DAY 1 — DATA GOVERNANCE
Data Governance: From Regulatory Requirement to Business Enabler
- Introduction to Data Governance
- What is Data Governance?
- Evolution of Data Governance
- Data Governance as a strategic business capability
- Data Governance, regulations, and organizational requirements
- Overview of relevant regulatory and compliance frameworks:
- EU BCBS 239
- Bank of Italy Circular 285
- IVASS Regulation 38/2018
- EU GDPR 2016/679
- Regulatory requirements vs. business value
- Data Governance and risk management
- Data Governance in data-driven organizations
- Data Governance Reference Models and Standards
- DAMA-DMBOK framework
- Data Governance Institute framework
- Data Maturity Models
- Data Governance operating models
- Relationship with:
- ISO standards
- COBIT
- IT governance
- Risk management frameworks
- Information security practices
- Assessing organizational data maturity
- Data Governance trends:
- Artificial Intelligence
- Machine Learning
- Internet of Things
- Cloud data platforms
- Generative AI
- Organizational Models for Data Governance
- Data Governance organizational structures
- Roles and responsibilities
- Data Owner
- Data Steward
- Data Custodian
- Data Architect
- Data Governance Manager
- Chief Data Officer (CDO)
- Business vs. IT responsibilities
- Establishing a Data Governance Council
- Centralized vs. decentralized models
- Federated Data Governance
- Data Governance as a Service
- Developing accountability and ownership
- Metadata Management
- What is metadata?
- Why metadata matters
- Types of metadata:
- Business metadata
- Technical metadata
- Operational metadata
- Metadata standards
- Business terminology and definitions
- Ontology and semantics
- Business Glossary
- Data Dictionary
- Data Catalog
- Metadata repositories
- Metadata management processes
- Benefits of metadata management
- Data Lifecycle and Data Lineage
- Understanding the data lifecycle
- Data creation and acquisition
- Data storage and processing
- Data usage and distribution
- Data retention and archival
- Data disposal
- What is Data Lineage?
- Why Data Lineage is important
- Data Lineage dimensions
- Source-to-target mapping
- Technical vs. business lineage
- Building Data Lineage
- Data Lineage and Metadata Management
- Common challenges and obstacles
- Data lineage for compliance, audit, and impact analysis
- Laboratory & Practical Case Studies
Participants will work through practical exercises involving:
- Creating a basic Data Governance framework
- Identifying data ownership and stewardship roles
- Building a Business Glossary
- Creating a basic Data Dictionary
- Mapping a simple data lineage
- Identifying governance gaps
- Applying governance principles to a business scenario
DAY 2 — DATA QUALITY & DATA VISUALIZATION
Data Quality and Effective Representation of Business Data
- Introduction to Data Quality Management
- What is Data Quality?
- Importance of trustworthy data
- Data Quality and business decision-making
- Data Quality within Data Governance
- The Garbage-In-Garbage-Out (GIGO) principle
- Business impact of poor-quality data
- Cost of bad data
- Data Quality management lifecycle
- Assessing Data Quality
Understanding the key dimensions of Data Quality:
- Accuracy
- Completeness
- Consistency
- Timeliness
- Validity
- Uniqueness
- Relevance / fitness for purpose
- Data Quality metrics
- Data Quality rules
- Establishing Data Quality thresholds
- Data Quality monitoring
- Data Quality reporting
- Data Quality KPIs
- Data Discovery and Profiling
- Identifying organizational data sources
- Data discovery techniques
- Understanding relationships between datasets
- Data profiling concepts
- Descriptive statistics
- Identifying anomalies and outliers
- Missing-value analysis
- Duplicate analysis
- Pattern identification
- Profiling tools and automated approaches
- Introduction to automated data-quality checks and bots
- Data Cleansing and Data Preparation
- Data cleansing principles
- Data cleansing checklist
- Identifying incorrect and incomplete data
- Handling missing values
- Removing duplicate records
- Standardizing data formats
- Data normalization
- Data transformation
- Discretization
- Imputation techniques
- Data enrichment
- Manual vs. automated data cleansing
- Building a repeatable Data Cleaning workflow
- Data Quality Controls and Firewalls
- Data Quality rules
- Preventive vs. detective controls
- Data validation
- Data entry controls
- Golden rules for data entry
- Data Quality firewalls
- Monitoring data at source
- Exception management
- Continuous Data Quality improvement
- Data Design and Visualization
- Why data visualization matters
- From raw data to business information
- Exploratory vs. explanatory analysis
- Selecting appropriate visualizations
- Charts and graphs
- Tables and dashboards
- Designing effective dashboards
- Data visualization principles
- Avoiding misleading visualizations
- Data Storytelling
- Turning analysis into business insights
- Presenting information to management and decision-makers
- Data Visualization Platforms
Introduction to commonly used visualization platforms:
- Microsoft Power BI
- Google Data Studio / Looker Studio
- RAWGraphs
- Tableau overview
- Selecting the right visualization tool
- Connecting datasets to visualization platforms
- Creating basic reports and dashboards
- Laboratory & Applied Cases
Practical exercises may include:
- Profiling a sample dataset
- Identifying data-quality problems
- Creating data-quality rules
- Cleaning and standardizing data
- Removing duplicate records
- Handling missing values
- Transforming a dataset
- Creating charts and dashboards
- Building a simple data story
- Presenting findings to business stakeholders
DAY 3 — DATA MANAGEMENT
Managing Data Across the Data Lifecycle
- Introduction to Data Management
- What is Data Management?
