Data Quality and Governance

Data Quality and Governance is a framework and set of practices that organizations implement to ensure the reliability and integrity of their data assets. It encompasses a systematic approach to managing data quality, enforcing data governance policies, and maintaining data consistency across the organization.

This competency area includes an understanding of the concepts of Data Analysis, Data Quality Rules, Data Governance, Data Integration, Data Modeling, Data Cleansing, Data Security and Privacy, Change Management, Project Management.

Key Competencies:

1. Data Analysis -  Proficiency in data analysis to identify data quality issues, inconsistencies, and patterns within data.

2. Data Quality Rules - Ability to define and implement data quality rules and standards to maintain data accuracy and completeness.

3. Data Governance -  Understanding of data governance principles, including data stewardship, data ownership, and compliance with data policies and regulations.

4. Data Integration - Knowledge of data integration techniques to consolidate and harmonize data from diverse sources.

5. Data Modeling -  Skill in data modeling to structure and represent data entities, attributes, and relationships.

6. Data Cleansing - Expertise in data cleansing processes to identify and rectify data inaccuracies and inconsistencies.. Data Tools: Familiarity with data quality tools and software   solutions for data profiling, cleansing, and monitoring.

7. Data Security and Privacy - Knowledge of data security measures and data protection regulations to safeguard sensitive information.

8. Change Management - Ability to manage changes in data quality rules, data governance policies, and data-related processes.

9. Project Management - Skills in project management to plan and execute data quality and governance initiatives effectively.