Training course

Overview

Data Quality Management is a professional five-day training course designed to provide participants with the knowledge, frameworks, tools, and practical techniques required to establish, monitor, improve, and sustain high-quality organizational data. Reliable data is essential for effective reporting, analytics, operational control, compliance, customer management, financial planning, and strategic decision-making. This course provides a structured approach to managing data quality throughout the data lifecycle, helping organizations identify quality problems, understand their root causes, establish measurable standards, and implement sustainable controls.

The course introduces the fundamental dimensions of data quality, including accuracy, completeness, consistency, validity, uniqueness, timeliness, integrity, and relevance. Participants learn how to assess data quality through profiling, validation, reconciliation, sampling, exception analysis, quality metrics, and scorecards. Practical tools such as data-quality checklists, business rules, data dictionaries, profiling templates, issue registers, root-cause analysis, quality dashboards, and corrective-action plans are incorporated to help participants translate data-quality principles into practical workplace processes.

Through case studies, exercises, simulations, and real-world scenarios, participants examine common causes of poor data quality and develop systematic approaches for preventing recurring problems. The course covers data governance, data stewardship, master data, metadata, data lineage, data standards, quality controls, monitoring processes, issue management, and continuous improvement. Participants also explore how data quality can be integrated with risk management, internal controls, compliance, process improvement, and digital transformation initiatives across different organizational functions.

By the end of the Data Quality Management course, participants will be able to design and implement practical data-quality management processes that support reliable operational and strategic information. Participants will develop skills for assessing current data-quality performance, establishing quality rules and thresholds, prioritizing data-quality issues, investigating root causes, implementing corrective and preventive actions, and monitoring improvement over time. The course culminates in a practical data-quality management project through which participants develop an integrated framework and improvement roadmap that can be adapted to their organization's data environment.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data quality analysts and data management professionals

• Data governance and data stewardship professionals

• Business analysts and reporting specialists

• Data analysts and business intelligence professionals

• IT, information management, and digital transformation teams

• Finance, HR, operations, sales, marketing, and customer data professionals

• Quality assurance and internal control professionals

• Risk, compliance, and audit personnel involved in data quality

• Managers and supervisors responsible for organizational data accuracy

Course Objectives

By the end of the training, participants will be able to:

• Explain the principles, dimensions, and business value of data quality

• Identify common causes, symptoms, and consequences of poor data quality

• Assess data quality using structured profiling, validation, sampling, and measurement techniques

• Define data-quality requirements, business rules, standards, and acceptance criteria

• Develop practical data-quality metrics, KPIs, thresholds, and scorecards

• Identify and prioritize critical data-quality issues based on business impact and risk

• Apply data cleansing, standardization, validation, reconciliation, and verification techniques

• Establish effective data governance, ownership, stewardship, and accountability structures

• Apply root-cause analysis and corrective-action techniques to recurring data-quality problems

• Develop data-quality monitoring, reporting, escalation, and issue-management processes

• Use data dictionaries, metadata, lineage, and business rules to support data consistency

• Integrate data-quality controls with organizational risk management and internal control frameworks

• Apply continuous improvement techniques to data-quality management

• Evaluate technology and automation opportunities for data-quality monitoring and remediation

• Develop a comprehensive data-quality management framework and improvement roadmap

Course Content

Day 1: Foundations of Data Quality Management

Module 1: Principles, Dimensions, and Business Value of Data Quality

Topics

1. Introduction to Data Quality Management
2. The Strategic and Operational Importance of High-Quality Data
3. Understanding the Data Lifecycle and Quality Management Lifecycle
4. Data Quality Dimensions: Accuracy, Completeness, Consistency, Validity, Uniqueness, Timeliness, Integrity, and Relevance
5. Common Sources and Causes of Data Quality Problems
6. Business, Financial, Operational, Compliance, and Customer Impacts of Poor Data Quality
7. Data Quality Requirements, Expectations, and Acceptance Criteria
8. Data Quality Roles, Responsibilities, Ownership, and Accountability
9. Data Quality Policies, Standards, Procedures, and Business Rules
10. Practical Exercise: Assessing the Quality of a Real-World Organizational Dataset

Day 2: Data Profiling, Measurement, and Assessment

Module 2: Data Quality Assessment and Measurement

Topics

1. Principles and Methods of Data Profiling
2. Designing Data Quality Assessment Procedures
3. Identifying Missing, Duplicate, Invalid, Inconsistent, and Anomalous Data
4. Sampling Techniques for Data Quality Reviews
5. Data Validation, Verification, and Reconciliation Techniques
6. Developing Data Quality Rules and Automated Validation Checks
7. Data Quality Metrics, KPIs, Thresholds, and Tolerance Levels
8. Creating Data Quality Scorecards and Management Dashboards
9. Data Quality Issue Registers, Exception Reporting, and Escalation Procedures
10. Case Study: Conducting a Comprehensive Data Quality Assessment and Reporting Findings

Day 3: Data Governance, Controls, and Issue Management

Module 3: Governance and Control of Data Quality

Topics

1. Data Governance Principles and Their Relationship with Data Quality
2. Data Stewardship, Ownership, Custodianship, and Accountability
3. Establishing Data Quality Governance Structures and Operating Models
4. Data Dictionaries, Metadata, Business Glossaries, and Standard Definitions
5. Data Lineage, Traceability, and Lifecycle Controls
6. Master Data Management and Critical Data Elements
7. Data Quality Controls, Preventive Controls, Detective Controls, and Corrective Controls
8. Data Quality Issue Management, Prioritization, and Escalation
9. Root Cause Analysis Using 5 Whys, Fishbone Analysis, and Related Techniques
10. Practical Exercise: Investigating a Recurring Data Quality Failure and Developing Corrective Actions

Day 4: Remediation, Automation, and Continuous Improvement

Module 4: Advanced Data Quality Improvement and Technology

Topics

1. Data Cleansing, Standardization, Transformation, and Remediation Strategies
2. Managing Duplicate Records, Conflicting Values, and Data Exceptions
3. Designing Corrective and Preventive Action Plans
4. Monitoring Data Quality Across Multiple Systems and Data Sources
5. Automating Data Quality Rules, Validation, and Monitoring
6. Using Excel, SQL, Power Query, and Data Quality Platforms for Quality Management
7. Data Quality in Cloud, Integrated, and Enterprise Data Environments
8. Applying Lean, PDCA, Six Sigma, and Continuous Improvement Principles to Data Quality
9. Evaluating Data Quality Maturity and Developing Improvement Priorities
10. Practical Simulation: Designing an Automated Data Quality Monitoring and Remediation Process

Day 5: Enterprise Data Quality Strategy and Implementation

Module 5: Strategic Data Quality Management and Organizational Excellence

Topics

1. Designing an Enterprise Data Quality Management Framework
2. Establishing Data Quality Strategy, Objectives, Standards, and Governance
3. Prioritizing Data Quality Initiatives Using Risk, Business Impact, and Value
4. Developing Enterprise Data Quality Scorecards and Executive Reporting
5. Integrating Data Quality with Risk Management, Compliance, Audit, and Internal Controls
6. Building Data Quality Culture, Accountability, and Organizational Capability
7. Establishing Sustainable Data Quality Monitoring and Review Processes
8. Developing Data Quality Maturity Models and Continuous Improvement Roadmaps
9. Capstone Exercise: Designing a Complete Data Quality Management Framework for an Organization
10. Final Case Study, Practical Assessment, and Data Quality Improvement Action Plan