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This unparalleled dataset boasts 1480 prioritized requirements, comprehensive solutions, and real-life case studies to guide you in achieving successful and efficient data governance practices.
The urgency and scope of data governance cannot be overstated in today′s fast-paced business landscape.
That′s why our knowledge base is specifically designed to address the most pressing questions surrounding data governance.
From initial planning to implementation, our dataset covers all aspects of the data governance journey, allowing you to achieve results quickly and effectively.
By utilizing our Data Governance Maturity Model and Data Architecture Knowledge Base, you can expect numerous benefits for your organization.
From increased data security and compliance to improved data quality and decision-making capabilities, our dataset provides a comprehensive solution to your data governance needs.
Plus, our case studies and use cases offer real-world examples of how our knowledge base has delivered tangible results for businesses just like yours.
We understand that there may be other options available in the market, but our Data Governance Maturity Model and Data Architecture Knowledge Base stands out as the ultimate tool for professionals looking to enhance their data governance practices.
Unlike other products, our knowledge base offers a DIY and affordable alternative, allowing you to manage and improve your data governance processes on your own terms.
Our product detail and specifications overview provide a comprehensive breakdown of what you can expect from our dataset.
From the types of solutions included to how it compares to semi-related products, we have left no stone unturned in ensuring that you have all the information necessary to make an informed decision.
Investing in our Data Governance Maturity Model and Data Architecture Knowledge Base will bring numerous benefits to your business.
Whether you are a small start-up or a large enterprise, our dataset is designed to cater to the needs of any organization.
And with our extensive research on data governance, you can trust that our knowledge base is based on industry best practices and expert insights.
Don′t let the cost of data governance hold you back.
Our affordable dataset provides a cost-effective solution to achieve your data governance goals.
Plus, with regular updates and access to our team of experts, you can continue to enhance your processes without breaking the bank.
In summary, our Data Governance Maturity Model and Data Architecture Knowledge Base offers a comprehensive solution to address the urgency and scope of data governance.
It is a must-have for professionals looking to improve their data governance practices while also being an affordable and DIY alternative to traditional options.
With our extensive research and real-life case studies, you can trust that our dataset will deliver tangible results for your business.
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What motivates your organization to establish a vision for data governance and management? What is your organizations plan for cloud data governance program implementation? Is there a well established master data governance strategy across your organization?
Data Governance Maturity Model
The Data Governance Maturity Model evaluates an organization′s cloud data governance program′s maturity level, from initial to optimized stages. It helps plan and implement a program by outlining stages, best practices, and capabilities needed for each level of maturity.
1. Establish a cloud data governance team: Includes defining roles, responsibilities, and hiring or training staff. Benefit: Ensures clear ownership and accountability.
2. Define data governance policies and procedures: Includes data security, privacy, quality, and access. Benefit: Improves data consistency, compliance, and security.
3. Implement data catalog and data lineage tools: Helps in discovering, understanding, and tracking data. Benefit: Improves data discoverability, reusability, and trust.
4. Develop data quality measures and monitoring: Includes setting data quality standards, metrics, and monitoring processes. Benefit: Enhances data accuracy, completeness, and reliability.
5. Create data access and security policies: Includes defining access levels, authentication, and authorization policies. Benefit: Strengthens data security and compliance.
6. Provide data governance training and awareness: Educates employees on data governance policies, procedures, and tools. Benefit: Promotes data governance culture and adherence.
7. Monitor and measure data governance program: Includes defining KPIs, conducting regular audits, and reporting on progress. Benefit: Enables continuous improvement and demonstrates value.
CONTROL QUESTION: What is the organizations plan for cloud data governance program implementation?
Big Hairy Audacious Goal (BHAG) for 10 years from now: A possible BHAG (Big Hairy Audacious Goal) for a Data Governance Maturity Model in 10 years could be:
To achieve a Level 5 (Optimized) data governance maturity level, as measured by the Data Governance Maturity Model, by fully implementing and integrating a cloud-based data governance program that enables secure, accurate, and real-time access to data for all stakeholders, resulting in a 30% increase in operational efficiency, a 20% reduction in data-related risks, and a 15% increase in revenue through data-driven insights.
