Operational Data Readiness
Facility Operations depends on current, mapped, and explainable source data. This guide shows how to prepare BMS points, meter records, inspection findings, work orders, CMMS data, documents, and fused datasets before they support dashboards, AI Agent summaries, evidence packs, or maintenance decisions.
Focus on preparing enough trusted data for the selected operating boundary and review cadence.
Readiness Flow
Prerequisites
| Requirement | Why it matters |
|---|---|
| Operating boundary | Limits data preparation to the site, system, assets, and review cadence in scope. |
| Source owners | Provide credentials, export rules, field meaning, and source-side validation. |
| Asset identity map | Allows BMS, meters, CMMS, Inspector, ECM, and DFS records to join cleanly. |
| Quality reviewer | Confirms missing values, stale records, units, and rejected rows before use. |
| Downstream owner | Confirms whether the data feeds dashboards, AI Agent, evidence packs, CMMS, or BI. |
Source Systems
| Source | Typical records | Facility Operations use |
|---|---|---|
| BMS or automation system | Points, statuses, alarms, setpoints, schedules, device metadata. | Asset status, alert context, energy review, subsystem diagnosis. |
| Meter or utility system | kW, kWh, demand, water, gas, carbon factors, billing intervals. | Consumption review, ESG input, Green Mark evidence preparation. |
| CMMS or EAM | Work orders, assets, priority, status, owner, action notes, completion evidence. | Maintenance context, multi-provider consolidation, closed-loop review. |
| Inspector | Alerts, inspections, worker tasks, photos, voice notes, comments, audit trail. | Field execution, issue escalation, evidence capture. |
| ECM or document source | Manuals, SOPs, drawings, service reports, certificates, compliance files. | Searchable operating context and evidence. |
| Spreadsheets or pilot exports | Asset tables, point lists, work history, inspection records. | Pilot setup, data cleanup, and baseline validation. |
Step 1: Define Required Fields
Every source should provide enough identity and timestamp context.
| Field group | Examples |
|---|---|
| Asset identity | Asset ID, equipment name, source-system code, alias, location, parent system. |
| Time | Event time, reading time, created time, closed time, sync time, ingestion time. |
| Source | System name, connector ID, table, topic, endpoint, file, or import batch. |
| Status | Alarm severity, work-order state, task state, quality flag, review state. |
| Evidence | Comment, attachment, document link, photo, service note, reviewer note. |
| Units and scale | Temperature, pressure, flow, energy, demand, humidity, status, count, currency. |
Step 2: Connect with DFS Lite
Use DFS Lite when the team needs to connect, browse, preview, map, and sync source data.
- Choose the connector type: REST, JDBC, CSV, MQTT, BACnet, or a project-enabled connector.
- Test credentials and network access.
- Browse the source schema or preview records.
- Select only records needed for the first workflow.
- Map fields to asset, time, value, status, owner, and evidence fields.
- Run a limited sync.
- Review sync history and rejected records.
Use DFS Lite Connectors and Connector Configuration for detailed setup.
Step 3: Review Data Quality
Run quality checks before the data becomes operational evidence.
| Check | Questions |
|---|---|
| Completeness | Are required asset, time, status, and value fields populated? |
| Freshness | Are readings, work orders, and inspection records current enough for the workflow? |
| Consistency | Do source units, status values, and owner names match the agreed mapping? |
| Accuracy | Do sample records match the source system and field-team understanding? |
| Duplicates | Are repeated records expected, or caused by connector or export logic? |
| Lineage | Can the team explain where the record came from and when it was synced? |
Data that fails these checks should stay visible as a readiness gap so the operator understands the source limitation.
Step 4: Promote to DFS Pro When Needed
Use DFS Pro when the workflow needs governed datasets, steward review, fusion, BI reports, AI Agent evidence, or repeatable data products.
| Need | DFS Pro capability |
|---|---|
| Reusable operating dataset | Dataset lifecycle, ownership, profile, and validation. |
| Multi-source identity | MDM, aliases, entity resolution, steward queue, and merge or re-point review. |
| Inspection and work-order fusion | Fusion tasks and review queue. |
| Reporting | BI reports and audit metrics. |
| Agent readiness | Curated dataset with source notes, lineage, and quality state. |
For combined evidence, use Fuse Inspection, Work-Order, and Sensor Data.
Step 5: Connect Documents and Evidence
Documents should be connected to operational records before users depend on them.
| Document type | Link target |
|---|---|
| Equipment manual | Asset, equipment class, system, or work-order template. |
| SOP | Task type, system, work order, inspection, or role. |
| Drawing | Site, building, floor, area, system, or model asset. |
| Service report | Work order, asset, contractor, or maintenance event. |
| Compliance file | Clause, evidence item, asset, system, project, or review period. |
Use ECM Getting Started and ECM AI and Agent when documents become AI Agent context.
Output Checklist
The data package is ready for the first Facility Operations workflow when:
- source connectors or imports have named owners;
- asset identity fields map to the facility hierarchy;
- timestamps and source freshness are visible;
- failed or rejected records have a review path;
- work-order and inspection records can be linked to assets and evidence;
- documents are searchable and linked to the correct context;
- DFS Pro datasets are validated when reusable data is required.