Data Validation

Use Validation Checks and Standards to validate models and track results over time.

This page covers Data Validation concepts, core objects, setup flow, and project-level entry points. It does not cover dashboard widget validation paths; for widget-based validation, see Validation Widgets in Dashboards (draft).

Why teams adopt it

Use it when validation rules must stay stable across iterations or across people—for repeatable QA, shared predicates and thresholds, and pass-rate comparison as versions ship.

Core objects

Validation checks execute saved rules against tracked models when you run manually or when auto-run triggers on new versions.

Standards store project-scoped rule templates that you reuse when creating checks.

A result captures one completed run with scores, per-rule statuses, counts, and failing element links.

History orders past results for a check so you can compare trends across versions.

Quickstart

The end-to-end flow is the single setup path from authoring to result review.

End-to-end flow

You need at least one published model version in the project.

1. Choose mode

   Open Data Validation and pick check or standard.
2. Author or import rules

   Use WHERE / CHECK, or import IDS, COBie, or Speckle bundles.

3. Run preview

   Validate filters on real data before save. Run Preview or Ctrl+Enter / Cmd+Enter.

4. Save

   Check: name, optional description, scoped models, manual or auto-run on new versions. Standard: stored for spawning checks later.

5. Track results

   Open the check result page; expand rules and models to see filter-step counts when debugging failures.

6. Share outcomes

   Export BCF from result views where coordination tooling expects it.

After the first pass

For authoring details and run analysis, see Checks (including predicates and matching), Standards, and Viewing Results.

To fetch check results and KPIs programmatically (GraphQL and Python), see Consuming Data Validation Results via GraphQL.

Navigate from project sidebar

In the project sidebar:

• Data Validation
• Run check — draft / preview without persisting yet
• Checks — saved checks (name and description editable; rules and scope frozen—see Checks)
• Project standards — create / import standards

Progressive implementation sequence

Explore — run preview on real revisions first so scope errors show before saving (Run a first check).

Persist — save against tracked models so reruns stay comparable over time (Operate checks over time, Practical review flow).

Scale — reuse one standard across checks so shared logic stays aligned (Standards to checks flow).

Start here

Run and manage checks

Rules, predicates, preview, thresholds, saves, tracked models.

Create project standards

Templates and interoperability imports inside a project.

Interpret results

Latest versus history, statuses, drills.

Using dashboard widgets instead?

Dashboard widget validation is documented in Validation Widgets in Dashboards (draft).

Plan availability

Caps differ mainly on whether checks persist and whether multiple models attach per check.

What Data Validation features do I get on each plan?

Explore (Free): in-session authoring and preview only; no saved checks or preserved history.

Team (Business): saved checks and trends for one tracked model per check.

Enterprise: multi-model checks and project standards on the same authoring stack.

FAQ

Should I start with a check or a standard?

Start with a check when you are validating one concrete requirement against active models. Start with a standard when you want reusable rules that multiple checks can inherit.

Do checks run automatically?

Checks can run in manual mode or auto-run on new model versions, depending on check trigger settings.

Where do I inspect detailed outcomes?

Open the check detail page to inspect latest and historical rule outcomes. See Viewing Results.

Can I still use dashboard widgets for validation?

Yes. Widget-first validation is documented in Validation Widgets in Dashboards.