The EDW Data Governance – Metadata, Data Lineage, Data Quality & Data Stewardship Training Course provides participants with a practical and structured understanding of data governance within an Enterprise Data Warehouse (EDW) environment. The course focuses on the governance practices required to ensure enterprise data is properly defined, documented, traceable, accurate, consistent, secure, and accountable throughout its lifecycle.

Participants will explore the core pillars of EDW data governance, including metadata management, business and technical metadata, data catalogs, end-to-end data lineage, data quality management, data ownership, and data stewardship. The course also examines how governance controls are integrated across source systems, data ingestion and transformation processes, EDW platforms, data marts, reporting systems, analytics platforms, and downstream consumers.

Through practical governance concepts and scenarios, participants will learn how to establish governance roles, define critical data elements, maintain business glossaries, trace data movement and transformation, implement data quality rules and controls, manage data issues, and establish effective stewardship processes.

The course is designed to be vendor-neutral and applicable to traditional EDW, cloud data warehouse, data lake, lakehouse, and hybrid enterprise data environments.

 

Duration 4 Days – 28 hrs.

 

Objectives

• Explain the principles and business value of enterprise data governance.
• Understand the role of data governance within an EDW architecture.
• Identify key data governance roles, responsibilities, and decision rights.
• Distinguish between business, technical, operational, and governance metadata.
• Establish and maintain business glossaries and data dictionaries.
• Understand the purpose and operation of enterprise data catalogs.
• Define and document end-to-end data lineage.
• Trace data from source systems through transformations to EDW and reporting layers.
• Understand column-level and transformation-level lineage.
• Define data quality dimensions, rules, thresholds, and controls.
• Perform data profiling and identify common data quality problems.
• Establish data quality monitoring and issue-management processes.
• Understand the responsibilities of data owners, data stewards, custodians, and data consumers.
• Develop effective data stewardship processes.
• Identify and govern Critical Data Elements (CDEs).
• Integrate metadata, lineage, quality, and stewardship into a unified governance framework.
• Define governance metrics, KPIs, and reporting mechanisms.
• Apply governance concepts to EDW, data lake, lakehouse, BI, and analytics environments.
• Develop a practical roadmap for implementing or improving EDW data governance.

 

Target Audience

• Data Governance Professionals
• Data Governance Managers and Leads
• Data Stewards
• Data Owners
• Data Custodians
• Data Architects
• Enterprise Architects
• EDW Architects
• Data Engineers
• Database Administrators
• Data Analysts
• Business Intelligence Professionals
• ETL/ELT Developers
• Data Quality Analysts
• Metadata Management Professionals
• Business Analysts
• Information Management Professionals
• Risk and Compliance Professionals
• IT Governance Professionals
• Data Management Managers and Team Leads
• Professionals involved in EDW, data lake, lakehouse, BI, and analytics initiatives

 

Prerequisites

• Basic understanding of databases, data warehouses, or enterprise information systems.
• General knowledge of relational data concepts, tables, fields, and relationships.
• Basic familiarity with ETL/ELT, reporting, BI, or analytics processes is beneficial.
• General understanding of organizational data management is helpful.
• No specific data governance platform or programming language experience is required.

Course Outline

Day 1 – EDW Data Governance Foundations and Metadata Management

Module 1: Introduction to Enterprise Data Governance

• What is data governance?
• Data governance versus data management
• Business drivers for data governance
• Data governance principles
• Governance policies, standards, processes, and controls
• Data governance operating models
• Centralized, decentralized, and federated governance
• Data governance maturity
• Building a data-driven governance culture

Module 2: Data Governance in the EDW Environment

• Enterprise Data Warehouse fundamentals
• Role of governance in EDW architecture
• Governing source-to-consumption data flows
• Source systems and operational data
• Data ingestion and staging layers
• Integration and transformation layers
• Enterprise warehouse and data marts
• Semantic, reporting, BI, and analytics layers
• Governance considerations for cloud and hybrid data platforms

Module 3: Data Governance Roles and Responsibilities

• Data governance council
• Data governance office
• Data owners
• Data stewards
• Data custodians
• Data architects and engineers
• Business and technical stakeholders
• Data consumers
• Decision rights and accountability
• RACI models for data governance

Module 4: Metadata Management Fundamentals

• Understanding metadata
• Business metadata
• Technical metadata
• Operational metadata
• Governance metadata
• Metadata repositories
• Metadata standards
• Metadata lifecycle management
• Metadata ownership and accountability

Module 5: Business Glossary, Data Dictionary and Data Catalog

• Business glossary fundamentals
• Establishing standard business terminology
• Business terms and definitions
• Data dictionaries
• Data catalog concepts
• Cataloging EDW data assets
• Metadata discovery
• Classification and tagging
• Data ownership within catalogs
• Search and discovery of enterprise data assets

 