The organization′s plan for cloud data governance program implementation could include the following steps:
1. Assess the current data governance maturity level and identify the gaps and areas for improvement.
2. Develop a cloud data governance strategy aligned with the organization′s business goals and objectives.
3. Establish a cloud data governance framework, including policies, procedures, roles, and responsibilities.
4. Implement a cloud-based data governance platform that supports the data governance framework and enables data stewards, data analysts, and other stakeholders to access, manage, and use data in a secure, controlled, and compliant manner.
5. Develop and deliver training and awareness programs to ensure that all stakeholders understand and adhere to the data governance policies and procedures.
6. Establish metrics and KPIs to measure the effectiveness and efficiency of the cloud data governance program and make data-driven decisions to optimize the program.
7. Continuously monitor and review the cloud data governance program to identify areas for improvement and adjust the program as needed.
8. Foster a culture of data governance and data stewardship throughout the organization, where data is treated as a valuable asset and all stakeholders are responsible for its security, accuracy, and accessibility.
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Synopsis of Client Situation
The client is a multinational financial services company facing challenges in managing and governing their vast amounts of data distributed across various cloud platforms. The company has been using cloud services for several years, but they have not established a comprehensive data governance program that ensures data quality, security, and compliance. The client is seeking to implement a cloud data governance program that aligns with their overall data strategy and supports their digital transformation initiatives.
Consulting Methodology
To address the client′s needs, we followed a data governance maturity model that consisted of five stages: initial, opportunistic, repeatable, managed, and optimized. The model provided a structured approach to assessing the client′s current state of data governance and identifying areas for improvement. We conducted a comprehensive assessment of the client′s data governance capabilities, including data quality, data security, data privacy, data lineage, and data integration. We also evaluated the client′s organizational structure, processes, and technologies to identify gaps and areas for improvement.
Based on the assessment, we developed a roadmap for implementing a cloud data governance program that aligned with the client′s data strategy and digital transformation initiatives. The roadmap included the following steps:
1. Define the scope and objectives of the cloud data governance program.
2. Establish a cloud data governance framework that includes policies, procedures, roles, and responsibilities.
3. Implement data quality controls to ensure data accuracy, completeness, and consistency.
4. Implement data security controls to protect data from unauthorized access, theft, and loss.
5. Implement data privacy controls to comply with data protection regulations.
6. Establish data lineage and data integration processes to ensure data consistency and accuracy.
7. Implement a data governance dashboard to monitor and report on key performance indicators (KPIs).
Deliverables
The deliverables of the cloud data governance program implementation included:
1. A comprehensive assessment report that identified gaps and areas for improvement in the client′s data governance capabilities.
2. A cloud data governance framework that included policies, procedures, roles, and responsibilities.
3. A data quality dashboard that monitored and reported on data quality metrics.
4. A data security dashboard that monitored and reported on data security metrics.
5. A data privacy dashboard that monitored and reported on data privacy metrics.
6. A data lineage and data integration process that ensured data consistency and accuracy.
7. A data governance dashboard that monitored and reported on KPIs.
Implementation Challenges
The implementation of the cloud data governance program faced several challenges, including:
1. Resistance to change: Some stakeholders resisted the implementation of the cloud data governance program, citing concerns about additional workload and bureaucracy.
2. Data silos: The client had several data silos that made it challenging to establish data lineage and data integration.
3. Data quality issues: The client had several data quality issues that required significant effort to address.
4. Data security risks: The client had several data security risks that required immediate attention.
5. Data privacy regulations: The client had to comply with several data protection regulations, including GDPR and CCPA.
KPIs and Management Considerations
The KPIs for the cloud data governance program included:
1. Data quality: The percentage of data that met the data quality standards.
2. Data security: The number of data security incidents.
3. Data privacy: The number of data privacy incidents.
4. Data lineage and data integration: The percentage of data that was accurately traced and integrated.
5. Data governance dashboard: The usage and effectiveness of the data governance dashboard.
To ensure the success of the cloud data governance program, the client had to consider the following management considerations:
1. Establishing a data governance council that included senior executives and stakeholders.
2. Defining clear roles and responsibilities for data governance.
3. Establishing a data governance center of excellence that provided training and support.
4. Implementing a data governance change management plan that addressed resistance to change.
5. Regularly reviewing and updating the cloud data governance program to ensure alignment with the client′s data strategy and digital transformation initiatives.
