BIMINAM EDUCATION WORKSTREAM MEETING, 31-03-2026
Date: 31 March 2026
Purpose of the Session
The session focused on defining a minimum viable data and validation model for asset information, particularly for fire-critical and operational assets in education environments (universities and schools).
The objective was to move toward a standardised, auditable structure that ensures:
- competent people can perform tasks correctly
- information is complete and usable at handover
- outcomes can be verified and evidenced
Key Themes from the Transcript
1. Defining “minimum data” for safe operation
The core goal was to agree what information must exist so that:
- a competent person can locate, understand and work on an asset
- the work can be completed safely and correctly
- there is proof that the work has been done properly
This represents a shift from over-specification to minimum viable, task-driven information.
2. Four-part information model (Identify → Understand → Work → Prove)
A structured framework was developed to organise asset information:
1. Identify – Where and what the asset is
- Location (XYZ, space ID)
- Asset ID and type
- Context (e.g. school, lab, accommodation)
- Roles and responsibilities
- Information requirements (AIR, EIR, OIR)
2. Understand – What needs to be known
- Manufacturer, specification, compliance
- System relationships and component parts
- Risks, constraints, and criticality
- Lifecycle and residual life
- Fire strategy and design intent
3. Work – What is required to perform tasks
- Task plans, schedules and permits
- RAMS (risk assessment & method statements)
- PPE, tools, spares and consumables
- Safety considerations (COSHH, zonal hazards, adjacency impacts)
- Maintenance history and operational constraints
4. Prove – Evidence of completion and compliance
- Photos and video evidence
- Test and commissioning records
- Checklists and sign-off
- Compliance with maintenance plans
- Notification/coordination (e.g. emergency services where relevant)
This structure creates a clear, repeatable validation logic for asset information.
3. Standardisation based on safety-critical asset research
The model builds on prior work analysing 12 safety-critical asset types, using consistent questions such as:
- What risk does the asset mitigate?
- What information is required for performance?
- What tasks and competencies are needed?
This provides a robust evidence base for defining information requirements at scale.
4. Addressing ambiguity and inconsistency in standards
A recurring issue was terminology confusion, particularly:
- AIR / EIR / OIR definitions
- RAMS vs RAM (engineering vs FM meanings)
The group emphasised the need to:
- standardise language
- avoid ambiguity
- ensure consistent interpretation across stakeholders
5. System thinking and asset context
A critical insight was that:
- every asset exists within a system and spatial context
Key additions included:
- system relationships and dependencies
- adjacency impacts (e.g. neighbouring spaces, labs, fire zones)
- environmental and operational conditions
This reinforces that asset data must reflect real operational context, not isolated components.
6. Enabling validation at source (not after the fact)
The discussion highlighted that current practice:
- validates information too late (post-handover)
- leads to errors, rework and risk
The proposed model enables:
- validation during delivery
- structured inputs aligned to requirements
- reduced reliance on manual checking
7. Focus on the end user (person doing the work)
A key principle was maintaining focus on:
the individual carrying out maintenance or operational tasks
This ensures the model:
- remains practical and usable
- avoids over-complexity
- delivers real operational value
Key Insights from the Chat
- Need for clear client-led information requirements and governance
- Strong support for machine-readable standards (e.g. IDS, QR codes, GS1 traceability)
- Importance of QA records, commissioning data and validation tools
- Recognition of liability risks in certifying O&M information
- Emphasis on keeping information updated post-handover (not just initial delivery)
- Calls for potential evolution into a formal standard (e.g. British Standard)
Overall Conclusion
The session represents a significant step toward a practical, standardised model for asset information in the education sector.
The emerging approach:
- defines minimum, task-driven information requirements
- structures data into a clear lifecycle workflow
- integrates validation, evidence and accountability
- aligns technical information with real operational needs
If adopted, this model has the potential to become a scalable industry standard, improving handover quality, reducing risk, and enabling effective delivery of the Golden Thread in practice.