AI is colliding with regulation. 87% of AI projects never reach production — in healthcare, life sciences, and federal markets, the blocker isn't the model, it's the unsigned data decisions beneath it. HIPAA, 21 CFR Part 11, FedRAMP, CMMC L2 §3.3, and the EU AI Act are all converging on the same requirement: every transformation must be attributable, version-pinned, and tamper-evident.

Compliance is baked into the data structure. Every mapping decision is ULID-stamped, electronically signed by a named SME with role-scoped permissions (21 CFR Part 11 §11.200), SHA-256 hash-chained so historical tampering is mathematically detectable, and version-pinned to the exact source and target schemas. An independent verifier script ships with the evidence pack so regulators can re-walk the chain without runtime access to Magellan.

Organizations where an unsigned data mapping is a compliance failure: healthcare interoperability (health systems, HIEs, EHR vendors, ONC-regulated platforms), life sciences under FDA 21 CFR Part 11 and EU Annex 11, federal and defense (FedRAMP, CMMC L2 §3.3 AU-2/AU-3/AU-9/AU-11, ICAM), and financial services under SOX and Basel III/IV. Total addressable market across these regulated verticals is over $40 billion annually.

The system of record for how data changes in regulated enterprises

The system of trust for data transformations

Guarantee data integrity and auditability across every pipeline.

Predictive mapping proposed by our Semantic Match Engine, approved by named SMEs, and recorded in a ULID‑keyed, SHA‑256 hash‑chained ledger — built for regulators in healthcare, life sciences, and federal.

Problem

87% of AI projects never ship in regulated industries — the mapping beneath is an unsigned decision.

Magellan's fix

Every mapping signed, version‑pinned, and chained — audit prep becomes a deterministic export.

Semantic Match Engine

hr_v3.2.0 → workforce_v1.4.0

Ready

employee_idstaffIdentifier99%

hire_datestartDate97%

department_codedivisionCode94%

job_titlepositionTitle92%

salary_amountcompensation.base88%

Matching 0 of 5 fields…

Audit & Trust Ledger

SHA‑256 · ULID

01JQ8X4N2K…draft.createl.cahlander
01JQ8X4P9R…match.proposesemantic-engine
01JQ8X4QM2…review.signs.patel · SME
01JQ8X4S8V…publish v1.4.0a.nguyen · steward

Building hash chain…

Why this matters

Plain‑language: why provenance, lineage, and audit trails matter

Every important decision an organization makes — a diagnosis, a payment, a hire, a forecast, a regulatory filing — is built on data that traveled through many hands and many systems before it got there. Magellan exists so that journey is never a mystery.

Data provenance

Where did this number come from?

When a board sees a revenue figure, a doctor sees a lab result, or a regulator sees a risk score, somebody has to be able to point at the original source. Without provenance, you're trusting a number nobody can vouch for.

Data lineage

What happened to it on the way here?

Data is rarely used the way it was captured. It gets joined, filtered, renamed, rounded, and reshaped across dozens of systems. Lineage is the receipt for every one of those steps — so a wrong answer can be traced back to the exact transformation that broke it.

Audit trail

Who changed what, when, and why?

Mappings and rules change constantly. An audit trail turns those changes from tribal knowledge ("ask Dave, he set that up in 2019") into a permanent, tamper‑evident record that holds up in front of an auditor, a regulator, or a judge.

Tamper‑evident ledger

Can anyone quietly rewrite the history?

A log you can edit isn't evidence — it's a suggestion. A hash‑chained ledger means any attempt to alter past records breaks the chain and is instantly detectable. That's the difference between "we think this is what happened" and "we can prove this is what happened."

Role‑separated governance

Did the right people sign off?

The person who proposes a mapping shouldn't be the same person who approves it, and neither should be able to silently overwrite the audit record. Separation of duties is how regulated industries prevent both honest mistakes and quiet fraud.

Why it matters

What goes wrong without it?

Failed audits. HIPAA fines up to $1.5M per violation category. SOX restatements. Months of forensic work to answer a single GDPR erasure request. AI models trained on data nobody can explain. Every one of these starts the same way: nobody could prove where the data came from or what was done to it.

