WSQ - Data Quality Management Framework

Topic 1: Introduction to Data Quality Management Framework 

• What is data quality?
• Overview of data quality management framework
• Impact of poor data quality
• Data quality dimensions
• Potential threats to data quality

Topic 2: Measuring Data Quality 

• Data quality rules
• Data quality processes
  • data profiling
  • data parsing
  • data standardization
  • identity resolution
  • data linkage
  • data cleansing
  • data enhancement
  • data inspection and monitoring
• Metrics for measuring data quality.
• Trade-offs in data dimensions

Topics 3: Design Strategies for Maximizing  Data Quality 

• Data quality roles
• Data quality improvement process
• Data quality techniques and tools
• Data quality governance
• Data quality best practices.
• Data quality maximization strategies.

Final Assessment

• Written Assessment (SAQ)
• Case Study

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

• Positive Learning Attitude
• Enthusiastic Learner

Experience

• Minimum of 1 year of working experience.

Target Year Group : 21-65 years old

Minimum Software/Hardware Requirement

Software:

You will need a AWS account (Credit Card is required).

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• Data Quality Analyst
• Data Quality Specialist
• Data Governance Specialist
• Data Analyst
• Business Intelligence Analyst
• Data Scientist
• Data Steward
• Database Administrator (DBA)
• Data Management Consultant
• Data Integrity Specialist
• Data Compliance Officer
• Data Engineer
• Master Data Management (MDM) Specialist
• Information Management Specialist
• Risk and Compliance Analyst
• Quality Assurance (QA) Analyst
• Systems Analyst
• Reporting Analyst
• IT Auditor
• Data Operations Specialist

Dr. Alvin Ang: Dr. Alvin Ang is an experienced data governance and digital transformation specialist with over 20 years of experience in data analytics, information management, and IT strategy. He holds a PhD in Information Systems and has worked extensively with both government agencies and private enterprises to implement data management frameworks that enhance accuracy, consistency, and compliance across organizations. His expertise spans master data management, data quality governance, and data lifecycle optimization, supported by his strong background in data analytics and enterprise systems integration.

In the Data Quality Management Framework course, Dr. Ang brings a strategic perspective on how organizations can structure, assess, and maintain data integrity across business processes. His sessions focus on developing policies, controls, and frameworks that ensure data accuracy and usability for analytics and decision-making. Learners benefit from his practical guidance on implementing enterprise-level data governance models that align with both regulatory requirements and organizational objectives.

Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is a data scientist and analytics leader with extensive experience in data quality management, predictive modelling, and business intelligence. As Head of Data Science at Plano Pte Ltd, he has led teams in developing data-driven solutions and implementing machine learning frameworks to enhance organizational decision-making. Dwight is also an accomplished trainer and consultant who has designed and delivered data analytics and governance courses for corporates and government agencies. His technical expertise includes Python, R, Keras, and SQL, with a strong foundation in data architecture and quality assurance.

In this course, Dwight helps participants understand the importance of data quality in driving reliable analytics and business outcomes. His sessions focus on establishing data validation mechanisms, monitoring pipelines, and developing quality metrics for structured and unstructured data. Learners gain the skills to implement and maintain robust data quality management systems that support accuracy, compliance, and scalability across enterprise data ecosystems.

Terence Ee: Terence Ee is a senior IT governance and management professional with over 25 years of experience in information systems, data security, and digital transformation. He has held leadership roles including Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he oversaw IT operations, compliance, and data governance strategies. Terence holds a Master of Science in Technology Management and is a certified ACLP trainer recognized for his expertise in implementing enterprise governance and process frameworks.

In the Data Quality Management Framework course, Terence focuses on the governance and organizational aspects of data quality management. His sessions guide leaders in developing effective policies, audit mechanisms, and accountability structures for sustainable data stewardship. Participants learn how to embed data quality standards into corporate governance systems, ensuring reliability, transparency, and long-term business value from data assets.

Dr. Alfred Ang: Dr. Alfred Ang is a distinguished technologist, researcher, and entrepreneur with over 25 years of experience in information systems, data management, and digital transformation. He holds a PhD in Electrical Engineering from the National University of Singapore, an MEng from NTU, and an MBA from Universitas 21 Global. As the Founder and Managing Director of Tertiary Infotech Pte. Ltd., Dr. Ang has designed and delivered over 130 accredited courses in data analytics, AI, and governance. His extensive background spans software engineering, business intelligence, and enterprise data management frameworks.

In this course, Dr. Ang provides a comprehensive understanding of the principles and practices of data quality management. His sessions emphasize establishing governance models, data validation workflows, and continuous improvement processes that ensure accuracy, consistency, and completeness of organizational data. Learners gain practical insights into building and maintaining data governance systems that support analytics reliability and regulatory compliance.