Day 2 – Data Lineage and Traceability

Module 6: Data Lineage Fundamentals

• What is data lineage?
• Business versus technical lineage
• Horizontal and vertical lineage
• End-to-end lineage
• Table-level lineage
• Column-level lineage
• Transformation-level lineage
• Lineage documentation
• Lineage visualization

Module 7: EDW Source-to-Target Data Lineage

• Identifying source systems
• Source-to-target mappings
• Data ingestion lineage
• ETL and ELT transformation lineage
• Staging-to-warehouse lineage
• EDW-to-data-mart lineage
• Reporting and BI lineage
• Analytics and downstream consumption
• Tracking derived and calculated data
• Understanding lineage across multiple platforms

Module 8: Data Transformation and Dependency Analysis

• Data transformation rules
• Mapping source and target attributes
• Business rules within transformations
• Data dependencies
• Upstream and downstream dependencies
• Impact analysis
• Root-cause analysis
• Change impact assessment
• Schema and transformation changes
• Managing lineage during EDW modernization

Module 9: Lineage Governance and Controls

• Lineage ownership
• Lineage documentation standards
• Lineage completeness
• Lineage validation
• Automated versus manually maintained lineage
• Lineage metadata integration
• Maintaining lineage accuracy
• Lineage for audit and compliance
• Lineage governance metrics

 

Day 3 – Enterprise Data Quality Management

Module 10: Data Quality Fundamentals

• Understanding data quality
• Business impact of poor-quality data
• Data quality within EDW environments
• Data quality management lifecycle
• Preventive versus detective controls
• Data quality ownership
• Data quality governance

Module 11: Data Quality Dimensions

• Accuracy
• Completeness
• Consistency
• Validity
• Uniqueness
• Timeliness
• Integrity
• Conformity
• Defining measurable quality dimensions
• Establishing data quality thresholds

Module 12: Data Profiling and Quality Assessment

• Data profiling concepts
• Structural profiling
• Content profiling
• Relationship profiling
• Pattern analysis
• Null and missing-value analysis
• Duplicate detection
• Referential integrity
• Outlier identification
• Establishing baseline data quality

Module 13: Data Quality Rules and Controls

• Defining business data quality rules
• Technical validation rules
• Source-system controls
• ETL/ELT validation
• EDW quality checks
• Reconciliation controls
• Data quality scorecards
• Thresholds and tolerances
• Data quality monitoring
• Exception handling

Module 14: Data Quality Issue Management

• Identifying data quality incidents
• Logging and categorizing issues
• Prioritizing data quality problems
• Root-cause analysis
• Corrective and preventive actions
• Assigning issue ownership
• Data remediation
• Issue escalation
• Tracking issue resolution
• Continuous quality improvement

 

Day 4 – Data Stewardship and Integrated EDW Governance

Module 15: Data Stewardship Fundamentals

• What is data stewardship?
• Business versus technical stewardship
• Data stewardship operating models
• Data steward responsibilities
• Data owner versus data steward
• Stewardship accountability
• Stewardship across business domains
• Building a stewardship community

Module 16: Critical Data Elements and Data Domains

• Understanding data domains
• Domain-based governance
• Identifying Critical Data Elements (CDEs)
• CDE selection criteria
• Assigning data ownership
• Establishing stewardship responsibilities
• Defining authoritative data sources
• Managing shared enterprise data
• CDE metadata and quality requirements

Module 17: Stewardship Processes and Workflows

• Business term approval
• Metadata review and approval
• Data classification
• Data quality issue resolution
• Lineage validation
• Governance exception management
• Change management
• Escalation workflows
• Stewardship meetings and governance forums
• Maintaining governance documentation

Module 18: Integrating Metadata, Lineage, Quality and Stewardship

• Connecting business glossary and technical metadata
• Linking metadata with data lineage
• Linking lineage with data quality
• Connecting quality issues with data ownership
• Stewardship of metadata and lineage
• Governance across the complete data lifecycle
• Building an integrated governance ecosystem
• Governance automation opportunities
• Governance for EDW, data lake, and lakehouse architectures

Module 19: Governance Metrics, KPIs and Reporting

• Measuring governance effectiveness
• Metadata completeness metrics
• Lineage coverage metrics
• Data quality KPIs
• Stewardship participation metrics
• Issue-resolution metrics
• Governance scorecards
• Executive governance dashboards
• Monitoring governance maturity
• Continuous governance improvement

Module 20: Building an EDW Data Governance Roadmap

• Assessing current governance maturity
• Identifying governance gaps
• Prioritizing data domains and CDEs
• Establishing governance roles
• Developing metadata standards
• Establishing lineage requirements
• Implementing data quality controls
• Operationalizing data stewardship
• Governance technology considerations
• Phased implementation roadmap
• Scaling governance across the enterprise
• Key success factors and common implementation challenges