- Data Management within the data lifecycle
- Data Management vs. Data Governance
- Data Management vs. Data Analysis
- Data lifecycle overview
- Data creation, storage, processing, usage, and disposal
- Role of Data Management in data-driven organizations
- Data Management challenges
- Establishing effective data management practices
- Data Types, Models & Structures
- Structured data
- Semi-structured data
- Unstructured data
- Common data formats:
- Introduction to data modeling
- Relational data models
- Document-based data models
- Tables and records
- Fields and attributes
- Primary and foreign keys
- Relationships between datasets
- Basic data modeling concepts
- Databases & Database Management Systems
- What is a Database?
- What is a DBMS?
- Relational databases
- NoSQL databases
- Relational vs. NoSQL databases
- Tables and relationships
- Basic SQL concepts
- SELECT statements
- Filtering and sorting
- JOIN concepts
- Understanding database schemas
Tools Overview
- MySQL
- PostgreSQL
- MongoDB
- When to use different database technologies
- Database management considerations
- Modern Data Architectures
- Introduction to Data Architecture
- Data Warehouse
- Data Lake
- Data Lakehouse overview
- Data Warehouse vs. Data Lake
- Data architecture use cases
- Cloud vs. on-premise data environments
- Introduction to the modern data stack
- Data platforms and analytics environments
- Choosing an appropriate architecture
- Data Integration & Data Flows
- What is Data Integration?
- Integrating multiple data sources
- Data pipelines
- Source-to-target data flows
- ETL — Extract, Transform, Load
- ELT — Extract, Load, Transform
- ETL vs. ELT
- Batch processing
- Real-time data processing
- APIs and data integration overview
- Business data-flow examples
- Monitoring data pipelines
- Operational Data Management
- Data storage concepts
- Cloud storage vs. on-premise storage
- Dataset organization
- Data versioning
- Dataset management
- Backup fundamentals
- Recovery fundamentals
- Data retention
- Data archival
- Data cataloging
- Data access and availability
- Operational considerations for managing enterprise data
- Introduction to Data Visualization
- Data management to reporting
- From raw data to analytical dataset
- Preparing data for visualization
- Data aggregation
- Report-ready datasets
- Introduction to:
- Microsoft Power BI
- Tableau
- Connecting data sources
- Basic reporting concepts
- Visualization as part of the data lifecycle
- Practical Case Study — End-to-End Data Management
Activities may include:
- Loading data from multiple sources
- Combining datasets
- Performing basic JOIN / MERGE operations
- Identifying and resolving data inconsistencies
- Applying simple transformations
- Organizing data into a coherent structure
- Performing basic quality checks
- Preparing a ready-to-analyze dataset
- Creating a basic report or visualization
- Presenting business insights
Practical Learning Approach
The program combines:
- Instructor-led presentations
- Interactive discussions
- Business case studies
- Practical demonstrations
- Hands-on laboratory exercises
- Data profiling exercises
- Data cleansing activities
- Metadata and Data Governance exercises
- Data lineage mapping
- Data integration exercises
- Visualization and reporting exercises
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