Data Management in Banking 101

Name: Data Management in Banking 101
Rating: 4.5 (27 reviews)

... from Data Governance, Infrastructure, Metadata & Integration strategies to BI Reporting, AI use cases & other trends

Created byJörg Marinko

Last updated 5/2026

English

English [Auto],

What you'll learn

Understand the Fundamentals of data management, and why it’s vital for banking transformation
Learn how Data Governance and Regulatory Compliance (e.g., BCBS 239, GDPR) shape data handling in financial institutions
Explore core elements of Banking Data Infrastructure, including data architecture, modelling approaches, and storage models
Dive into Metadata Management, with practical emphasis on data quality, data lineage, and data integration
See how banks turn raw data into insights through Business Intelligence (BI) reporting
Examine real-world Data Analytics and AI Use Cases—from fraud detection and credit risk modeling to customer personalization

Course content

6 sections • 38 lectures • 2h 29m total length

What is this course about?2:27
Master data management in banking by exploring data governance, regulatory compliance, data architecture, and modern storage models like data mesh and data fabric, plus data quality, lineage, integration, and analytics.
Why is data crucial in banking?3:55
Understand how data powers decision making, risk management, fraud detection, AML, and regulatory compliance in banking to improve customer service and open banking innovations.
What are the types of data in banking?1:08
Data Management & Data Governance - how do these concepts reinforce each other?7:19
Data management and data governance reinforce each other to secure, accurate, and accessible banking data for compliant analytics and reporting.
What does poor Data Management entail and how to overcome these challenges?3:47
Poor data management in banking leads to penalties, breaches, and bad customer experiences. Implement centralized data platforms, governance, automated compliance, and ai-driven data cleansing for accurate, secure, and integrated data.
Data Management in the light of Regulatory Compliance & Risk Management5:39
Explore how regulatory compliance and risk management shape data governance in banking, from Basel III and BCBS 239 to GDPR, CCPA, FATF, AML directives, and DORA, across policy to controls.
❓ Quick check

How does a typical (high-level) Banking Data Infrastructure look like?7:01
Explore a typical high level banking data infrastructure, from core systems and third‑party data sources through ETL/ELT pipelines, data warehouses and lakes, analytics, AI, governance, and secure access.
Key Functions and types of (modern) Core Banking Systems (CBS)7:15
Explore the core banking system (CBS) architecture, covering customer account management, real-time transaction processing, loans and credit management, payments, risk and compliance, and digital banking integration.
Data Warehousing and Data Lakes in Banking5:23
Compare data warehouses and data lakes in banking, explaining structured versus unstructured data, schema on write versus schema on read, and their regulatory reporting, analytics, and real time insights.
Legacy vs. Modern Banking Data Infrastructure
❓ Quick check

Master Data Management (MDM) in Banking5:33
Master data management unifies banking data into a golden record, enabling a single source of truth, trusted analytics, and compliant, real-time data integration across channels.
Data Quality Management in Banking5:26
Establish a measurable data quality framework in banking by defining standards, metrics, and ownership, then implement automated validation, cross-system integration, and real-time monitoring for regulatory compliance and risk management.
Data Integration and Interoperability6:23
Data integration unifies core banking, CRM, risk, compliance, and third-party data to enable real time exchange, a 360-degree customer view, AI driven insights, and regulatory compliance.
Wrap-Up: Technical Blueprint for Banking Data Integration0:59
Data Models - Why do they matter in Banking?3:56
Explore why data models and schemas matter in banking, covering conceptual, logical, and physical types, data integration, and Basel III and GDPR compliance.
Example - (Simplified) Banking Data Model4:58
Explore conceptual, logical, and physical data models in banking, detailing core entities: customer, account, loan, collateral, and how their attributes map to SQL tables.
Wrap-Up: Data Models in Banking - A Three-Step Approach
Additional Lecture: BCBS 239 - Five-Step Best Practice implementation guide1:45
Additional Lecture: Data Lineage Best practices implementation1:05
❓ Quick check

