In addition to the mention, i would like to suggest the following, 1. Increase the data accessibility and security - Control access, role-based permission, and incident tracking. 2. Policy and process adherence - Compliance with internal data governance policies. 3. User adoption and awareness - Train participants, and stakeholder feedback. 4. Incident and risk management - Number of data breaches, unauthorized access attempts. 5. Efficiency of data processes - Time taken for data retrieval, reporting accuracy.

Mass data is verified by nodes of authorisation, authentication, & accountability. While inputting data queries, the data flow should go from one to the other end, and vice versa without intersecting, that would point out which data is correct from all the data sets. This method is critically important to verify information in 'Big Data' sets. Another, critical part is the storage of data, and the accessibility to the data sets, and whether it's transparent to individual stakeholders (enterprise), or accessible by all. It is critical to ensure the information intercepted in the Cyberspace, is valid, as different consumer's have different Cyber algorithms of their own, thence we need to maintain the data flow from all sets to ensure that.

- Using different data quality metrics like accuracy, - completeness, consistency, uniqueness, etc. - Having a data regulations practices. - Following audits and compliance standard practices. These metrics, when monitored over time, provide a holistic view of how well data governance is functioning and where improvements might be needed.

• Track data quality metrics: accuracy, completeness, consistency. • Conduct regular compliance audits. • Implement user feedback surveys. • Monitor data access and usage metrics. • Track data-related incidents. • Assess training and awareness programs. • Establish key performance indicators (KPIs). • Foster stakeholder engagement. • Utilize data stewardship roles. • Incorporate technology solutions for automation.

1. Data breach incidents 2. Data quality metrics 3. Compliance with regulations 4. Data access and authorization 5. Incident response 6. Data loss prevention 7. Security audit and compliance 8. Training and awareness 9. Data governance maturity 10. Return on investment (ROI)