DATA 360 / DATA ARCHITECTURE
Data Architecture
The data model decisions you live with for years: DLOs, DMOs, mapping, data spaces, relationships, keys. The architecture every other Data 360 surface — and every agent grounded on it — depends on.
Foundation · 2
Production note
Data 360 architecture gotchas: the model decisions that outlast everything
The Data 360 data model looks like a setup wizard — connect a stream, map a few fields, done. The production reality is the opposite: the model is the one decision every segment, Calculated Insight, activation, and grounded agent inherits, and it's hardest to change after data is flowing. Ten model-architecture choices that bite, each with the question to answer first and the cost of getting it wrong.
Decision framework
Data 360 Data Architecture: Style Guide
The opinionated rules Cleon applies to every Data 360 model decision — naming, modeling, documentation, the patterns to prefer and the ones to refuse — plus the agent-readiness check that decides whether the model can ground an agent. The discipline document that ties the Data Architecture subcategory together.
Reference · 4
Reference
Data Lake Objects (DLOs) — Data 360 reference
The raw landing layer of Data 360: what a Data Lake Object is, how Data Streams create them, and why you map them to DMOs instead of building business logic against them directly.
Reference
Data Model Objects (DMOs) — Data 360 reference
The harmonized layer of Data 360: standard vs custom DMOs, the Customer 360 data model, and why mapping to standard objects buys you semantics that segmentation, identity resolution, and agents already understand.
Reference
Data spaces — Data 360 reference
Data spaces partition a Data 360 org for a real boundary — brand, region, regulatory regime. What's isolated, what's shared, and why the partition is a wall you build once.
Reference
Relationships & keys — Data 360 reference
Primary keys and relationships in the Data 360 model: what makes a row unique, how DMOs connect, and why an unmodeled relationship is a join your segments and insights silently can't make.
How-to · 2
How-to
Mapping DLOs to DMOs — Data 360 how-to
How meaning gets assigned in Data 360: the flow from Data Lake Object fields to Data Model Object attributes, the transformations available on the way, and the mapping mistakes that produce silent wrong data downstream.
How-to
Debugging mapping failures — Data 360 how-to
A DMO attribute lands blank, or wrong, or a record won't unify. The diagnostic flow is the same every time — walk from the DLO up to the DMO and find the layer where the value breaks. The mapping debugging playbook.