The bottom line: Spreadsheets, tribal knowledge, and ETL scripts can move data around. They can't prove what happened. Magellan turns every mapping decision and every transformation into a ULID‑keyed, hash‑chained record — so when somebody asks "how do you know?", the answer takes seconds, not weeks.

See it in action

A tamper‑evident ledger you can try right now

Try editing a past entry. The SHA‑256 chain breaks and every downstream block lights up red.

Live Demo

Verify the chain in your browser

Four sample ledger entries, hashed with SHA‑256 in the Web Crypto API. Toggle a row to tamper with its content and watch every downstream hash fail.

Computing SHA‑256…

Transformation debt

Every undocumented mapping is a payment you have not made yet

Technical debt usually means old code. In data work it means something narrower and more expensive: transformation logic that exists only as a script and a memory. It does not show up in a dependency report. It shows up the day a source schema changes and nobody can say which of the last four mappings was the right one — or who decided.

The re-mapping tax

Every new target system re-opens every past mapping decision. A source schema that has been remapped four times is four sets of choices nobody can reconstruct, and the fifth target pays for all of them. Mappings pin the exact source and target schema versions they were built against, so a decision made once stays valid — and a decision that has to change is recorded as a change, not a silent edit.

Logic you cannot find

Transformation logic normally lives in scripts, stored procedures, and the memory of whoever wrote it. When that person leaves, the only way to learn what a field means is to reverse-engineer it from output. In Magellan the mapping is the artifact: versioned, diffable, and readable without running anything.

Drift nobody noticed

A column changes meaning, a code set gains a value, a unit shifts from pounds to kilograms. Nothing fails loudly, so the mapping quietly keeps producing plausible numbers. Because every version carries a ULID and a recorded decision, drift becomes something you can ask a question about instead of something you discover in an incident review.

What it turns into

Debt is the cost. A trustworthy history is the return.

Stopping the accumulation is only half of it. The other half is that the decisions you already made become evidence — attributable to a named person, pinned to the exact schema versions in force, and reproducible years later after both schemas have moved on. Each entry is ULID-keyed and SHA-256 hash-chained, so altering a past record breaks the chain. That is what makes the record usable to somebody who has to sign something: HIPAA §164.312(b), 21 CFR Part 11 §11.10(e), or the CMMC L2 §3.3 AU-family controls.

Without a governed record

Mapping logic in scripts and spreadsheets, owned by whoever wrote it
Approval asserted in an email thread or a wiki page
Rejections and overrides discarded, so the record shows only the winner
History stored in a table your own team can edit
Proving a past decision takes weeks of forensic work

With a versioned, hash-chained record

Mapping is a versioned artifact, pinned to the exact schema versions it used
Approval is an entry in the ledger, signed under a named role
Rejections are kept with their reasons, so judgment is visible
History is hash-chained and sealed to WORM storage with fixed retention
Proving a past decision is running one verifier script

The order is not decorative. Every schema version a mapping pins must be Approved & Published before that mapping version can enter approval — so the sequence itself is part of the evidence, not a process someone promises to follow.

Hold the thing itself

A sample evidence pack, with nothing held back

Eleven ledger entries covering one mapping version from spreadsheet import to published and sealed record: both schema approvals, the Semantic Match Engine's suggestions, the three rejected links with their reasons, the release signature, and the WORM seal. The tenants, people, and identifiers are synthetic. The cryptography is not — every hash was computed from the payloads printed in the pack, and the verifier re-derives them without Magellan running.

Evidence pack (PDF, 17 pp.)Machine-readable record Verifier script

Run it yourself

$ python3 verify_chain.py \
    magellan_sample_evidence_pack.json

[PASS] #01 SchemaIngested ... [PASS] #11 WormHeadFlushed
[PASS] partition last_hash == final entry
[PASS] WORM head == recomputed chain head

RESULT: CHAIN INTACT

Standard library only — no API keys, no network, no database. Run it with --tamper and it changes one character inside one payload, then reports the chain broken from that entry onward.

The full inventory: seven debts Magellan retires·See the mapper these entries describe·Read the white paper·Talk to the founder

See It In Action

Watch the Highlight Reel

A quick look at how Magellan transforms metadata management — featuring our Semantic Match API with live confidence scoring.

