Data Governance Involves Supervision, Monitoring, And Contro

Data Governance Involves The Supervision Monitoring And Control Of A

Data governance involves the supervision, monitoring, and control of an organization’s data assets. Its main concerns are data quality, appropriateness, and cost effectiveness of the controls. It is difficult because organizations typically have a lot of old data, which is of unknown quality. Imagine that you must collect and assess the quality and appropriateness of data held by a large, multinational organization. What steps you would take? Include how you would address network, security, and ethical considerations when deciding what data to collect from the company. Explain the kinds of support you’ll need and how you will obtain resources and cooperation. This is not a job you can do on your own!

Paper For Above instruction

Effective data governance is essential for large multinational organizations to manage their vast and diverse data assets efficiently. As organizations grow, they accumulate extensive repositories of data, often characterized by varying data quality and relevance. Applying systematic procedures to assess, supervise, and improve data quality involves a series of strategic steps addressing data inventory, quality assessment, and the establishment of governance frameworks. These steps also encompass critical considerations concerning network, security, and ethics, which are paramount in safeguarding organizational data while complying with legal and societal standards.

Step 1: Data Inventory and Categorization

The initial step involves creating a comprehensive inventory of all existing data assets across organizational units and geographic locations. This requires collaboration with data owners and stakeholders to map data sources, formats, storage locations, and access permissions. Categorizing data based on sensitivity, relevance, and compliance requirements facilitates subsequent assessment processes. In multinational organizations, data is often stored in multiple systems and formats; hence, integrating these inventories into a unified framework is crucial for efficiency and accuracy.

Step 2: Data Quality Assessment

After inventorying existing data, the next step is to evaluate its quality and relevance. This involves defining metrics for data accuracy, completeness, timeliness, consistency, and compliance with organizational standards. Techniques such as data profiling and sampling are used to identify errors, inconsistencies, and outdated information, especially in legacy datasets of unknown origin. It is also essential to consult data owners and subject matter experts to understand the context and intended use of the data, which informs decisions on data retention and cleansing.

Step 3: Establishing Governance Frameworks and Policies

A governance framework comprising policies, standards, and procedures ensures consistent management of data assets. Policies should outline data stewardship roles, responsibilities, and accountability for data quality and security. Implementing data lineage tools and metadata management systems helps trace data flows and transformations, providing transparency and traceability essential for compliance and auditing.

Step 4: Addressing Network, Security, and Ethical Considerations

In collecting and assessing data, particular attention must be paid to network infrastructure to enable secure and reliable data transfer. Encryption protocols, VPNs, and firewalls protect data in transit and at rest. Ethical considerations include respecting data privacy rights, obtaining necessary consent, and complying with regulations like GDPR or CCPA. Data collection should prioritize anonymization or pseudonymization for sensitive data to mitigate privacy risks. Ethical frameworks guide decisions on what data is appropriate to collect, especially regarding personally identifiable information (PII) and proprietary data.

Step 5: Securing Support and Resources

Implementing comprehensive data governance requires organizational support from senior management, data stewards, IT departments, and legal teams. Securing resources involves budgeting for technology solutions such as data quality tools, compliance software, and training programs. To gain cooperation, clear communication of the benefits—improved decision-making, regulatory compliance, and risk mitigation—is essential. Establishing cross-functional governance committees fosters accountability and ensures alignment of goals across departments.

Step 6: Collaboration and Continuous Improvement

Effective data governance is an ongoing process involving continuous monitoring, feedback, and refinement. Regular audits and data quality reviews help identify new issues and areas for improvement. Encouraging a culture of data responsibility through training and awareness programs fosters compliance and promotes best practices throughout the organization. Collaboration with external auditors and regulators can also help ensure adherence to evolving standards and legal requirements.

Conclusion

In summary, assessing and managing the quality and appropriateness of data within a large multinational organization involves a comprehensive approach starting with inventory and categorization, followed by rigorous quality assessment, the establishment of governance frameworks, and addressing critical security and ethical issues. Securing organizational support and fostering collaboration are vital to implementing effective data governance. Continuous improvement through monitoring and training sustains data integrity and maximizes the value derived from organizational data assets, ensuring compliance with legal standards and promoting ethical data usage across the enterprise.

References

• Laudon, K. C., & Laudon, J. P. (2020). Management Information Systems: Managing the Digital Firm. Pearson.
• Khatri, V., & Brown, C. V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148-152.
• McKinsey & Company. (2017). Putting Data Governance into Practice: A Framework for Success. McKinsey Digital.
• ISO/IEC 38500:2015. Information technology — Governance of IT for the organization.
• West, M. (2019). Ethical Data Use in Multinational Organizations. Data & Society Research Institute.
• Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33.
• Gartner. (2020). Data Governance Frameworks and Best Practices. Gartner Research.
• OECD. (2013). Big Data and Privacy: How to Balance Data Utility with Privacy Concerns.
• European Union. (2018). General Data Protection Regulation (GDPR). Regulation (EU) 2016/679.
• Singh, J. P., & Nair, T. R. (2021). Data Security and Privacy in Cloud Computing. Springer International Publishing.