Semantic Tagging and Modeling in Niagara - Haystack & Brick
As BAS data volumes grow, consistent semantics become essential. Haystack and Brick provide structured ways to describe equipment, points, spaces, and relationships so applications can interpret data without custom, site-specific logic.
Why Semantic Modeling Matters
- Faster discovery of relevant points for analytics and dashboards
- Better interoperability across tools and vendors
- Reduced manual mapping effort during integrations
- More reliable context for fault detection and reporting
Haystack in Niagara
Haystack focuses on practical tagging conventions and data exchange patterns. In Niagara workflows, Haystack tags can standardize point meaning and support consistent extraction for downstream applications.
Brick in Niagara
Brick provides ontology-style modeling of entities and relationships, including physical and logical context. It is useful when richer graph-style relationships and extensibility are needed.
Niagara Best Practices
- Define enterprise tagging standards before rollout
- Map points to equipment and spaces consistently
- Use Niagara Tag Dictionary capabilities to manage ontology context
- Validate tag quality with periodic audits and review workflows
- Keep a versioned tagging playbook for project teams
Haystack vs Brick (Practical View)
- Haystack: simpler operational tagging, often faster to adopt
- Brick: richer relationship modeling for complex analytics ecosystems
- Many teams use both, with clear boundary rules by use case
Strong semantic strategy in Niagara improves data usability, reduces integration friction, and supports long-term analytics maturity.