Banking Data Quality Management Training Course

Banking Data Quality Management Training Course

Introduction

Banking Data Quality Management Training is designed to help banking and financial services organizations build trusted, accurate, secure, and regulatory-compliant data ecosystems. With the rapid adoption of Digital Banking, Artificial Intelligence (AI), Machine Learning (ML), Open Banking, Cloud Banking, and Real-Time Analytics, maintaining high-quality financial data has become a strategic priority. This course equips professionals with advanced knowledge of Data Governance, Data Quality Frameworks, Master Data Management (MDM), Data Validation, Data Lineage, Metadata Management, Regulatory Reporting, and Risk-Based Data Controls to improve operational efficiency and customer experience.

The program focuses on practical approaches for implementing enterprise data quality strategies, resolving data issues, improving customer and transaction data accuracy, and supporting compliance requirements such as Basel regulations, AML/KYC standards, GDPR principles, and financial risk management frameworks. Participants learn industry best practices through real-world banking scenarios, case studies, and hands-on exercises that enable them to transform poor-quality data into a valuable business asset.

Course Duration

5 days

Course Objectives

By completing this Banking Data Quality Management Training, participants will be able to:

1. Understand the fundamentals of Banking Data Quality Management and Enterprise Data Governance. 
2. Develop effective Data Quality Frameworks aligned with banking business objectives. 
3. Implement Data Governance Models for financial institutions. 
4. Apply advanced Data Profiling and Data Assessment Techniques. 
5. Design automated Data Validation and Data Cleansing Strategies. 
6. Improve Customer Data Quality Management (CDQM) practices. 
7. Manage Master Data Management (MDM) initiatives in banking environments. 
8. Establish strong Metadata Management and Data Lineage Controls. 
9. Build data quality scorecards using Data Quality Metrics and KPIs. 
10. Support Regulatory Compliance and Risk Management Reporting. 
11. Use modern technologies including AI-driven Data Quality Analytics and Automation. 
12. Implement continuous improvement strategies using Data Quality Monitoring Frameworks. 
13. Create a sustainable Data Excellence Culture across banking organizations. 

Target Audience

1. Banking Data Management Professionals 
2. Data Governance Managers and Officers 
3. Chief Data Officers (CDOs) and Data Strategy Leaders 
4. Banking Risk and Compliance Professionals 
5. Business Intelligence and Analytics Professionals 
6. Database Administrators and Data Engineers 
7. Digital Transformation and IT Managers 
8. Financial Services Consultants and Auditors 

Course Modules

Module 1: Fundamentals of Banking Data Quality Management

• Introduction to Banking Data Quality Concepts and Principles
• Importance of high-quality data in Digital Banking Transformation
• Data quality dimensions: accuracy, completeness, consistency, validity, and timeliness 
• Role of data quality in customer experience and operational excellence 
• Case Study: Improving customer data accuracy in a global retail bank 

Module 2: Banking Data Governance Frameworks

• Designing an effective Enterprise Data Governance Strategy
• Data ownership, stewardship, and accountability models 
• Creating banking data governance policies and standards 
• Aligning governance with regulatory requirements 
• Case Study: Implementing data governance at a multinational financial institution 

Module 3: Data Profiling and Data Quality Assessment

• Techniques for identifying banking data quality issues 
• Data profiling methods for customer, transaction, and account data 
• Detecting duplicate, missing, and inconsistent records 
• Establishing data quality assessment frameworks 
• Case Study: Reducing duplicate customer records through profiling analytics 

Module 4: Data Cleansing and Data Remediation Strategies

• Data cleansing methodologies for financial databases 
• Designing data correction and remediation workflows 
• Improving customer and transaction master data 
• Automation of data quality improvement processes 
• Case Study: Resolving inaccurate customer information affecting AML compliance 

Module 5: Master Data Management (MDM) in Banking

• Fundamentals of Banking Master Data Management
• Customer, product, and account master data management 
• Creating a single customer view
• MDM integration with CRM and core banking systems 
• Case Study: Building a unified customer data platform for digital banking 

Module 6: Metadata Management and Data Lineage

• Understanding metadata-driven data management 
• Creating end-to-end banking data lineage models 
• Tracking data movement across banking applications 
• Supporting audit, compliance, and reporting requirements 
• Case Study: Improving regulatory reporting accuracy through data lineage 

Module 7: AI, Automation, and Advanced Data Quality Analytics

• Applying Artificial Intelligence in data quality monitoring 
• Machine Learning approaches for anomaly detection 
• Automated data quality rules and alerts 
• Predictive analytics for identifying data risks 
• Case Study: Using AI-powered monitoring to detect fraudulent transaction data patterns 

Module 8: Regulatory Compliance and Data Quality Excellence

• Data quality requirements for banking regulations 
• Supporting AML, KYC, Basel, and risk reporting processes 
• Building continuous data quality monitoring frameworks 
• Developing enterprise data quality dashboards 
• Case Study: Creating a regulatory-ready data quality operating model 

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.