Provide data stewardship

This standard is about providing data stewardship.

Data stewards oversee the effective management of data within an organisation, ensuring its quality, security, and compliance with governance policies. Collaborating with stakeholders, they champion adherence to data governance standards and address data-related challenges. They safeguard data assets, uphold integrity, and facilitate compliance efforts.

Additionally, they collaborate with data owners to create an organisation-wide data catalgue. They identify and rectify data quality issues, establish data standards, and promote best practices in data management to enhance data quality and utilisation.

This standard is for those who need to provide data stewardship as part of their duties.

1. implement data governance policies and procedures to facilitate effective day-to-day data management
2. identify and catalogue organisational data assets in line with requirements
3. manage data lineage to track data origin and usage, aligning with organisational protocols
4. establish data-quality requirements and metrics in line with organisational requirements
5. develop procedures for detecting and correcting data-quality issues efficiently
6. assess and monitor data quality throughout data processes, adhering to organisational standards
7. identify and resolve data quality issues promptly
8. provide advice and guidance on data procedures to verify compliance with legislation and organisational policies
9. establish guidelines and protocols to maintain privacy controls for organisational data
10. develop and implement data retention and archival policies in line with organisational procedures
11. implement data security policies and practices to safeguard organisational data
12. develop and maintain metadata standards and documentation in line with organisational procedures
13. confirm adherence of data practices to organisational standards and legal requirements
14. document data processes, metadata, and data dictionaries in line with organisational procedures
15. generate and interpret reports related to data quality, security, and governance effectively

1. data governance principles and best practices
2. data lifecycle management, encompassing acquisition, processing, storage, and archival procedures
3. how to evaluate and uphold data quality standards to ensure data integrity
4. techniques for identifying and resolving data issues through cleansing, correction, or enrichment methodologies
5. data modeling concepts and techniques for effective data representation
6. organisational data sources, structures, formats, and flows
7. data security and privacy regulations to maintain compliance
8. industry-standard database, data warehouse, and data integration tools
9. the importance of establishing and maintaining relationships with data owners and stakeholders
10. data protection and privacy regulations used to safeguard sensitive information
11. the utilisation of industry-standard tools and methodologies such as data dictionaries, data catalogues, data quality tools, data lineage tools, and data governance frameworks
12. the steps involved in tracking and tracing data lineage to understand its origin, transformation, and destination
13. how to define and document organisational data types, attributes, definitions, and metadata in a data dictionary or a data catalogue
14. industry trends and emerging technologies in data management
15. how to establish and enforce data rules, policies, and standards in line with the data governance framework
16. data security methods including encryption, masking, anonymisation, and access control used to safeguard data
17. the importance of educating and training data users on the definitions, rules, standards, and best practices
18. the significance of monitoring and measuring data quality using appropriate metrics to ensure continuous improvement