FactVerse Scout
FactVerse Scout is the data discovery and onboarding workspace in DFS Pro. It brings sources, equipment identity, field meaning, model points, and review records into one workflow so data engineers and domain experts can turn fragmented data into trusted operational context.
If you are new to Scout, begin with Your first equipment check. Start with one asset or a small scope, inspect the available data, confirm what the fields mean, and assign open questions to the right owners.
Your tenant must have DFS Pro and Scout enabled with the appropriate user permissions. AI-assisted exploration also requires an approved model service configured for the platform.
Choose a workflow
| Business need | Workflow | Main outcome |
|---|---|---|
| Understand what data is available for equipment | Explore available data in Your first equipment check | Source and field inventory, open questions, and matching suggestions |
| Assess data for a specific application | Equipment check with a published business objective | Required signals, gaps, validation results, and application-use evidence |
| Onboard business files or tables | Data onboarding | Inventory, field and identity decisions, dry-run results, and a dataset version |
| Connect a Brick model to operational data | Brick models and data | Model changes, point associations, and publication records |
| Process a recurring delivery | Subsequent deliveries | Comparison with the active version, review outcome, and a new version when required |
How Scout works
Each Scout task preserves its scope, evidence, and decisions. AI can organize fields, compare context, raise clarification questions, and prepare suggestions. Domain experts confirm asset identity, units, measurement location, and business meaning. When publication is required, an approver reviews the change and a release operator applies it.
Connector configuration, ingestion, and application enablement remain in their respective DFS or application workflows. Scout references those capabilities and records the resulting associations so ownership and evidence stay clear.
Understand task outcomes
| Status | What it means | Recommended next step |
|---|---|---|
| Discovered | Authorized sources in the selected scope were inspected | Review omissions and confirm field meaning |
| Proposal accepted | A reviewer confirmed the suggested content | Complete validation and prepare publication |
| Publication applied | The selected version or association is active | Check source readings and downstream use |
| Reading observed | The source returned values in the selected time range | Review freshness, coverage, and quality |
| Application verified | The target application used the approved data | Preserve acceptance evidence and move into operations |
Historical coverage, current freshness, and application use are separate evidence. A useful handoff records the scope, observation time, confirmed decisions, and remaining questions for each one.
Continue by role
- Data operator: Your first equipment check, Data onboarding, and Operate and review
- Domain or model expert: Brick models and Evidence and decisions
- Administrator or deployment engineer: Administration, Private deployment, and API and integration
- Project lead: Troubleshooting and acceptance