Organizational Data Model¶
The Organizational Data Model defines the canonical data, knowledge, semantic, and governance model for the organization.
It is a living model. It evolves as organizational domains are discovered, defined, mapped, and validated.
Explore the Model¶
Ontology¶
The foundational concepts, domains, and relationships that describe the organization.
Data Dictionary¶
The canonical definitions of concepts, metrics, KPIs, and data assets.
Definitions are maintained as machine readable YAML and published through the documentation site.
Domains¶
Domain specific models connect organizational concepts to real sources, measurements, and operational context.
Additional domains will be added as they are modelled.
Current Domain¶
The first implementation domain is Social Media, covering:
- X
- TikTok
Social Media provides the first practical test of the organizational ontology, data dictionary, source mappings, and metric model.
Model Philosophy¶
The model separates organizational meaning from the systems that happen to store or expose the data.
Source Systems
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Source Fields
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Source Mappings
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Canonical Concepts
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Metrics and KPIs
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Organizational Knowledge
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Decision Making
A platform field is therefore not automatically a canonical organizational definition.
The model captures the organization's meaning first, then describes how that meaning is represented across systems and platforms.
Source of Truth¶
The repository is the source of truth for the model.
YAML Definitions
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Validation
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Generated Documentation
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Human Review
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Evolving Organizational Model
Machine readable definitions are validated against schemas before being published as documentation.
This allows the model to serve both as a human readable reference and as a foundation for future data engineering, governance, lineage, and AI systems.
Evolution¶
The model will evolve as additional domains are discovered and understood.
Definitions, relationships, terminology, mappings, and metrics may change as the organization develops a clearer understanding of its data and operations.
The objective is not to produce a perfect model upfront.
The objective is to establish a durable semantic foundation that can become more precise as organizational knowledge grows.