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DM200: Data Governance & Data Asset Management in Practice NEW

Participants receive a hands-on deep dive for everyone who needs to implement and operate Data Governance: the foundational framework is turned into an actionable operating model. Participants develop a role model with clear accountability logic (RACI), define policies, standards, and decision records, design catalog and metadata processes, and connect them with lineage and impact analyses, data quality, and issue management. Includes blueprints and templates for a direct start.

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Possible course languages: German

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Agenda:

Refresh DM100 Fundamentals of Data Management & Data Governance: governance foundation, target state & guardrails
Data Asset Management: asset definition, classification, criticality, ownership principles
Role model & organization: Data Owner/Steward/Domain Lead, RACI, delegation logic
Policies & standards in practice: structure, approval, exceptions, evidence
Metadata operations: glossary/catalog processes, data asset onboarding, maintenance & metadata quality
Lineage & impact: purpose, required granularity, how to “start” pragmatically
Data quality as an organization: DQ rules, DQ KPIs, data issue management (triage, ownership, SLAs)
Operating models: centralized, federated, hub and spoke, Data Mesh-compatible governance
Adoption & change: communication and enablement plan, governance backlog, quick wins

Objectives:

Design and justify a practical role and accountability model
Formulate policies/standards so that they are implementable, auditable, and integrable
Define catalog/glossary operating processes (onboarding, maintenance, approvals, quality criteria)
Define a lineage/impact approach (scope, granularity, value, initial MVP)
Set up a data quality organization: rules, KPI set, issue workflow, ownership/SLAs
Design a governance operating model including bodies, decision paths, and escalation
Create a concrete implementation roadmap (pilot, scaling, metrics, change measures)
Structured preparation for CDMP/governance certifications

Target audience:

Data Asset Manager, Data Manager, Data Steward, Data Governance Lead/Manager

Prerequisites:

To be able to follow the course content and learning pace in the DM200 Data Governance & Data Asset Management in Practice training, we recommend having attended the following course beforehand:
- DM100 Fundamentals of Data Management & Data Governance or equivalent foundational knowledge
Practically helpful: knowledge of your own data domains, stakeholders, and typical problem cases

Description:

The DM200 Data Governance & Data Asset Management in Practice course is the practice-oriented deep dive for everyone who must not only “understand” governance, but implement and operate it. Building on DM100 Fundamentals of Data Management & Data Governance, the foundational framework is turned into an implementable operating model: participants develop a role model with clear accountability logic (including RACI thinking), define governance artifacts (policies, standards, decision records), design catalog and metadata processes (onboarding, maintenance, approvals), and connect these with lineage/impact analyses, data quality organization, and pragmatic issue management.

A central element is translation into day-to-day work: How is stewardship embedded organizationally? Which body makes which decisions? How are conflicts resolved? How do you prioritize data assets (e.g., critical data elements), and how do you establish a “definition of done” for data (e.g., catalog entry, quality rules, access control, accountable owners, lineage evidence)? The workshop provides concrete blueprints and templates that teams can use directly as a starting point (policy templates, stewardship backlog, operating model canvas, DQ KPI set, catalog onboarding checklist).