issues, and implement preventive measures and remediation plans.

Data Profiling:

• Develop and maintain comprehensive data profiles to understand data characteristics.

Data Validation:

• Create and implement validation rules to ensure that incoming data conforms to expected formats and values.

Data Cleansing:

• Design and execute data cleansing processes to correct errors and inconsistencies, enhancing overall data quality and reliability.

Data Governance:

• Establish Governance Framework: Implement and enforce data governance practices to ensure compliance with regulatory requirements and corporate policies, ensuring data is managed according to best practices.
• Metadata Management: Develop and maintain a comprehensive metadata repository to document data definitions, lineage, and usage, ensuring it is kept up to date and accessible to end users.
• Understand User Needs: Collaborate with business users to identify data needs, pain points, and requirements, ensuring the data is fit for its intended use.
• Identify Improvement Areas: Continuously seek opportunities for process improvement in data governance and quality management.
• User Roles and Access Requirements: Understand user roles and access requirements for systems, so that similar protection can be implemented into the analytical solutions.
• Row-Level Security: Work with the data & analytics team to establish row-level security for analytical solutions, ensuring data is accessible only to authorised users.

Continuous Improvement:

• Establish Naming Conventions: Define business friendly table names and column names, along with synonyms, to ensure data easily accessible using AI.
• Create Synonyms: Implement synonyms to simplify data access and enhance data readability.
• Establish KPIs for data governance and quality efforts and create regular reports for stakeholders to track progress and demonstrate the value of data governance initiatives.
• Continuous Improvement: Establish a feedback loop where users can report data quality issues and suggest improvements.