The Control Layer

Every control an auditor will ask for

Magellan is the audit‑ready mapping and metadata control layer — every schema ingested, every mapping decision, and every transformation written to a ULID‑keyed, hash‑chained Audit & Trust Ledger with role‑separated governance built for HIPAA §164.312(b), 21 CFR Part 11, and CMMC L2 §3.3 AU‑family controls (AU‑2, AU‑3, AU‑9, AU‑11).

1

Import & Discover

Bring in any schema, from any source

Multi‑Format Schema Ingestion

Every ingest writes a SchemaIngested entry to the ULID‑keyed, SHA‑256 hash‑chained audit ledger — Avro, XML Schema, JSON Schema, Excel, Erwin XML, JDBC, or a custom importer. The source fingerprint is stamped into the entry; nothing reaches the MetaModel without a ledger row.

Automated Lineage

Lineage is derived from the audit ledger, not reconstructed after the fact. Every discovered edge writes a LineageEdgeDerived entry — so the lineage graph an auditor sees is the same graph the application uses, with the same hash chain backing it.

Intelligent Search

Search runs against the projection of the ledger, not a parallel index that can drift. Index rebuilds are themselves SearchIndexRebuilt entries — so a reviewer can prove which version of the catalogue answered any historical query.

2

Map & Transform

Shape your data from source to target

Source‑to‑Target Mapping

Every draft, edit, and field‑level change writes MappingDrafted or MappingRevised to the ledger — including the actor's role at the moment of the action. There is no silent overwrite, no lost authorship, no after‑the‑fact reconstruction.

Predictive Mapping

Every Semantic Match suggestion is replayable from the ledger. SemanticSuggestionReturned and SemanticSuggestionAccepted entries record the model identifier, threshold profile version, confidence, and accepting actor — enough to reconstruct any decision under HIPAA §164.312(b), 21 CFR Part 11 §11.10(e), and CMMC L2 §3.3 AU‑3 (Content of Audit Records).

Entity Enrichment

Annotations are first‑class versioned properties of the MetaModel. Every enrichment writes EntityAnnotated to the ledger, so the parent‑context, abbreviation, and domain vocabulary that a Semantic Match suggestion depended on can be replayed exactly as it was at the moment of decision.

Visual Rules for Targets

Every rule definition and edit writes RuleDefined or RuleRevised to the ledger. Because the rule lives next to the mapping it governs, an auditor can reproduce the exact transformation logic that was active at any historical point.

Impact Analysis

Impact reports are derived from the same hash‑chained ledger that drives lineage. Every report writes an ImpactAnalysisRun entry pinned to the ledger ULID it was computed against — so a stakeholder review is reproducible months later.

Service Integration

Every push or pull through a web service writes ArtifactExported or ArtifactImported to the ledger, stamped with the source ULID. ETL platforms downstream can prove which Magellan version their generated code was derived from.

3

Visualize & Explore

See your data from every angle

Data Lineage Exploration

The lineage graph is a projection of the ledger — pinned to a specific ULID range. LineageGraphRendered entries record what a viewer saw and when, so historical lineage views are reproducible bit‑for‑bit.

Hierarchical Schema Inspection

Switching between business, logical, and physical views never mutates the underlying schema. Every view selection writes SchemaViewRendered, so a reviewer can see exactly which abstraction a stakeholder was looking at when they made a decision.

Source‑to‑Target Layouts

Every layout export writes LayoutExported to the ledger with the mapping ULID and the export format. The PDF a reviewer signed off on can be tied back to the exact mapping version it was generated from.

4

Govern & Integrate

Control, trust, and automate your data

Data Provenance

Provenance is the audit ledger. Every change writes an entry with the actor's role at time of action, a SHA‑256 hash of the canonical payload, and the prior chain hash — so the provenance an auditor sees cannot have been silently rewritten.

Draft & Publish Workflow

Every state transition is a ledger entry. DraftSaved, ApprovalRequested, ApprovalGranted, ApproverReleaseSigned — the workflow state machine and the audit trail are the same artifact, satisfying 21 CFR Part 11 §11.200's two‑component signature requirement.

