Data Mesh and Data Product Management

Module 1: data mesh principles

Establish the vocabulary, boundaries, stakeholders, and decision context for data mesh principles. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Separate established requirements and reliable evidence from untested assumptions about data mesh principles. Record the decision, supporting evidence, and unresolved questions in the solution canvas.

Map data mesh principles to the organization’s current responsibilities, dependencies, and constraints. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 2: domain ownership

Diagnose the current state of domain ownership using a structured evaluation lab. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Compare alternative methods and select an approach suited to risk, maturity, and scale. Record the decision, supporting evidence, and unresolved questions in the evaluation lab.

Document requirements, owners, decision criteria, and exceptions for domain ownership. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 3: data product design

Design the workflow, safeguards, and handoffs required for data product design. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Test normal, failure, and edge-case scenarios before operational adoption. Record the decision, supporting evidence, and unresolved questions in the production-readiness review.

Review the design for security, quality, accessibility, sustainability, or assurance implications as relevant. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 4: federated governance and platform

Define meaningful measures, evidence, review cadence, and escalation thresholds for federated governance and platform. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Investigate performance gaps and separate root causes from symptoms. Record the decision, supporting evidence, and unresolved questions in the solution canvas.

Plan corrective action, controlled change, and accountable follow-through. State what would trigger rejection, escalation, or redesign of the proposed approach.

Module 5: Applied Data Mesh and Data Product Management Workshop

Complete a evaluation lab that integrates the course decisions around operating-model workshop. Use architecture choices, evidence-based evaluation, and an applied solution review to test assumptions against the client context.

Defend recommendations against a realistic stakeholder challenge or scenario. Record the decision, supporting evidence, and unresolved questions in the evaluation lab.

Produce a prioritized workplace action plan with owners, dependencies, and review points. State what would trigger rejection, escalation, or redesign of the proposed approach.