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The data-readiness platform for enterprise AI

AI readiness,
measured—not asserted.

Know whether your data can support a defined AI use case, what is blocking it, what to fix first, and what the fix is worth—without moving connected source data.

Request a Demo See the assessment

Built for regulated industries · Source data stays in your environment · Read-only, in place

The readiness instrument An instrument dial. A calibration arc runs from 0 to 100. The current readiness needle points to 50. A terracotta trace projects from 50 to a target of 58 after two remediation waves. A reticle marks the primary blocker: semantic fragmentation.
Use caseSupervised AI
Current readiness50 / 100
Projected readiness58 / 100
EvidenceNamed tests · cited findings
4 readiness dimensions 50+ analytical algorithms Evidence-traced recommendations Deployed inside your environment
AI Program Success

What evidence says your AI program is ready to scale?

A promising model is not a production system. Scaling depends on an explicit use case, fit-for-purpose data, workflow integration, accountable ownership, feedback and learning, and measurable business outcomes.

No single checkpoint guarantees success. The evidence has to hold across the system.

  1. 01 · Defined use

    The business outcome, decision, workflow, and intended level of autonomy are explicit.

  2. 02 · Data readiness

    The data is semantically clear, statistically sound, contextually valid, and assessed against applicable regulatory and data-integrity expectations for the declared use.

    • Semantic clarity
    • Statistical health
    • Contextual validity
    • Regulatory compliance
    Measured by DATA Compass
  3. 03 · Workflow fit

    The system operates where the work happens rather than beside the workflow.

  4. 04 · Ownership

    Business, operational, and technical accountability are clear.

  5. 05 · Learning

    Feedback and changing context improve the system over time.

  6. 06 · Business outcome

    Operational value is measured after deployment.

DATA Compass direct scope DATA Compass directly measures Checkpoint 02—the data gate. The other five remain program responsibilities.

AI success is a system. Data readiness is a critical gate.

DATA Compass makes that gate measurable—and reassesses it to prove the lift.

Data quality is not data readiness

83/100 Data quality
is not equal to
50/100 Data readiness
for supervised AI
  1. Semantic ambiguity
  2. Statistical risk
  3. Contextual mismatch
  4. Regulatory exposure

The gap is the work.

A dataset can pass conventional quality checks and still be unsuitable, ambiguous, contextually invalid, or non-compliant for a defined AI use case.

Illustrative sample dataset
How It Works

Measurement becomes direction.

Every finding resolves into evidence, a recommended next move, and a measurable reassessment target. Every screen and every number below is the product on one illustrative sample dataset — recorded, not mocked.

01 · Assess

Clean data is not ready data.

83 data quality · 50 data readiness for supervised AI

Data quality scores 83/100—yet technical data readiness for supervised AI is 50, because semantic, FAIR-foundation, and ML-readiness gaps remain. The gap between those two numbers is the argument.

The sample dataset's Data Quality Assessment: 83 of 100 with a seven-component breakdown and cleanliness at 45 percent
Sample data · The Data Quality read: 83/100 with cleanliness at 45% — the weak component, named.
02 · Diagnose

The gaps, named and evidenced.

Cross-system semantic and metadata fragmentation

11 event-type variants · 3 severity scales · 4 of 18 columns mapped

Every failed check carries its evidence: the interoperability pillar at zero, no machine-readable identifiers or vocabulary bindings — the fragmentation made visible, rule by rule, with review controls on each verdict.

The sample dataset's FAIR Evaluate view: interoperability pillar at zero, rule-by-rule verdicts with evidence and review controls
Sample data · FAIR Evaluate: 7 of 16 rules passing, interoperability at zero — every verdict carries its evidence.
03 · Act

The fix arrives with the finding.

Establish a governed semantic foundation across source systems.

Recommendations arrive ranked, with impact and effort attached — and a business-value calculation offered across the full set, so the first thing you do is the thing that moves readiness most.

The sample dataset's recommendations: impact and effort on every card, with a business-value calculation offered across all of them
Sample data · 15 recommendations with impact and effort on every card — and a business-value calculation over the lot.
04 · Prove

Reassessed. The trajectory is the deliverable.

50 → 58 overall · FAIR 31 → 57 · 150 duplicate records removed

Every re-analysis appends a reading: the score trend per dimension and the delta since your last checkpoint — the readiness lift measured, the business value modeled from your assumptions.

The sample dataset's technical trajectory: overall score and FAIR lines across the remediation readings, with the data table beneath
Sample data · The trajectory: 50 → 58 overall, FAIR 31 → 57 across the waves.
Outcomes

Four dimensions. One foundation.

FAIR makes data legible — findable, accessible, interoperable, reusable. Necessary, and not sufficient: a perfectly FAIR dataset can still be statistically broken, wrong for your use, or non-compliant. Compass treats FAIR as the substrate and measures the four dimensions it enables but cannot guarantee.

Declared AI use case Supervised AI Every dimension is read against the use you declare — sample shown.
Semantic Clarity

Does the data mean what it says — to a machine?

Proof · ontology inference with labelled confidence

  • Infer column-to-class alignment before it's tagged
  • Score against your private ontologies — CDISC, IDMP, your taxonomy
  • Interoperability made first-class: declared, inferred, or absent
Statistical Health

Is the data quantitatively sound?

Proof · every score traces to a named test

  • Data quality — eight component algorithms
  • ML readiness — sufficiency, bias, separability, features
  • Named tests: Benford, Pearson, Cohen's Kappa, Shapley
Contextual Validity

Is the data right for your specific use?

