Introduction

Data Governance & Management in Finance is an intensive course designed to equip financial institutions with the knowledge and tools necessary to manage data as a strategic asset. As data volumes grow exponentially and regulatory pressures increase, robust data governance frameworks are vital for operational efficiency, risk management, and compliance. This course guides participants through designing and implementing data governance programs tailored for banking and financial services environments.

The program focuses on key areas such as data ownership, data quality, metadata management, master data management (MDM), and regulatory data compliance. It covers how to establish data stewardship roles, define data standards, and implement controls that ensure accuracy, security, and accessibility of financial data. Learners will explore how governance structures support business intelligence, digital transformation, and regulatory reporting.

Participants will also gain practical experience through general case studies demonstrating how global and regional banks implement governance frameworks to reduce operational risks, ensure compliance with Basel, BCBS 239, GDPR, and support analytics-driven decision-making. The course includes detailed insights on how cloud computing, AI, and automation are shaping the future of enterprise data management in finance.

Whether you are modernizing legacy systems or launching data-driven innovation projects, this course provides the foundational principles and advanced practices needed to master enterprise data governance in financial institutions.

Course Objectives
Understand data governance principles and frameworks
Establish enterprise-wide data ownership and stewardship
Implement data quality and validation procedures
Define and manage metadata effectively
Deploy master and reference data management systems
Align data strategy with regulatory compliance (BCBS 239, GDPR)
Measure data governance maturity and effectiveness
Integrate governance with analytics and reporting tools
Develop governance policies for structured and unstructured data
Leverage AI and automation for smart data governance

Organizational Benefits
Reduce data errors and inconsistencies across systems
Improve regulatory compliance and audit readiness
Enhance decision-making through trusted data
Support cross-functional data collaboration
Optimize risk and financial data reporting
Boost productivity with better data access and integrity
Strengthen cybersecurity and data control measures
Achieve alignment between business and IT
Increase customer trust through transparency and data ethics
Accelerate innovation through reliable data assets

Target Participants
Chief Data Officers (CDOs)
Data governance and management teams
Compliance and risk management professionals
IT and data architecture leads
Data stewards and quality analysts
Financial reporting and analytics teams
Core banking modernization managers
Internal auditors and data privacy officers
Business intelligence professionals
Consultants and digital transformation leaders

Course Outline

Module 1: Introduction to Data Governance
Definition and business value of data governance
Key components of a governance framework
Challenges in financial data management
Data lifecycle management
Stakeholder roles and responsibilities
General case study: Establishing governance in a retail bank

Module 2: Data Ownership and Stewardship
Defining data domains and ownership
Role of data stewards and custodians
Business vs IT responsibilities
Cross-functional collaboration
Data accountability matrix
General case study: Building a data stewardship model

Module 3: Data Quality Management
Dimensions of data quality (accuracy, completeness)
Data profiling and cleansing techniques
Quality metrics and dashboards
Root cause analysis of data issues
Continuous data improvement cycles
General case study: Improving risk reporting accuracy

Module 4: Metadata and Master Data Management
Metadata strategy and tools
Business and technical metadata alignment
Master data governance across systems
Reference data taxonomy
Data lineage and audit trails
General case study: Implementing MDM in compliance reporting

Module 5: Data Policies and Standards
Developing data handling policies
Data access and classification protocols
Standardization of data formats and definitions
Data usage and retention policies
Policy communication and training
General case study: Policy enforcement in a decentralized institution

Module 6: Data Governance Tools and Platforms
Governance tools comparison (Collibra, Informatica)
Tool selection and integration
Dashboards for tracking governance KPIs
Automation of data quality and validation
Scalability and cloud integration
General case study: Deploying governance tools in hybrid IT environment

Module 7: Regulatory Data Compliance
BCBS 239 principles and frameworks
GDPR and personal data handling
Audit trails and regulatory reporting
Compliance heatmaps and scorecards
Regulatory data reconciliation
General case study: Ensuring compliance with BCBS 239

Module 8: Risk Data Aggregation and Reporting
Data governance in risk management
Data consistency across risk systems
Aggregation metrics and risk dashboards
Stress testing and risk simulations
Basel reporting alignment
General case study: Governance for enterprise risk reports

Module 9: Data Ethics and Privacy
Ethical use of financial data
Consent management and data subject rights
Anonymization and pseudonymization
Transparency and disclosure principles
Data ethics governance framework
General case study: Enhancing customer trust with ethical data practices

Module 10: AI & Automation in Data Governance
Machine learning for data quality
Natural language processing for metadata
Automation in data lineage tracking
AI governance and explainability
Intelligent data discovery
General case study: AI-driven metadata management

Module 11: Governance Metrics and Maturity Models
Governance KPIs and benchmarks
Maturity model frameworks (DAMA, CMMI)
Governance health assessments
Roadmap and performance tracking
Continuous improvement loop
General case study: Scaling governance with maturity insights

Module 12: Governance for Digital Transformation
Aligning governance with innovation initiatives
Governance in cloud migration
Data strategy for AI and analytics
Governance for agile projects
Embedding governance in DevOps
General case study: Governance for digital banking rollout

Essential Information

1. Our courses are customizable to suit the specific needs of participants.
2. Participants are required to have proficiency in the English language.
3. Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
4. Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
5. Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
6. Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
7. The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
8. To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
9. For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
10. Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.