About the Course

Banks generate vast amounts of data, yet many struggle to govern, analyse and use it effectively for decisions, risk management and customer insight.

This ten-day programme covers data governance, data quality, analytics techniques, business intelligence and dashboard development, and data-driven decision-making across banking functions.

Participants leave with a data governance framework, an analytics use-case pipeline and a working dashboard.

Target Participants

Data governance and data management staff, Business intelligence and analytics teams, IT and MIS staff, Risk and finance analysts, and product and strategy teams.

What You Will Learn

By the end of this course, participants will be able to:

• Analyse data governance frameworks and their application to banking.
• Assess data quality management practices.
• Apply analytics techniques to banking data.
• Design business intelligence dashboards.
• Evaluate data privacy and security requirements.
• Build a data-driven decision-making process.
• Interpret analytics outputs for business decisions.
• Develop an analytics use-case pipeline.
• Prepare an institutional data strategy.

Course Duration

• Classroom – 10 Days
• Online – 14 Days

Course Outline

Foundations of Data Governance

• Data governance concepts
• Data governance frameworks
• Roles/responsibilities
• Maturity models
• Regulatory data requirements

Data Quality Management

• Data quality dimensions
• Data profiling techniques
• Data cleansing approaches
• Data quality monitoring
• Data quality metrics

Data Architecture and Management

• Data architecture basics
• Data warehouses/lakes
• Master data management
• Data integration approaches
• Data lifecycle management

Data Privacy and Security

• Data protection regulations
• Data classification
• Access control principles
• Data breach response
• Privacy by design

Introduction to Banking Analytics

• Analytics types overview
• Descriptive vs predictive analytics
• Analytics use cases in banking
• Analytics maturity assessment
• Building an analytics culture

Customer Analytics

• Customer segmentation analytics
• Churn prediction basics
• Cross-sell/upsell analytics
• Customer lifetime value
• Personalization analytics

Credit and Risk Analytics

• Credit scoring analytics
• Portfolio risk analytics
• Early warning analytics
• Fraud analytics overview
• Stress testing analytics

Financial and Operational Analytics

• Profitability analytics
• Cost analytics
• Branch performance analytics
• Operational efficiency analytics
• Liquidity analytics

Business Intelligence Tools and Techniques

• BI tool landscape
• Dashboard design principles
• Data visualization best practice
• Report automation
• Self-service BI

Building Dashboards and Reports

• Dashboard requirements gathering
• KPI selection
• Dashboard build exercise
• User testing/feedback
• Dashboard maintenance

Advanced Analytics and Machine Learning Basics

• Machine learning concepts
• Predictive modelling basics
• Model validation overview
• Explainability considerations
• Advanced analytics use cases

Data-Driven Decision-Making

• Decision-making frameworks
• Data storytelling techniques
• Presenting analytics to executives
• Overcoming data skepticism
• Embedding analytics in processes

Data Strategy and Roadmapping

• Data strategy components
• Prioritizing data initiatives
• Data investment planning
• Change management for data culture
• Data strategy governance

Data Ethics and Responsible Analytics

• Ethical data use
• Bias in analytics
• Transparency/explainability
• Responsible AI/analytics practices
• Building trust in data

Building Institutional Data Capability

• Data skills assessment
• Data team structure design
• Analytics centre of excellence models
• Vendor/tool selection
• Institutional data capability roadmap

Practical Outputs

By the end of this course, participants will have developed the following practical outputs:

• Data governance framework
• Data quality assessment tool
• Analytics use-case pipeline
• Working BI dashboard
• Data strategy roadmap
• Data privacy checklist
• Data capability assessment

Training Approach

Combines technical instruction with hands-on dashboard-building exercises and data strategy workshops using spreadsheet and BI tools.

Organizational Benefits

• Improved data-driven decision-making
• Stronger data governance and quality
• Enhanced customer and risk analytics
• Better regulatory data compliance
• Improved reporting efficiency
• Stronger data privacy protection
• Clearer data investment priorities

Certification

Participants who successfully complete this course will receive a Certificate of Course Completion from Devimpact Institute, recognizing the knowledge, practical skills and competencies developed through the programme.

Tailor-Made Course

This course can be customized and delivered as an in-house programme to address the specific policies, systems, sector context and capacity-development priorities of an organization. The course duration, modules, case studies and practical exercises may be adjusted following a training needs assessment.