Differences and Overlaps between BI, Data Analytics, ML & AI3:10
Discover how BI, data analytics, ML, and AI differ and overlap in banking, with use cases from profitability reports to fraud detection, churn forecasting, and automated decisions.
Excursus: Artificial Intelligence at a glance6:38
Explore artificial intelligence at a glance, covering machine learning, deep learning, NLP, computer vision, and reinforcement learning, and trace AI from rule-based systems to narrow AI, AGI, and beyond.
Overview use cases - Reporting, Analytics, Intelligence, Prediction & Automation7:11
Explore how banks collect and integrate data from core systems, CRM, and channels to enable reporting, analytics, intelligence, prediction, and automation, with governance and risk, liquidity, and profitability insights.
Overall coverage of (potential) use cases
Use case at glance – BI (Business Intelligence) Reporting in Banking4:03
Explore three pillars of banking BI reporting: risk management, steering and performance, and regulatory compliance; learn how real-time dashboards, data lineage, and multidimensional analysis support risk, profitability, and compliance.
Use case at glance - Predictive Analytics and Credit Risk Modeling2:41
Leverage predictive analytics and credit risk modeling with historical and real time data to forecast defaults, optimize underwriting, and ensure regulatory compliance.
Use case at glance - Predictive Analytics and Credit Risk Modeling - Example1:43
Use case at glance - Data-Driven Approach of Managing Financial Crime Risks in B5:10
Use data analytics, AI, and automation to strengthen AML, KYC, and real-time monitoring, detecting and preventing financial crime in banking.
Use case at glance - Customer Data Analytics and Personalization4:41
Leverage customer data analytics and personalization in banking to deliver tailored products and experiences using a 360-degree view, segmentation, and ai-powered predictions.
Additional Lecture: Challenges and Trade-offs in AI adoption3:51
Identify the key challenges and trade-offs of ai adoption in banking, including data privacy and security, data quality and integration, governance, bias, and regulatory constraints.
❓ Quick check

Data Security and Privacy in Banking3:51
Data Fabric ("Hybrid Multi-Cloud") & Data Mesh ("Data Products") Architectures4:27
Compare data fabric and data mesh for banking. Data fabric centralizes real-time integration across hybrid multi-cloud environments with unified metadata, while data mesh decentralizes ownership to domains, enabling data products.
Cloud Computing and Modular Banking5:48
Explore how cloud computing and modular banking transform modern banks by enabling scalable, flexible service-based architectures with API-connected modules and rapid deployment.
Modular Banking Stack: APIs, Open Banking, and Banking-as-a-Service4:33
Cloud Migration at Scale – What are the trade-offs?4:05
Explore the cloud and on premise trade-offs in banking data management, balancing control, agility, security, cost, and compliance through cloud native innovation, hybrid strategies, and migration governance.
Distributed Ledger Technology & Blockchain7:02
❓ Quick check

Summary - Developing a Data-Driven Strategy in Banking2:09
Build a data driven banking culture with governance, data quality, and compliance for Basel III, GDPR, AML; leverage data lakes and AI with dashboards and KPIs.
Future of Data Management in Banking3:42
Explore how digital transformation drives proactive, real-time data use in banking, powered by cloud, APIs, and data fabric. Learn about AI-driven governance, privacy by design, and data monetization across ecosystems.
THANK YOU!!!0:23

Requirements

No prerequisites

Description

What is this course about?

In today’s data-driven financial landscape, data is the backbone of modern banking operations —powering everything from regulatory compliance and risk management to customer insights and digital innovation. As banking transforms under the weight of new technologies and increasing regulatory demands, effective data management is no longer optional—it's mission-critical.

Why should you sign up?

This course is designed for professionals who want to bridge the gap between data, technology, and banking operations. Whether you're working in compliance, risk, IT, analytics, or business strategy, you'll gain the tools and knowledge to navigate the complexities of banking data with confidence and strategic insight. Or to be more specific:

Gain In-Demand Skills – Master the fundamentals of data architecture, governance, and analytics in banking
Enhance Career Opportunities – Stand out in the banking and fintech industry with specialized knowledge in data-driven decision-making
Stay Compliant & Secure – Learn how banks manage data while adhering to regulations. GDPR, Basel III, BCBS 239, and AML
AI & Big Data Readiness – Understand how AI, machine learning, and data lakes are transforming financial institutions

What will the course format look like?

Expect hands-on examples, architecture diagrams, real case studies, and regulatory insights—all structured to give you practical skills and strategic understanding to lead or contribute to data initiatives in your bank.

This course is your gateway to becoming a data-savvy banking professional.

Who this course is for:

Banking & finance professionals looking to enhance their data management skills
IT & data professionals working in banking technology or fintech
Students and aspiring professionals looking to enter the data-driven finance sector

Data Management in Banking 101

What you'll learn

Explore related topics

Course content

Introduction, Broader Context and Rationale6 lectures • 24min

Data Architecture (Infrastructure & Storage)4 lectures • 20min

Data Management in Banking Deep-Dive9 lectures • 30min

Reporting, Insight Generation, Data Driven Decision Making & Compliance10 lectures • 39min

Miscellaneous: Data Security, Emerging Technologies and Current Trends6 lectures • 30min

Conclusive Remarks & Future of Banking Data Management3 lectures • 6min

Requirements

Description

Who this course is for: