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4 courses
48 practices
4 hours
Build conceptual fluency in data architecture by connecting semantic models, knowledge graphs, data mesh operating models, and data quality practices to real stakeholder decisions.
You'll compare architectural perspectives, break an ontology into its working parts, and use controlled vocabulary to align product, analytics, and governance on shared meaning. You'll practice diagnosing semantic ambiguity and reframing disputed terms before they become architectural trust problems.
You'll learn to reason in nodes, edges, and relationships, and to judge when connected context better supports certain relationship-centered or variable-depth questions than tables and hierarchies do. You'll practice separating observed relationships from inferred cause and translating graph evidence into decision-ready advice without writing code or producing technical data designs.
You'll treat data mesh as a decision-rights and accountability model rather than a technology programme, working through domain ownership, data products, and federated governance. You'll practice mediating an ownership dispute and closing a cross-domain conversation with a bounded, evidence-based decision.
You'll first build data-quality foundations — decision-relevant dimensions, expectations, controls, and observability — then use them to synthesize a bounded stewardship agreement. You'll be pushed to interrogate comfortable defaults: who really owns quality, what a control is actually worth, and how trust gets argued for in front of leadership.
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