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A Complete Guide to Data Governance: Tools, Technologies, and Key Performance Metrics. Become a Data Architect/Steward.
What you'll learn
- Define and articulate the purpose of Data Governance and explain its key components, including stakeholders, policies, and processes.
- Identify and analyze key Data Governance frameworks (e.g., DAMA-DMBOK, CMMI, IBM) and explain their role in managing data effectively.
- Understand the roles and responsibilities within a Data Governance structure, such as Data Stewards, Data Owners, and Data Users.
- Evaluate the importance of data stewardship and ownership and describe the relationship between the two within an organization.
- Develop a comprehensive Data Governance plan, outlining the essential components, tools, and technologies required for successful implementation.
- Assess data privacy and compliance requirements and apply key regulations (e.g., GDPR, CCPA) in the context of Data Governance.
- Examine and select appropriate tools and technologies for managing data governance, including data quality tools, data catalogs, and governance platforms.
- Implement and monitor Data Governance policies and procedures by establishing effective metrics, KPIs, and reporting structures.
- Explore recent trends in Data Governance, such as master data management, data lineage, and collaboration tools, to stay ahead in managing organizational data.
- Identify and address common challenges and benefits associated with Data Governance implementation and develop strategies to overcome them.
Requirements
- Enthusiasm and determination to make your mark on the world!
Description
A warm welcome to the Data Governance: Strategies for Data Quality and Compliance course by Uplatz.
Data governance is the framework that outlines how data is managed, accessed, and used within an organization to ensure its accuracy, security, and compliance. It involves setting policies, roles, and responsibilities that dictate how data is handled throughout its lifecycle, from creation and storage to usage and deletion. The primary goal is to ensure that data is reliable, consistent, secure, and used appropriately, especially in the context of regulatory requirements and business needs.
Data governance is crucial for ensuring that organizations can trust and effectively use their data while maintaining compliance and minimizing risk. It works by defining clear processes, roles, and technologies that manage data’s quality, security, and compliance throughout its lifecycle.
How Data Governance Works
Establish Governance Policies: The first step is creating governance policies that define how data should be managed and protected. These policies outline rules for data quality, data access, data usage, and data security.
Assign Roles and Responsibilities: Organizations must identify stakeholders responsible for various aspects of data governance. These roles include Data Stewards (responsible for day-to-day data management), Data Owners (accountable for specific data sets), and Data Governance Committees (which set policies and oversee the program).
Implement Data Governance Tools: Technologies like data cataloging, metadata management, and data quality tools are implemented to track data lineage, maintain data consistency, and monitor data usage.
Monitor and Enforce Compliance: Organizations continuously monitor data processes to ensure adherence to governance policies. They use metrics and key performance indicators (KPIs) to evaluate data quality, compliance, and security.
Iterate and Improve: Data governance is an ongoing process. Organizations must regularly assess their governance practices, adjust policies, and ensure they adapt to new business, legal, or technological developments.
Key Features of Data Governance
Data Quality Management: Ensuring the accuracy, completeness, and consistency of data across the organization.
Data Stewardship: Assigning individuals or teams to manage and ensure the quality and security of data throughout its lifecycle.
Data Security and Privacy: Protecting sensitive data and ensuring that data usage complies with privacy laws (e.g., GDPR, CCPA).
Data Lineage and Metadata Management: Tracking data’s origin, transformations, and its journey across systems to ensure transparency and traceability.
Access Control: Implementing role-based access policies to ensure only authorized users can access or manipulate certain data sets.
Compliance and Regulatory Alignment: Ensuring that data governance practices adhere to industry regulations and standards (e.g., HIPAA, SOX, Basel III).
Data Cataloging: Creating a centralized repository of data assets that provides an inventory of available data, its location, and its governance rules.
KPIs and Metrics: Establishing performance indicators to measure the success of governance initiatives, such as data quality scores, policy compliance rates, and data security metrics.
Data Lifecycle Management: Defining processes for how data is stored, maintained, archived, or deleted at the end of its useful life.
Change Management: Managing changes in data policies, technologies, or business processes while ensuring minimal disruption and consistent governance.
Data Governance - Course Curriculum
Understanding Data Governance
Definition and Purpose
Key components
Stakeholders Involved
Data Governance Frameworks
Implementation Steps
Challenges and Benefits
Why Data Governance is required
Data Governance frameworks
Definition and Purpose
Core components
Policies, standards and processes
Tools and Technologies
DAMA-DMBOK Framework
CMMI Framework
IBM Data Governance Framework
Establishing DG structures, Roles & responsibilities
Data Stewards
Data Owners
Data Users
Organizational Levels of data Governance
Data Governance Council
Data Architect
Data Quality Analyst
Compliance officer
Business users
Management Team
Data Governance Plan
Components
Data Stewardship and Ownership
Relationship between Data Stewardship and Ownership
Data privacy and Compliance
Key Regulations
Tools and Technologies
Recent Trends
Data Catalog
Data quality tools
Governance Platforms
Data Security and privacy tools
Master data management
Lineage tools
Collaboration and workflow management tools
Implementing DG Policies and procedures
Metrics and KPIs
Who this course is for:
- Data Stewards: Responsible for overseeing the data lifecycle and ensuring its quality and compliance with governance policies.
- Data Architects: Professionals designing and managing data architecture, ensuring alignment with governance strategies.
- Data Engineers: Responsible for building data pipelines and ensuring that governance practices are followed during data integration and processing.
- Data Analysts and Scientists: Professionals who use data to derive insights and must understand governance principles to ensure data integrity and compliance.
- Business Analysts: Professionals working closely with data who need to ensure that data usage complies with governance policies.
- IT Managers and Directors: Overseeing data and infrastructure operations, ensuring alignment with governance frameworks.
- Compliance Officers: Responsible for ensuring that data handling meets regulatory and legal standards.
- C-Level Executives: Especially Chief Data Officers (CDOs), CIOs, and CTOs who drive data governance initiatives within the organization.
- Risk and Audit Professionals: Those ensuring that data governance policies mitigate risks and ensure data accuracy and integrity in audits.
- Project Managers: Overseeing data-driven projects, ensuring that governance standards are embedded into processes.
You should keep in mind that the Coupons last a maximum of 4 days or until 1000 registrations are exhausted, but it can expire anytime. Get the course with coupon by clicking on the following button:
