Course Description
An integrated program focused on building a practical data-governance framework that defines ownership, responsibilities, policies, and decision-making mechanisms for data. It covers data quality, master and reference data management, data cataloging, metadata, quality rules, performance measurement, and issue remediation. The course also addresses the roles of data owners and stewards, the lifecycle of data-quality issues, and alignment with organizational objectives. It aims to help participants design a more reliable, consistent, and controlled data environment.
Course Objectives
- Understand data governance principles and roles.
- Define data owners, stewards, and responsibilities.
- Build a framework for measuring data quality.
- Apply quality dimensions and validation rules.
- Understand master and reference data management.
- Design a data-quality issue remediation lifecycle.
- Connect data catalog and metadata with governance.
- Create governance and quality KPIs and dashboards.
Course Content
- Data governance principles
- Operating model and roles
- Policies and standards
- Data classification
- Metadata and data catalog
- Data quality dimensions
- Quality rules and monitoring
- Root-cause analysis
- Quality issue management
- Master Data Management
- Reference data
- Matching, merging, and deduplication
- KPIs and maturity
- Implementation roadmap
Target Audience
Data, digital transformation, BI, IT, governance, risk, compliance teams, data owners, and data stewards.
Duration
5 training days
