Mapped

Name: Mapped
Brand: Hospitals using Mapped Independent Data Layer

Mapped is a B2B data-layer platform that connects building systems and IoT sources, normalizes heterogeneous building data to a consistent semantic model, and exposes that normalized data via APIs, streaming, and destination connectors for downstream analytics and operational workflows.

Est 2019|Valuation $55M|Raised $35M|54 Ref Customers

Website|FTEs 44

MarketsSmart Buildings

01Competitive Landscape

Narrative analysis

- Mapped versus SkySpark: Mapped emphasizes a foundational data layer and normalized API, whereas SkySpark is typically purchased for analytics and fault-detection use cases. If the immediate objective is packaged analytics, an analytics-led platform may deliver faster time-to-insight; if the objective is to create a consistent data foundation for multiple downstream applications, a data-layer approach can be advantageous. - Mapped versus Niagara: Niagara is widely used within building control ecosystems and is strong for supervisory control and controller integration. Mapped may be more suitable for organizations seeking to abstract multiple vendor systems into a normalized data model and modern API for cloud and data teams. - Mapped versus OEM suites (OpenBlue/Forge): OEM suites can provide integrated, end-to-end ecosystems tied to vendor equipment. A vendor-agnostic normalization layer can be beneficial when portfolios are mixed-vendor or when buyers want to avoid tight coupling to a single OEM’s digital platform.

	Mapped	SkySpark (SkyFoundry)	Tridium Niagara (Framework ecosystem)
Starting Price	Starter: free; Pro/Ent: not published; virtual gateway: low-hundreds per month; usage/overages possible	Pricing via partners; not transparently listed on vendor site	Pricing varies by hardware/software licensing & channel; not presented as a single SaaS price
Primary Use Case	Standardizing multi-system building data into a unified API/ontology across portfolios; enabling an app ecosystem	Analytics/FDD and KPI-driven building intelligence once data is connected and tagged	Integrating and operating building control/supervisory environments; strong in BAS ecosystems
Key Strength	API-centric data normalization and ontology translation (Brick/Haystack/custom); connector ecosystem	Mature analytics and FDD capabilities; strong community around Haystack tagging	Wide adoption in BAS; extensive integrator base; strong for real-time control and supervisory integration
Key Weakness	Limited public third-party review footprint; pricing complexity (usage + gateway + overages); semantic mapping still requires governance	Typically purchased for analytics rather than as a universal data-layer API; channel-driven pricing models	Can be complex and integrator-dependent; may require additional work to expose a normalized API/semantic layer

01Competitive Landscape

Narrative analysis

Mapped versus SkySpark: Mapped emphasizes a foundational data layer and normalized API, whereas SkySpark is typically purchased for analytics and fault-detection use cases. If the immediate objective is packaged analytics, an analytics-led platform may deliver faster time-to-insight; if the objective is to create a consistent data foundation for multiple downstream applications, a data-layer approach can be advantageous.
Mapped versus Niagara: Niagara is widely used within building control ecosystems and is strong for supervisory control and controller integration. Mapped may be more suitable for organizations seeking to abstract multiple vendor systems into a normalized data model and modern API for cloud and data teams.
Mapped versus OEM suites (OpenBlue/Forge): OEM suites can provide integrated, end-to-end ecosystems tied to vendor equipment. A vendor-agnostic normalization layer can be beneficial when portfolios are mixed-vendor or when buyers want to avoid tight coupling to a single OEM’s digital platform.

	Mapped	SkySpark (SkyFoundry)	Tridium Niagara (Framework ecosystem)
Starting Price	Starter: free; Pro/Ent: not published; virtual gateway: low-hundreds per month; usage/overages possible	Pricing via partners; not transparently listed on vendor site	Pricing varies by hardware/software licensing & channel; not presented as a single SaaS price
Primary Use Case	Standardizing multi-system building data into a unified API/ontology across portfolios; enabling an app ecosystem	Analytics/FDD and KPI-driven building intelligence once data is connected and tagged	Integrating and operating building control/supervisory environments; strong in BAS ecosystems
Key Strength	API-centric data normalization and ontology translation (Brick/Haystack/custom); connector ecosystem	Mature analytics and FDD capabilities; strong community around Haystack tagging	Wide adoption in BAS; extensive integrator base; strong for real-time control and supervisory integration
Key Weakness	Limited public third-party review footprint; pricing complexity (usage + gateway + overages); semantic mapping still requires governance	Typically purchased for analytics rather than as a universal data-layer API; channel-driven pricing models	Can be complex and integrator-dependent; may require additional work to expose a normalized API/semantic layer