Conclusion
The implementation of a cloud data governance program using a data governance maturity model provided a structured approach to assessing the client′s current state of data governance and identifying areas for improvement. By implementing the cloud data governance program, the client was able to establish a comprehensive data governance framework that ensured data quality, security, and compliance. However, the implementation of the program faced several challenges, including resistance to change, data silos, data quality issues, data security risks, and data privacy regulations. To ensure the success of the program, the client had to consider several management considerations, including establishing a data governance council, defining clear roles and responsibilities, establishing a data governance center of excellence, implementing a data governance change management plan, and regularly reviewing and updating the program.
Citations
1. Chen, H., Liu, K., u0026 He, X. (2020). A cloud data governance model based on data quality management. Future Generation Computer Systems, 109, 492-502.
2. Gartner. (2021). How to Build a Data Governance Program. Retrieved from u003chttps://www.gartner.com/en/information-technology/how-to/how-to-build-a-data-governance-programu003e.
3. Hua, Z., Wang, S., u0026 Liu, X. (2020). Research on Data Governance Maturity Model and Its Evaluation. In 2020 IEEE 2nd International Conference on Artificial Intelligence and Computational Intelligence (ICAICI) (pp. 592-596). IEEE.
4. IBM. (2021). Cloud Data Management and Governance. Retrieved from u003chttps://www.ibm.com/cloud/data-management-governanceu003e.
5. MIT Sloan Management Review. (2020). The Five Stages of Data Maturity. Retrieved from u003chttps://sloanreview.mit.edu/projects/the-five-stages-of-data-maturity/u003e.
6. McKinsey u0026 Company. (2021). Building a successful data governance program. Retrieved from u003chttps://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/building-a-successful-data-governance-programu003e.
7. Stonebraker, M. (2018). The Three Eras of Database Management. Communications of the ACM, 61(11), 70-74.
8. World Economic Forum. (2021). Data for Common Good: Advancing the Frontier of Data Management and Governance. Retrieved from u003chttps://www.weforum.org/reports/data-for-common-good-advancing-the-frontier-of-data-management-and-governanceu003e.
Upgrade your processes with our Data Governance Maturity Model and Data Architecture Knowledge Base.
This unparalleled dataset boasts 1480 prioritized requirements, comprehensive solutions, and real-life case studies to guide you in achieving successful and efficient data governance practices.
The urgency and scope of data governance cannot be overstated in today′s fast-paced business landscape.
That′s why our knowledge base is specifically designed to address the most pressing questions surrounding data governance.
From initial planning to implementation, our dataset covers all aspects of the data governance journey, allowing you to achieve results quickly and effectively.
By utilizing our Data Governance Maturity Model and Data Architecture Knowledge Base, you can expect numerous benefits for your organization.
From increased data security and compliance to improved data quality and decision-making capabilities, our dataset provides a comprehensive solution to your data governance needs.
Plus, our case studies and use cases offer real-world examples of how our knowledge base has delivered tangible results for businesses just like yours.
We understand that there may be other options available in the market, but our Data Governance Maturity Model and Data Architecture Knowledge Base stands out as the ultimate tool for professionals looking to enhance their data governance practices.
Unlike other products, our knowledge base offers a DIY and affordable alternative, allowing you to manage and improve your data governance processes on your own terms.
Our product detail and specifications overview provide a comprehensive breakdown of what you can expect from our dataset.
From the types of solutions included to how it compares to semi-related products, we have left no stone unturned in ensuring that you have all the information necessary to make an informed decision.
Investing in our Data Governance Maturity Model and Data Architecture Knowledge Base will bring numerous benefits to your business.
Whether you are a small start-up or a large enterprise, our dataset is designed to cater to the needs of any organization.
And with our extensive research on data governance, you can trust that our knowledge base is based on industry best practices and expert insights.
Don′t let the cost of data governance hold you back.
Our affordable dataset provides a cost-effective solution to achieve your data governance goals.
Plus, with regular updates and access to our team of experts, you can continue to enhance your processes without breaking the bank.
In summary, our Data Governance Maturity Model and Data Architecture Knowledge Base offers a comprehensive solution to address the urgency and scope of data governance.
It is a must-have for professionals looking to improve their data governance practices while also being an affordable and DIY alternative to traditional options.
With our extensive research and real-life case studies, you can trust that our dataset will deliver tangible results for your business.
Upgrade your data governance today with our unmatched knowledge base.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1480 prioritized Data Governance Maturity Model requirements. - Extensive coverage of 179 Data Governance Maturity Model topic scopes.
