DATA QUALITY MANAGEMENT
“Improving Data Accuracy, Consistency, and Reliability for Better Business Intelligence and Compliance”
Course Schedule
| Date | Venue | Fees (Face-to-Face) |
|---|---|---|
| 21 – 23 Jan 2026 | Kuala Lumpur, Malaysia | USD 2495 per delegate |
| 03 – 05 Mar 2026 | Doha, Qatar | USD 2495 per delegate |
Course Introduction
High-quality data is a cornerstone of effective decision-making, regulatory compliance, customer satisfaction, and digital transformation. Yet, many organizations struggle with incomplete, outdated, inconsistent, or duplicate data—undermining the value of analytics, AI, and strategic planning.
This intensive three-day course helps professionals build the skills and systems to manage and improve data quality across departments. Covering the full data quality lifecycle—from profiling to cleansing, monitoring, and governance—this program blends strategy with practical tools for long-term success.
Course Objectives
By the end of this course, participants will be able to:
- Understand key dimensions of data quality and why they matter.
- Perform data profiling, validation, and root cause analysis.
- Design data quality rules, scorecards, and monitoring frameworks.
- Implement cleansing and de-duplication strategies across systems.
- Align data quality practices with business needs, compliance, and governance.
- Foster a culture of data ownership and continuous improvement.
Key Benefits of Attending
- Avoid the cost and risk of poor data in reporting, analysis, and operations.
- Ensure data accuracy, consistency, and trust across systems and teams.
- Support regulatory requirements such as GDPR, ISO 8000, and internal audits.
- Improve the ROI of digital tools, ERP, CRM, and analytics initiatives.
- Build sustainable data quality practices in your organization.
Intended Audience
This program is designed for:
- Data stewards, analysts, and data quality specialists
- IT and data governance professionals
- BI/reporting teams and master data managers
- Compliance and internal audit staff
- Functional users responsible for critical business data
Individual Benefits
Key competencies that will be developed include:
- Data profiling and root cause identification
- Data quality measurement and reporting
- Cleansing, standardization, and enrichment
- Metadata and master data alignment
- Data governance and stakeholder engagement
Organization Benefits
Upon completing the training course, participants will demonstrate:
- Higher accuracy and trust in business reporting and KPIs
- Reduced data-related errors, rework, and compliance risks
- Improved integration across systems (ERP, CRM, BI)
- Better customer experience through clean, consistent data
- A sustainable approach to data ownership and stewardship
Instructional Methdology
The course follows a blended learning approach combining theory with practice:
- Conceptual Briefings – Data lifecycle, quality framework, and strategy
- Templates – Data quality dashboards, cleansing logs, issue trackers
- Case Studies – Real-world DQ failures and turnaround stories
- Excel Simulations – Profiling, scoring, and validation tools
- Group Exercises – Cleansing rule design and stakeholder planning
- Peer Feedback – Sharing experiences and defining best practices
Course Outline
Detailed 3-Day Course Outline
Training Hours: 7:30 AM – 3:30 PM
Daily Format: 3–4 Learning Modules | Coffee breaks: 09:30 & 11:15 | Lunch Buffet: 01:00 – 02:00
Day 1: Data Quality Principles and Profiling
- Module 1: Understanding Data Quality (07:30 – 09:30)
- What is data quality and why it matters?
- Business, compliance, and technical impacts
- Key data quality dimensions: accuracy, completeness, consistency, etc.
- Module 2: Data Profiling and Risk Assessment (09:45 – 11:15)
- Profiling techniques: frequency, uniqueness, nulls, and outliers
- Identifying common issues and priority datasets
- Tools and platforms for data profiling (Excel, SQL, DQ tools)
- Module 3: Root Cause and Process Mapping (11:30 – 01:00)
- Tracing data issues to source processes
- Data entry, integration, and transformation points
- Roles and responsibilities in the data chain
- Module 4: Workshop – Profiling & Issue Log Creation (02:00 – 03:30)
- Participants profile sample data and document data quality risks
Day 2: Cleansing, Monitoring, and Controls
- Module 5: Cleansing and Standardization Techniques (07:30 – 09:30)
- De-duplication, merging, formatting, and enrichment
- Address, name, and date cleaning standards
- Managing reference and master data consistency
- Module 6: Designing Data Quality Rules (09:45 – 11:15)
- Business rule logic and exception handling
- Validation at entry, batch, and system levels
- Creating reusable rules in spreadsheets or tools
- Module 7: Monitoring and Data Quality Scorecards (11:30 – 01:00)
- Setting up KPIs and thresholds
- Scorecards, dashboards, and exception reporting
- DQ trend analysis and reporting to leadership
- Module 8: Group Simulation – Cleansing Plan Design (02:00 – 03:30)
- Teams define a cleansing and monitoring strategy for a selected dataset
Day 3: Governance, Ownership, and Continuous Improvement
- Module 9: Embedding Data Governance and Stewardship (07:30 – 09:30)
- Governance structure: data owners, stewards, and custodians
- Defining roles and building accountability
- Data quality in the context of data governance frameworks
- Module 10: Sustaining Data Quality Programs (09:45 – 11:15)
- Change management and user training
- Root cause tracking and process reinforcement
- Integrating DQ in projects and business processes
- Module 11: Case Review and Business Alignment (11:30 – 01:00)
- Learning from real-world data quality improvement programs
- Aligning DQ with BI, analytics, and compliance goals
- Gaining leadership buy-in and stakeholder support
- Module 12: Final Exercise – Data Quality Roadmap & Wrap-Up (02:00 – 03:30)
- Participants build a DQ improvement roadmap for their organization
Certification
Participants who complete the program will receive a Certificate of Completion in Data Quality Management, recognizing their readiness to lead, manage, and improve data quality initiatives in alignment with business priorities.