Schema & Map Version Control

Versions are derived from the ledger, not the other way round. SchemaVersionPublished and MappingVersionPublished entries are the canonical source of every semantic version increment — patch, minor, or major — and the diff between them is a hash‑checkable subsection of the chain.

Governance Built In

Classification labels (PII, PHI, confidential) live in the ledger, not in a sidecar table that policy could quietly bypass. ClassificationApplied and PolicyEnforced entries make compliance posture replayable, not just visible on a dashboard.

RBAC through Lines of Business

Seven roles, mapped to two Postgres database roles. Every grant, revoke, and role-at-time-of-action stamp is a ledger entry — so HIPAA §164.308(a)(2), 21 CFR Part 11 §11.10(j), and CMMC L2 §3.3 AC‑5/AU‑9 accountability is enforced by the data structure, not by application discipline.

Hierarchical Access Groups

Group membership and SSO assertions are ledger‑stamped. AccessGroupChanged and SsoAssertionRecorded entries make it possible to prove which group an actor belonged to — and which IdP authenticated them — at the moment of any historical action.

CI/CD & Automation

CI/CD pulls signed specs, not raw mappings. ReleasePromoted entries close the chain for each environment promotion, and every promotion carries the ULID of the ApproverReleaseSigned entry it was authorized by.

CMMC L2 §3.3 · Audit & Accountability

How the ledger satisfies AU-2, AU-3, AU-9, and AU-11

Magellan does not claim CMMC certification — it produces the AU-family evidence a prime contractor's assessor needs. Each control below pairs the plain-language requirement with a concrete Magellan example and the exact ledger artifact you hand the assessor.

CMMC §3.3 AU-2

Event Logging

Control: Identify the types of events the system must log and keep that event catalog under review.
Magellan example: Magellan publishes a fixed event catalog: SchemaImported, MappingProposed, SemanticSuggestionReturned, SemanticSuggestionAccepted, SemanticSuggestionRejected, ThresholdProfileUpdated, RoleAssigned, ActorAuthenticated. Every catalog change is itself a ledger entry, so the catalog under review is the catalog enforced.
Evidence the assessor pulls: ledger.event_catalog view + audit_ledger entries WHERE event_type = 'EventCatalogUpdated'

Definition in glossary →

CMMC §3.3 AU-3

Content of Audit Records

Control: Each audit record must capture what happened, when, where, the source, the outcome, and the identity of the user or process responsible.
Magellan example: A SemanticSuggestionAccepted entry carries: a ULID (millisecond-precise, lexicographically sortable), source/target schema versions and attribute paths, candidate set with confidence scores, model identifier (google/gemini-2.5-flash), API version, active threshold-profile version, the authenticated actor, and that actor's role-at-time-of-action. Sufficient to replay the decision four years later.
Evidence the assessor pulls: audit_ledger row → JSON columns: ulid, event_type, payload, actor_id, actor_role_at_time, occurred_at, prev_hash, this_hash

Definition in glossary →

CMMC §3.3 AU-9

Protection of Audit Information

Control: Audit information and audit tools must be protected from unauthorized access, modification, and deletion.
Magellan example: The ledger lives in a dedicated Postgres role with INSERT-only privileges. Append-only is enforced twice — by REVOKE UPDATE, DELETE at the role level and by a BEFORE UPDATE OR DELETE trigger that raises an exception. Each row binds the SHA-256 of the prior row, so a retroactive edit breaks the chain on verification. The chain head is mirrored to a WORM secondary sink (S3 Object Lock); on conflict, the WORM head wins and the divergence is treated as an incident.
Evidence the assessor pulls: Hash-chain verification report (SELECT verify_chain(start_ulid, end_ulid)) + S3 Object Lock retention policy + Postgres role grants

Definition in glossary →

CMMC §3.3 AU-11

Audit Record Retention

Control: Audit records must be retained for a defined period consistent with records-retention policy to support after-the-fact investigations and meet regulatory requirements.
Magellan example: Magellan's ledger is append-only by design — entries are never overwritten or pruned. Retention windows are configurable per tenant to align with the strictest applicable regime (HIPAA 6-year minimum, 21 CFR Part 11 record-lifetime, DoD CUI per contract). The configured retention is itself a ledger entry, so the retention policy in force at time-of-decision is auditable.
Evidence the assessor pulls: tenant_retention_policy table + audit_ledger entries WHERE event_type = 'RetentionPolicyUpdated' + WORM sink lifecycle rules

Definition in glossary →

Evidence-to-control mapping

Every artifact below is produced by the running Magellan deployment — no separate audit-prep workstream. The CMMC L2 §3.3 controls each artifact satisfies are listed alongside.