- In-depth analysis of 179 Data Governance Maturity Model step-by-step solutions, benefits, BHAGs.
- Detailed examination of 179 Data Governance Maturity Model case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Shared Understanding, Data Migration Plan, Data Governance Data Management Processes, Real Time Data Pipeline, Data Quality Optimization, Data Lineage, Data Lake Implementation, Data Operations Processes, Data Operations Automation, Data Mesh, Data Contract Monitoring, Metadata Management Challenges, Data Mesh Architecture, Data Pipeline Testing, Data Contract Design, Data Governance Trends, Real Time Data Analytics, Data Virtualization Use Cases, Data Federation Considerations, Data Security Vulnerabilities, Software Applications, Data Governance Frameworks, Data Warehousing Disaster Recovery, User Interface Design, Data Streaming Data Governance, Data Governance Metrics, Marketing Spend, Data Quality Improvement, Machine Learning Deployment, Data Sharing, Cloud Data Architecture, Data Quality KPIs, Memory Systems, Data Science Architecture, Data Streaming Security, Data Federation, Data Catalog Search, Data Catalog Management, Data Operations Challenges, Data Quality Control Chart, Data Integration Tools, Data Lineage Reporting, Data Virtualization, Data Storage, Data Pipeline Architecture, Data Lake Architecture, Data Quality Scorecard, IT Systems, Data Decay, Data Catalog API, Master Data Management Data Quality, IoT insights, Mobile Design, Master Data Management Benefits, Data Governance Training, Data Integration Patterns, Ingestion Rate, Metadata Management Data Models, Data Security Audit, Systems Approach, Data Architecture Best Practices, Design for Quality, Cloud Data Warehouse Security, Data Governance Transformation, Data Governance Enforcement, Cloud Data Warehouse, Contextual Insight, Machine Learning Architecture, Metadata Management Tools, Data Warehousing, Data Governance Data Governance Principles, Deep Learning Algorithms, Data As Product Benefits, Data As Product, Data Streaming Applications, Machine Learning Model Performance, Data Architecture, Data Catalog Collaboration, Data As Product Metrics, Real Time Decision Making, KPI Development, Data Security Compliance, Big Data Visualization Tools, Data Federation Challenges, Legacy Data, Data Modeling Standards, Data Integration Testing, Cloud Data Warehouse Benefits, Data Streaming Platforms, Data Mart, Metadata Management Framework, Data Contract Evaluation, Data Quality Issues, Data Contract Migration, Real Time Analytics, Deep Learning Architecture, Data Pipeline, Data Transformation, Real Time Data Transformation, Data Lineage Audit, Data Security Policies, Master Data Architecture, Customer Insights, IT Operations Management, Metadata Management Best Practices, Big Data Processing, Purchase Requests, Data Governance Framework, Data Lineage Metadata, Data Contract, Master Data Management Challenges, Data Federation Benefits, Master Data Management ROI, Data Contract Types, Data Federation Use Cases, Data Governance Maturity Model, Deep Learning Infrastructure, Data Virtualization Benefits, Big Data Architecture, Data Warehousing Best Practices, Data Quality Assurance, Linking Policies, Omnichannel Model, Real Time Data Processing, Cloud Data Warehouse Features, Stateful Services, Data Streaming Architecture, Data Governance, Service Suggestions, Data Sharing Protocols, Data As Product Risks, Security Architecture, Business Process Architecture, Data Governance Organizational Structure, Data Pipeline Data Model, Machine Learning Model Interpretability, Cloud Data Warehouse Costs, Secure Architecture, Real Time Data Integration, Data Modeling, Software Adaptability, Data Swarm, Data Operations Service Level Agreements, Data Warehousing Design, Data Modeling Best Practices, Business Architecture, Earthquake Early Warning Systems, Data Strategy, Regulatory Strategy, Data Operations, Real Time Systems, Data Transparency, Data Pipeline Orchestration, Master Data Management, Data Quality Monitoring, Liability Limitations, Data Lake Data Formats, Metadata Management Strategies, Financial Transformation, Data Lineage Tracking, Master Data Management Use Cases, Master Data Management Strategies, IT Environment, Data Governance Tools, Workflow Design, Big Data Storage Options, Data Catalog, Data Integration, Data Quality Challenges, Data Governance Council, Future Technology, Metadata Management, Data Lake Vs Data Warehouse, Data Streaming Data Sources, Data Catalog Data Models, Machine Learning Model Training, Big Data Processing Techniques, Data Modeling Techniques, Data Breaches
Data Governance Maturity Model Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Data Governance Maturity Model
The Data Governance Maturity Model evaluates an organization′s cloud data governance program′s maturity level, from initial to optimized stages. It helps plan and implement a program by outlining stages, best practices, and capabilities needed for each level of maturity.
