Training on Data Governance and Quality Assurance in Health Analytics

What is Data Governance and Quality Assurance in Health Analytics?

Data governance in health analytics refers to the policies, standards, and processes that ensure health data is properly managed, protected, and used responsibly. Quality assurance focuses on maintaining accuracy, consistency, completeness, and reliability of health data used for analysis, reporting, and decision-making in healthcare systems.

Health systems increasingly rely on data-driven insights, but many face challenges such as poor data quality, fragmented systems, inconsistent reporting standards, weak compliance, and inadequate data protection. These issues compromise analytics accuracy, lead to unreliable insights, and can negatively affect patient outcomes and policy decisions.

This course provides a structured approach to implementing robust data governance and quality assurance frameworks in health analytics environments. Participants will learn how to establish data standards, enforce compliance, improve data quality, and implement oversight mechanisms that ensure trustworthy and actionable health insights.

Key Concepts Covered

• Data governance frameworks in healthcare systems
• Data quality dimensions (accuracy, completeness, timeliness, consistency)
• Health data lifecycle management
• Data standards, policies, and regulatory compliance
• Ethical handling of health data and patient privacy
• Data stewardship and accountability structures
• Quality assurance methodologies in health analytics
• Monitoring, auditing, and continuous improvement systems

Participants will apply skills to:
• Improve reliability of health data systems and reporting
• Ensure compliance with health data regulations and standards
• Strengthen data integrity across health information systems
• Support evidence-based health policy and decision-making
• Reduce errors in health analytics and reporting systems
• Enhance trust in health data-driven insights

By the end of the course, participants will be able to implement data governance frameworks, enforce data quality standards, ensure compliance with health data regulations, and improve the accuracy and reliability of health analytics outputs.

Duration

10 Days

Who Should Attend

• Health data analysts and health information officers
• Monitoring and evaluation professionals in the health sector
• Health informatics specialists
• Public health program managers
• Hospital and healthcare administrators
• Policy makers and health system planners
• Data governance and compliance officers in health organizations

Personal Impact

• Enhances professional competency in managing data quality and compliance

• Strengthens analytical decision-making and data stewardship skills

• Increases confidence in implementing governance frameworks

• Positions participants for strategic roles in digital health transformation

Organizational Impact

• Improves accuracy of health analytics, reporting, and decision-making

• Strengthens data compliance with health regulatory bodies and data privacy laws

• Reduces risks related to data breaches, inaccuracies, and system inefficiencies

• Enhances reliability and trust in data used for health planning and research

By the end of this course, participants will:

• Understand data governance principles and frameworks applicable to health systems

• Assess and improve data quality to support reliable analytics and reporting

• Implement policies and procedures for ethical and compliant data management

• Strengthen data protection, privacy, and security within health analytics workflows

• Develop a structured data governance and quality assurance roadmap

Module 1: Introduction to Data Governance in Health Systems

• Importance of governance in health analytics

• Core components and industry standards

• Case Study: Data governance failure and its impact on clinical outcomes

Module 2: Data Governance Frameworks and Policies

• Developing governance structures and roles

• Policy creation, implementation, and monitoring

• Workshop:Designing a governance framework

Module 3: Data Quality Concepts and Dimensions

• Understanding accuracy, completeness, consistency, and reliability

• Key determinants of data quality in healthcare

• Case Study:Analysis of data errors in hospital patient records

Module 4: Data Quality Assessment and Audit Techniques

• Data validation tools and methodologies

• Conducting health data audits

• Practical Exercise:Performing a data quality check

Module 5: Ethical Data Use and Regulatory Compliance

• Data privacy regulations (GDPR, HIPAA, NHIF policies)

• Ethical data collection and management practices

• Case Study: Legal implications of improper patient data handling

Module 6: Data Protection, Security & Risk Management

• Cybersecurity in health data systems

• Risk assessment and mitigation strategies

• Scenario Simulation:Managing a data breach in a health organization

Module 7: Health Analytics and Data Quality Integration

• Linking data governance to analytical outputs

• Ensuring quality in statistical and predictive modeling

• Case Study:Impact of data governance on disease surveillance analytics

Module 8: Stakeholder Roles and Data Stewardship

• Responsibilities and accountability frameworks

• Integrating departments and system owners

• Group Exercise: Mapping a data stewardship structure

Module 9: Designing and Implementing a Data Governance Strategy

• Blueprint development and execution planning

• Aligning with national health system strategies

• Workshop: Drafting a governance roadmap

Module 10: Monitoring, Evaluation & Continuous Improvement

• Governance performance indicators

• Implementing quality monitoring mechanisms

• Final Action Plan: Developing a long-term quality and governance improvement strategy

• Case Study: Successful implementation in a national health data system