Ledger artifact	Satisfies
Event catalog (ledger.event_catalog view)	AU-2
Audit ledger row (ULID + payload + actor + role-at-time)	AU-3
Hash-chain verification report (verify_chain RPC)	AU-9
WORM sink (S3 Object Lock) chain-head mirror	AU-9, AU-11
Postgres role grants (INSERT-only audit role)	AU-9
Tenant retention policy + RetentionPolicyUpdated entries	AU-11

The same chain also satisfies HIPAA §164.312(b) and 21 CFR Part 11 §11.10(e). One ledger, three regimes.

Schema Formats

Bring any schema, from any source

Every ingested schema is normalized into Magellan's unified MetaModel and stamped with a SchemaIngested ledger entry — so the same Semantic Match, lineage edge derivation, and ULID‑keyed audit trail apply whether the source was Avro, JDBC, or a proprietary Erwin XML.

Avro XML Schema JSON Schema Excel Erwin XML JDBC Custom Import

Don't see your format? Magellan ships a documented ImportAdapter interface — implement parse(): MetaModel and the format inherits the same MetaModel projection, Semantic Match, and ledger writes as the built‑in adapters.

Mapping Import

Start with what you already have

Most teams already maintain source‑to‑target mappings in spreadsheets. Magellan lets you import them directly — preserving your existing work while unlocking Semantic Match suggestions, lineage derivation, and the ULID‑keyed audit trail.

Import Existing Spreadsheets

Bring your current source‑to‑target mapping spreadsheets directly into Magellan — no rework, no re‑keying. Each import atomically creates the source schema(s), the target schema(s), and a mapping (all sharing one version number) and writes MappingImported to the ULID‑keyed ledger so the spreadsheet that was the prior source of truth becomes a hash‑checkable provenance artifact.

Automatic MetaModel Conversion

Imported mappings are normalized into Magellan's governed MetaModel — gaining version control, lineage derivation, and impact analysis from the first ingest. The conversion writes MetaModelProjected entries so the projection is replayable from the ledger alone.

Validation & Enrichment

Imported mappings are validated against target schemas, type-checked, and annotated with parent-context, abbreviation, and domain-vocabulary metadata before they reach a Contributor's queue — every check writes a ValidationRunCompleted entry to the ledger.

Immediate Predictive Enhancement

Once imported, the Semantic Match Engine proposes mappings for the rest of the target schema, lineage derivation activates, and impact analysis runs — extending the value of work your team has already done. Every model call writes a SemanticSuggestionReturned entry to the ledger.

Integration

Hand your ETL stack a hash‑chained, audit‑ready spec

Magellan emits the governed mapping artifacts your existing ETL/ELT tools already accept — every export carries the ULID and ledger entry it was generated from, so the transformation that runs in Informatica, dbt, or Glue is the one an auditor can trace back to a signed decision.

Magellan

Governed Metadata

Machine‑Readable Output

YAML · JSON · XML · API

Your ETL/ELT Tools

Execute & Deploy

Informatica & Talend

Export governed mappings as Informatica PowerCenter XML or Talend .item definitions. Each export filename and embedded comment carries the source ULID and ApproverReleaseSigned entry hash, so an auditor can prove the transformation running in production matches the version a Magellan Approver actually signed.

dbt & Modern Data Stack

Generate schema.yml plus dbt model SQL from a signed Magellan mapping version. The generated header pins the ULID range and SHA‑256 chain head, so dbt build against a stale spec fails CI before it ever touches the warehouse.

NiFi, Azure Data Factory & AWS Glue

Emit NiFi flow templates, Azure Data Factory linked-service JSON, and AWS Glue job scripts — each artifact embeds the source mapping ULID and is regenerable byte-for-byte from the ledger, so a change in the runtime that didn't originate in Magellan is immediately detectable.