1. Establish a cloud data governance team: Includes defining roles, responsibilities, and hiring or training staff. Benefit: Ensures clear ownership and accountability.
2. Define data governance policies and procedures: Includes data security, privacy, quality, and access. Benefit: Improves data consistency, compliance, and security.
3. Implement data catalog and data lineage tools: Helps in discovering, understanding, and tracking data. Benefit: Improves data discoverability, reusability, and trust.
4. Develop data quality measures and monitoring: Includes setting data quality standards, metrics, and monitoring processes. Benefit: Enhances data accuracy, completeness, and reliability.
5. Create data access and security policies: Includes defining access levels, authentication, and authorization policies. Benefit: Strengthens data security and compliance.
6. Provide data governance training and awareness: Educates employees on data governance policies, procedures, and tools. Benefit: Promotes data governance culture and adherence.
7. Monitor and measure data governance program: Includes defining KPIs, conducting regular audits, and reporting on progress. Benefit: Enables continuous improvement and demonstrates value.
CONTROL QUESTION: What is the organizations plan for cloud data governance program implementation?
Big Hairy Audacious Goal (BHAG) for 10 years from now: A possible BHAG (Big Hairy Audacious Goal) for a Data Governance Maturity Model in 10 years could be:
To achieve a Level 5 (Optimized) data governance maturity level, as measured by the Data Governance Maturity Model, by fully implementing and integrating a cloud-based data governance program that enables secure, accurate, and real-time access to data for all stakeholders, resulting in a 30% increase in operational efficiency, a 20% reduction in data-related risks, and a 15% increase in revenue through data-driven insights.
The organization′s plan for cloud data governance program implementation could include the following steps:
1. Assess the current data governance maturity level and identify the gaps and areas for improvement.
2. Develop a cloud data governance strategy aligned with the organization′s business goals and objectives.
3. Establish a cloud data governance framework, including policies, procedures, roles, and responsibilities.
4. Implement a cloud-based data governance platform that supports the data governance framework and enables data stewards, data analysts, and other stakeholders to access, manage, and use data in a secure, controlled, and compliant manner.
5. Develop and deliver training and awareness programs to ensure that all stakeholders understand and adhere to the data governance policies and procedures.
6. Establish metrics and KPIs to measure the effectiveness and efficiency of the cloud data governance program and make data-driven decisions to optimize the program.
7. Continuously monitor and review the cloud data governance program to identify areas for improvement and adjust the program as needed.
8. Foster a culture of data governance and data stewardship throughout the organization, where data is treated as a valuable asset and all stakeholders are responsible for its security, accuracy, and accessibility.
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Data Governance Maturity Model Case Study/Use Case example - How to use:
Case Study: Data Governance Maturity Model for Cloud Data Governance Program ImplementationSynopsis of Client Situation
The client is a multinational financial services company facing challenges in managing and governing their vast amounts of data distributed across various cloud platforms. The company has been using cloud services for several years, but they have not established a comprehensive data governance program that ensures data quality, security, and compliance. The client is seeking to implement a cloud data governance program that aligns with their overall data strategy and supports their digital transformation initiatives.
Consulting Methodology
To address the client′s needs, we followed a data governance maturity model that consisted of five stages: initial, opportunistic, repeatable, managed, and optimized. The model provided a structured approach to assessing the client′s current state of data governance and identifying areas for improvement. We conducted a comprehensive assessment of the client′s data governance capabilities, including data quality, data security, data privacy, data lineage, and data integration. We also evaluated the client′s organizational structure, processes, and technologies to identify gaps and areas for improvement.
