Data Governance: Strategies for Data Quality and Compliance

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

1. 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.

2. 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).

3. 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.

4. 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.

5. 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

1. Data Quality Management: Ensuring the accuracy, completeness, and consistency of data across the organization.

2. Data Stewardship: Assigning individuals or teams to manage and ensure the quality and security of data throughout its lifecycle.

3. Data Security and Privacy: Protecting sensitive data and ensuring that data usage complies with privacy laws (e.g., GDPR, CCPA).

4. Data Lineage and Metadata Management: Tracking data’s origin, transformations, and its journey across systems to ensure transparency and traceability.

5. Access Control: Implementing role-based access policies to ensure only authorized users can access or manipulate certain data sets.

6. Compliance and Regulatory Alignment: Ensuring that data governance practices adhere to industry regulations and standards (e.g., HIPAA, SOX, Basel III).

7. Data Cataloging: Creating a centralized repository of data assets that provides an inventory of available data, its location, and its governance rules.

8. 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.

9. Data Lifecycle Management: Defining processes for how data is stored, maintained, archived, or deleted at the end of its useful life.

10. Change Management: Managing changes in data policies, technologies, or business processes while ensuring minimal disruption and consistent governance.

Data Governance – Course Curriculum

1. Understanding Data Governance

   1. Definition and Purpose

   2. Key components

   3. Stakeholders Involved

   4. Data Governance Frameworks

   5. Implementation Steps

   6. Challenges and Benefits

2. Why Data Governance is required

3. Data Governance frameworks

   1. Definition and Purpose

   2. Core components

   3. Policies, standards and processes

   4. Tools and Technologies

   5. DAMA-DMBOK Framework

   6. CMMI Framework

   7. IBM Data Governance Framework

4. Establishing DG structures, Roles & responsibilities

   1. Data Stewards

   2. Data Owners

   3. Data Users

   4. Organizational Levels of data Governance

   5. Data Governance Council

   6. Data Architect

   7. Data Quality Analyst

   8. Compliance officer

   9. Business users

   10. Management Team

5. Data Governance Plan

   1. Components

6. Data Stewardship and Ownership

   1. Relationship between Data Stewardship and Ownership

7. Data privacy and Compliance

   1. Key Regulations

8. Tools and Technologies

   1. Recent Trends

   2. Data Catalog

   3. Data quality tools

   4. Governance Platforms

   5. Data Security and privacy tools

   6. Master data management

   7. Lineage tools

   8. Collaboration and workflow management tools

9. Implementing DG Policies and procedures

10. Metrics and KPIs