Concepts
Database Schema Overview
The IDN platform is built on a deeply normalized relational schema designed for Computer-Aided Facilities Management (CAFM). It models every operational entity a facilities organization touches — from portfolios and buildings down to individual work order line items.
333
Database Tables
12+
Functional Modules
8
Core Entity Types
5
Reporting Horizons
Three layers
The schema is organized in three conceptual layers:
- Modules — functional domains such as Portfolio, Work, Equipment, or EHS. A module is a grouping, not a database object; it determines which tables belong together.
- Tables — the entities themselves (e.g.
building,work_order,asset). 333 tables in total. - Fields — typed columns on each table. 12,000+ across the system, with consistent naming, validation, and relationship conventions.
The 12 modules
Every table belongs to exactly one module. Here is the full list:
PROPPEOPLEEQUIPEQUIPSPACESYSSYSINVDOCSREPORTSSYSCAD
Portfolio & Properties
Countries, regions, cities, properties, buildings, floors, spaces and zones.
People & Organizations
Employees, contractors, visitors, org structures, branches, cost centres and roles.
Work & Maintenance
Service requests, work orders, PM schedules, inspections and task lifecycle.
Equipment & Assets
Asset registry, surveys, maintenance history and classification.
Spaces & Occupancy
Space inventory, occupancy data, zones and capacity planning.
Moves, Adds & Changes
Space moves, new installations, renovations and projects with approval workflows.
EHS & Compliance
Safety inspections, incidents, waste management and environmental tasks.
Materials & Procurement
Inventory, stock, purchase orders and material consumption.
Documents
Drawing registers, O&M manuals, certificates and document control.
Reporting & Analytics
Daily, weekly, monthly and yearly summary views across all operations.
System & Security
User roles, dashboards, audit trails, help content and background jobs.
CAD & Design Tools
Computer-aided design tools for CAFM — floor plans, drawings and spatial authoring.
Why normalization matters
A normalized model is more verbose than a flat one, but it is what enables the things FM teams actually need in 2026:
- Reliable analytics — every metric has one source of truth.
- Predictive maintenance — asset, work-order, and sensor history are linked, not duplicated.
- AI-native queries — the AI Assistant can reason over a clean graph instead of guessing across spreadsheets.
- Compliance — EHS, audit, and document trails stay attached to the entities they describe.
Naming conventions
Tables and fields use
snake_case. Foreign keys end in _code. Lookup tables are singular (building, not buildings). Date fields use _at for timestamps and _on for date-only values.Where to go next
- Browse the Core Tables at a Glance view.
- Read the Schema Explorer guide.
- Ask the AI Assistant a natural-language question.