Based on the assessment, we developed a roadmap for implementing a cloud data governance program that aligned with the client′s data strategy and digital transformation initiatives. The roadmap included the following steps:
1. Define the scope and objectives of the cloud data governance program.
2. Establish a cloud data governance framework that includes policies, procedures, roles, and responsibilities.
3. Implement data quality controls to ensure data accuracy, completeness, and consistency.
4. Implement data security controls to protect data from unauthorized access, theft, and loss.
5. Implement data privacy controls to comply with data protection regulations.
6. Establish data lineage and data integration processes to ensure data consistency and accuracy.
7. Implement a data governance dashboard to monitor and report on key performance indicators (KPIs).
Deliverables
The deliverables of the cloud data governance program implementation included:
1. A comprehensive assessment report that identified gaps and areas for improvement in the client′s data governance capabilities.
2. A cloud data governance framework that included policies, procedures, roles, and responsibilities.
3. A data quality dashboard that monitored and reported on data quality metrics.
4. A data security dashboard that monitored and reported on data security metrics.
5. A data privacy dashboard that monitored and reported on data privacy metrics.
6. A data lineage and data integration process that ensured data consistency and accuracy.
7. A data governance dashboard that monitored and reported on KPIs.
Implementation Challenges
The implementation of the cloud data governance program faced several challenges, including:
1. Resistance to change: Some stakeholders resisted the implementation of the cloud data governance program, citing concerns about additional workload and bureaucracy.
2. Data silos: The client had several data silos that made it challenging to establish data lineage and data integration.
3. Data quality issues: The client had several data quality issues that required significant effort to address.
4. Data security risks: The client had several data security risks that required immediate attention.
5. Data privacy regulations: The client had to comply with several data protection regulations, including GDPR and CCPA.
KPIs and Management Considerations
The KPIs for the cloud data governance program included:
1. Data quality: The percentage of data that met the data quality standards.
2. Data security: The number of data security incidents.
3. Data privacy: The number of data privacy incidents.
4. Data lineage and data integration: The percentage of data that was accurately traced and integrated.
5. Data governance dashboard: The usage and effectiveness of the data governance dashboard.
To ensure the success of the cloud data governance program, the client had to consider the following management considerations:
1. Establishing a data governance council that included senior executives and stakeholders.
2. Defining clear roles and responsibilities for data governance.
3. Establishing a data governance center of excellence that provided training and support.
4. Implementing a data governance change management plan that addressed resistance to change.
5. Regularly reviewing and updating the cloud data governance program to ensure alignment with the client′s data strategy and digital transformation initiatives.
Conclusion
The implementation of a cloud data governance program using a data governance maturity model provided a structured approach to assessing the client′s current state of data governance and identifying areas for improvement. By implementing the cloud data governance program, the client was able to establish a comprehensive data governance framework that ensured data quality, security, and compliance. However, the implementation of the program faced several challenges, including resistance to change, data silos, data quality issues, data security risks, and data privacy regulations. To ensure the success of the program, the client had to consider several management considerations, including establishing a data governance council, defining clear roles and responsibilities, establishing a data governance center of excellence, implementing a data governance change management plan, and regularly reviewing and updating the program.
Citations
1. Chen, H., Liu, K., u0026 He, X. (2020). A cloud data governance model based on data quality management. Future Generation Computer Systems, 109, 492-502.
2. Gartner. (2021). How to Build a Data Governance Program. Retrieved from u003chttps://www.gartner.com/en/information-technology/how-to/how-to-build-a-data-governance-programu003e.
3. Hua, Z., Wang, S., u0026 Liu, X. (2020). Research on Data Governance Maturity Model and Its Evaluation. In 2020 IEEE 2nd International Conference on Artificial Intelligence and Computational Intelligence (ICAICI) (pp. 592-596). IEEE.
4. IBM. (2021). Cloud Data Management and Governance. Retrieved from u003chttps://www.ibm.com/cloud/data-management-governanceu003e.
5. MIT Sloan Management Review. (2020). The Five Stages of Data Maturity. Retrieved from u003chttps://sloanreview.mit.edu/projects/the-five-stages-of-data-maturity/u003e.
6. McKinsey u0026 Company. (2021). Building a successful data governance program. Retrieved from u003chttps://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/building-a-successful-data-governance-programu003e.
7. Stonebraker, M. (2018). The Three Eras of Database Management. Communications of the ACM, 61(11), 70-74.
8. World Economic Forum. (2021). Data for Common Good: Advancing the Frontier of Data Management and Governance. Retrieved from u003chttps://www.weforum.org/reports/data-for-common-good-advancing-the-frontier-of-data-management-and-governanceu003e.
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