Introduction

The modern financial landscape is increasingly powered by vast, complex datasets, making effective risk modeling more dependent than ever on high-quality, trustworthy data. However, the sheer volume, velocity, and variety of big data present significant challenges, including data quality issues, privacy concerns, and a lack of clear ownership. Big data governance in risk modeling is the strategic framework that addresses these challenges, ensuring that the data used to assess and manage risk is accurate, secure, compliant, and auditable. This Data Guardian: Training Course on Big Data Governance in Risk Modeling is a 10-day intensive program designed to equip you with the skills to build and manage a robust data governance framework.

This course will guide you through the principles and practical steps required to establish a strong data governance program, tailored specifically for the demands of risk modeling. You will learn how to define data quality standards, implement data lineage and traceability, and navigate the complex web of regulatory requirements. By the end of this training, you will be able to transform raw, messy data into a reliable and strategic asset, enabling more accurate and transparent risk assessments and fostering greater trust in your models and decisions.

Duration: 10 Days

Target Audience

• Data governance officers
• Risk model developers
• Data quality analysts
• IT professionals in finance
• Compliance and audit managers
• Chief Data Officers (CDOs)
• Financial regulators
• Data scientists
• Business analysts
• Project managers

Objectives

• Understand the core concepts of data governance.
• Identify the specific challenges of big data in risk modeling.
• Learn to define and measure data quality.
• Master the principles of data lineage and traceability.
• Understand the regulatory landscape for data in finance.
• Develop a data governance framework for a financial institution.
• Implement data security and privacy protocols.
• Learn to audit data governance processes.
• Foster a data-driven culture within an organization.
• Ensure model integrity and explainability through good data governance.

Course Modules

Module 1: Introduction to Data Governance

• What is data governance and why is it crucial?
• The difference between data management and data governance
• Key components of a data governance framework
• The role of data stewards and data owners
• Aligning data governance with business strategy

Module 2: Big Data in Financial Risk Modeling

• The volume, velocity, and variety of financial data
• The challenges of unstructured data
• The importance of data for risk assessment
• How big data can enhance risk models
• The risks of using poor quality data

Module 3: Data Quality Frameworks

• Defining data quality dimensions
• Establishing data quality metrics and rules
• Building a data cleansing and validation process
• The use of data profiling tools
• Continuous monitoring of data quality

Module 4: Data Lineage and Traceability

• What is data lineage and why is it important?
• Mapping the journey of data from source to model output
• Tools and techniques for data lineage
• The importance of traceability for model validation
• Creating a data dictionary and business glossary

Module 5: Regulatory and Compliance Requirements

• An overview of key regulations (e.g., GDPR, CCPA)
• The role of regulations in data governance
• Specific requirements for financial institutions
• The impact of regulatory audits on data
• Ensuring data is used ethically and legally

Module 6: Building a Data Governance Program

• Gaining leadership buy-in and stakeholder support
• Defining a clear scope and objectives
• Assembling a cross-functional data governance committee
• Creating and communicating policies and standards
• Measuring the success of the program

Module 7: Data Security and Privacy

• The principles of data security
• Implementing access controls and encryption
• The role of data masking and tokenization
• Protecting sensitive customer information
• Responding to data breaches and incidents

Module 8: Master Data Management (MDM)

• The importance of a single source of truth
• Identifying and managing critical master data
• Building a golden record for key entities
• Synchronizing data across different systems
• Ensuring consistency and accuracy across the organization

Module 9: Model Risk Management and Data

• The concept of model risk
• The role of data in model validation
• Data issues that can lead to model errors
• Ensuring model explainability and transparency
• The governance of model inputs and outputs

Module 10: Capstone Project Part 1: Assessment

• Assessing the current state of data governance
• Identifying key data challenges in a sample case study
• Analyzing data quality and lineage issues
• Outlining a plan to address the identified problems
• Preparing a clear and concise project plan

Module 11: Capstone Project Part 2: Implementation

• Building a simple data governance plan
• Implementing a data quality rule on a dataset
• Documenting data lineage for a specific risk model
• Creating a policy for data access and use
• Presenting the plan to a mock stakeholder group

Module 12: Capstone Project Part 3: Reporting

• Presenting the final project and its findings
• Explaining the impact of good data governance
• Discussing the challenges and future improvements
• Q&A and peer feedback session
• Receiving expert recommendations and insights

Module 13: Change Management and Culture

• The human side of data governance
• Fostering a data-driven culture
• Training and education for employees
• Communicating the value of governance
• Sustaining the program over time

Module 14: Data Governance Technology

• An overview of data governance tools
• The role of metadata management solutions
• Data catalogs and their importance
• Automation in data quality and governance
• The future of AI in data governance

Module 15: Course Certification

• A final comprehensive assessment
• A final review of key concepts and objectives
• Issuance of a certificate of completion
• Post-course career guidance and networking
• A final Q&A with instructors

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.