Proof · evidence-cited judgment per rule

  • Customer-authoritative Domain Packs, versioned and yours
  • Fit scored against a declared use
  • Deterministic rules first, synthesis only where it must be
Regulatory Compliance

Does the data meet the integrity bar?

Proof · readiness against regulatory expectations — advisory, not certification

  • ALCOA+ — nine data-integrity attributes
  • FDA credibility — seven steps plus Step 8
  • GDPR · HIPAA · CCPA, cited line-by-line
Defensibility

Three reads on one domain.
The disagreement is the finding.

Compass doesn't take the data's word for it. Every FAIR assessment triangulates three independent reads. Where they agree, you have a finding you can defend. Where they diverge, the divergence itself is the insight — and it's named, not hand-waved.

TechnicalWhat the data shows.
PerceivedWhat people experience.
GovernedWhat the documents say.
The three reads resolve to Confidence The Finding
Eight canonical patterns

Eight canonical patterns turn the relationship among Technical, Perceived, and Governed evidence into a shared diagnostic language. Explore a pattern to see what it means and where to look next.

01 Confirmed Maturity Technical High · Perceived High · Governed High

What it means. The data, stakeholder experience, and governance evidence all agree that the domain is strong.

First move. Preserve and propagate. Protect the practices from regression and reuse the controls, artifacts, and working methods that make the domain successful.

02 Aligned Maturity (suspected) Technical High · Perceived High · Governed Not yet gathered

What it means. Technical evidence and stakeholder experience agree on high maturity, but governance evidence has not yet been reviewed.

First move. Review the governance documents. Determine whether the pattern resolves to Confirmed Maturity or Unformalized Practice.

03 Greenfield Technical Low · Perceived Low · Governed Low or deliberately absent

What it means. All available signals agree that the domain is early-stage. This is an investment decision, not necessarily a remediation problem.

First move. Decide whether to invest or accept the current state. Do not treat every Greenfield domain as broken.

04 Usability Gap Technical High · Perceived Low · Governed High

What it means. The data and governance evidence appear strong, but the people who depend on the data cannot use it productively.

First move. Examine discoverability, training, UX, and the path from the catalog entry to the practitioner.

05 Tribal Knowledge Technical Low · Perceived High · Governed Deliberately absent

What it means. The domain works because a small number of people know its quirks, while metadata and formal governance remain thin or deliberately absent.

First move. Capture the practice. Externalize the knowledge while the subject-matter experts are still available.

06 Unformalized Practice Technical High · Perceived High · Governed Low

What it means. The data is strong and the team operates effectively, but the working method has not been formally documented.

First move. Codify what already works. Write the SOP around the real operating practice rather than replacing it with a generic template.

07 Abandoned Capability Technical High · Perceived Low · Governed Deliberately absent

What it means. A technically sound asset remains, but it has lost users, ownership, and governing practice.

First move. Make a decision. Reinvest around a current use case or formally retire the capability.

08 Policy Theater Technical Low · Perceived Low · Governed High

What it means. Governance documentation describes a mature system that the data and operating experience do not support.

First move. Audit practice against policy. Reconcile the documented state with operational reality.

Patterns are diagnostic, not directive. Recommended first moves are starting points for discussion with the people who run the domain.

Security & Deployment

Your data stays. Compass comes to it.

Compass deploys inside your environment and connects to your platforms read-only. Analyses run through your own compute. Connected source data stays in the systems that own it—no ETL and no copies out. When you provide a file, that authorized copy is processed inside your deployment boundary.

ConnectRead-only, in place.Unity Catalog, Snowflake, Veeva — credentials AES-256 encrypted.
Analyze50+ algorithms on your warehouse.Results return as scores, evidence, and plans — not copies of your data.
AI you controlIn-environment or customer-approved AI.Bridge AI can run against a model endpoint inside your own environment.
Off by default — the external path exists only through explicit approval.
Read-onlyConnected sources are queried, never written — and never copied out.
In your environmentCompass deploys inside your boundary; results stay there too.
Encrypted secretsCredentials AES-256 encrypted, key-vault managed.
AuditableAnalyses, decisions, and overrides carry an audit trail.
Evidence-tracedEvery finding cites the check and the data that produced it.

How much autonomy can your data support?

Higher autonomy raises the evidence bar.

Level 1AdvisoryAI suggests; a human decides everything.
Level 2CopilotAI drafts; a human reviews and approves.
Level 3SupervisedAI acts within bounds; a human monitors.
Level 4AutonomousAI acts; humans audit the trail.
Integrations

Connect your data where it lives.

Connected sources are assessed read-only and in place. Customer-provided files stay inside your deployment boundary.

Databricks

Unity Catalog browser, SQL profiling, MLflow assessment.

Snowflake

Database and schema browser with SQL API profiling.

Veeva Vault

QMS, CDMS, and RIM module adapters with OAuth2 SSO.

Stardog

SPARQL querying, ICV validation, reasoning-powered analysis.

Files, APIs, and pipelines — drag-and-drop CSV, Excel, Parquet, and JSON, or a REST API for CI/CD.

Know before you deploy.Measure data readiness. Ship AI on evidence.

Start with a technical data-readiness assessment in minutes. No code changes, no movement of connected source data, no black boxes.

Request a Demo

We'll walk you through a technical data-readiness assessment on an illustrative dataset—read-only, in place.