DM100: Fundamentals of Data Management & Data Governance NEW

Agenda:

• Introduction
  • Objectives
  • Expectations
  • Introduction of common terms and delimitations
    
    
• Data Management "Big Picture"
  • Disciplines
  • Responsibilities
  • Data lifecycle

• Data Governance
  • Purpose
  • Principles
  • typical anti patterns
  • Success factors
    
    
• Roles & Responsibilities
  • Owner
  • Steward
  • Custodian/Engineer
  • Consumer
  • Governance Bodies
    
    
• Policies, Standards & Controls
  • from guardrails to actionable rules
    
    
• Metadata as Enablement
  • Business Glossary
  • Catalog
  • Classification
  • critical data elements
    
    
• Data Quality Fundamentals
  • Dimensions
  • DQ Rules
  • Issue Management
  • KPI Logic
    
    
• Security, Privacy & Compliance
  • Risk thinking
  • Access
  • Protection requirements
  • "Need to know"
    
    
• Operating Model & Change
  • Decision paths
  • Adoption
  • Communication
    
    
• Conclusion
  • "Minimum Viable Governance" sketch and next steps

Objectives:

• Consistent understanding of key terms (data asset, data product, governance, stewardship, etc.)
• Classification of the most important data management domains and their dependencies
• Ability to outline governance basic frameworks (roles, committees, artifacts, processes)
• Distinguishing and applying policy/standard/guideline including enforcement
• Basic logic of metadata/catalog/glossary, data quality management, and privacy requirements
• Deriving initial, realistically implementable steps for your own organization

Target audience:

• Data Owner
• Data Steward
• Data Manager
• Data Engineer
• Analytics/BI
• Data Scientist
• Product Owner
• IT/Department Management
• Compliance/Privacy Stakeholder

Prerequisites:

Description:

The course DM100 Fundamentals of Data Management & Data Governance creates a shared level of terminology, methods, and orientation for all data roles: from operational specialists through to managers. The focus is on a practical understanding of why Data Management and Data Governance are necessary, what typically belongs to an effective governance system (roles, rules, processes, artifacts, metrics), and how these building blocks are embedded in the organization. The workshop combines short input sequences with guided exercises: participants transfer concepts to a shared case study and build a “Minimum Viable Governance” framework step by step.

A key benefit of the DM100 Fundamentals of Data Management & Data Governance workshop is alignment between business, IT, and analytics: What is a “data product”, what is a “data asset”, where does accountability begin/end, how are binding rules (policies/standards) created, and how is effectiveness measured? In addition, the most important domains of Data Management are classified at a high level (e.g., metadata, data quality, security/privacy, architecture, lifecycle) and their interactions are explained, so that subsequent advanced modules (DM200 Data Governance & Data Asset Management in Practice & DM250: Data Engineering & DataOps) can build on a stable foundation.