For Chief Data Officers

Every migration, visible. Every artifact, reversible.

LucidOntos turns migrations from opaque, multi-quarter programs into a portfolio you can govern — with module-level evidence, reversible artifacts, and policy controls baked in.

The pain today

  • Portfolio-wide migration status lives in PowerPoint, not the workspace.
  • PII handling is decided ad-hoc per project, not enforced as policy.
  • When a target changes (Snowflake → Databricks), the cost is a full rewrite.
  • Reconciliation between source and target is a one-off script, not a contract.
  • Audit asks 'what changed and why' and there's no version-controlled answer.

What LucidOntos delivers

  • Single workspace with module-level outputs across every active migration.
  • Portable ontology decouples source from target — change targets without rewriting consumers.
  • Reversible, version-controlled artifacts: ontology, mappings, generated code, DAGs.
  • PII masking expressed once and enforced env-conditionally across all generated code.
  • Per-batch reconciliation rules emitted with the pipeline, not bolted on after.
  • Human-readable AI Health Analysis when a load drifts — explainable to risk & audit.

Artifacts you'll review

Migration portfolio view

Status, milestone burndown, blockers, and SLAs across every active engagement.

Reversibility report

What can be re-generated, what is hand-edited, and what would cost a rewrite.

Policy & PII matrix

Which fields are masked, in which environments, with which rule — across migrations.

Audit trail

Every plan, ontology change, mapping approval and code regeneration is timestamped and signed.

Reconciliation evidence

Per-batch row counts, checksums, key aggregates — exportable for risk & audit.

Cost-of-change view

Switch a target from Snowflake to Databricks: estimated rework before you commit.

A typical Oracle → Snowflake finance warehouse — 5 entities, 15 mappings, SCD2 on Customer & Account, PII masked in non-prod, reconciled per batch — moves from prompt to deploy-ready in days, not quarters. The same ontology re-targets to Databricks without rewriting a single consumer.

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