CI/CD Pipelines

A GitHub Actions or GitLab CI step calls the Magellan API with the target environment's pinned mapping ULID. The job fails if the artifact in the repo doesn't hash-match the signed ledger entry, blocking promotion of any transformation that wasn't approved under role-at-time-of-action RBAC.

Quick Start

Open Informatica Cloud Mapping Designer

Five steps to reach the Mappings area in Informatica Intelligent Data Management Cloud (IDMC), where Magellan-emitted PowerCenter XML and IDMC mapping specs are imported.

Why Magellan: Mapping Designer is a developer surface. In Magellan the subject-matter expert proposes, reviews, and approves the semantic match — the PowerCenter XML or IICS task is emitted from the signed decision.

1
Sign in to IDMC
Open your regional pod — dm-us.informaticacloud.com (US), dm-em.informaticacloud.com (EMEA), or dm-ap.informaticacloud.com (APAC). Use your IDMC org credentials or SSO.
2
Open the My Services launcher
From the IDMC home page, click the service tile launcher in the upper-left and select Data Integration.
3
Switch to the Explore view
In Data Integration, click Explore in the left navigation. This is the project/folder browser where all assets — mappings, mapping tasks, taskflows — live.
4
Filter by Mappings
Choose your project and folder, then set the Asset Type filter to Mappings. You'll see every mapping in that folder, including any imported from a Magellan ApproverReleaseSigned export.
5
Open the Mapping Designer
Click an existing mapping to open the Mapping Designer canvas, or click New ▸ Mappings ▸ Mapping to create one. The canvas is where source, transformation, and target objects from a Magellan-emitted spec are wired together.

Reference: Informatica Mapping Designer documentation

How It Works

Import. Map. Govern. Every step on the ledger.

Three steps from raw schema to a ULID‑keyed, SHA‑256 hash‑chained record an auditor can verify — with role‑separated approval at every transition.

1

Import

schemas + spreadsheets

2

Map

semantic match + SME approval

3

Govern

ULID + hash‑chained ledger

SHA‑256 hash‑chained ledger

01JQ8X…draft.create→

01JQ8Y…review.sign→

01JQ8Z…publish.v1.4.0→

01JQ90…run.transform→

01JQ91…audit.export

01

Import

Upload Avro, XML Schema, JSON Schema, Excel, Erwin XML, or connect via JDBC. Each ingest writes a SchemaIngested entry to the ledger — ULID‑keyed, hash‑chained, and stamped with the source fingerprint — so nothing reaches the MetaModel without a signed provenance row.

SchemaIngestedSchemaParsedSchemaVersionPublished

02

Map

Domain experts draft source‑to‑target mappings with Semantic Match suggestions. Every draft, edit, reviewer comment, and field‑level change appends MappingDrafted, MappingRevised, or ReviewerCommented entries to the chain — full provenance, no silent overwrites.

MappingDraftedMappingRevisedFieldMappingChanged

03

Govern

Approvers sign releases under role‑at‑time‑of‑action RBAC. ApproverReleaseSigned closes the chain for that version, ReleasePromoted records each environment promotion, and CI/CD pulls the signed spec — no untracked path to production.

ApprovalRequestedApprovalGrantedApproverReleaseSigned

The system of trust for data transformations

We say predictive mapping — not "AI‑powered."

Plain‑language: why provenance, lineage, and audit trails matter

Data provenance

Data lineage

Audit trail

Tamper‑evident ledger

Role‑separated governance

Why it matters

A tamper‑evident ledger you can try right now

Verify the chain in your browser

Every undocumented mapping is a payment you have not made yet

The re-mapping tax

Logic you cannot find

Drift nobody noticed

Debt is the cost. A trustworthy history is the return.

A sample evidence pack, with nothing held back

Mappings are first‑class, versioned assets — not buried in ETL scripts.

Versioned by design

Part 11, HIPAA & CMMC ready

Auditor‑grade evidence

Watch the Highlight Reel

Every control an auditor will ask for

Import & Discover

Multi‑Format Schema Ingestion

Automated Lineage

Intelligent Search

Map & Transform

Source‑to‑Target Mapping

Predictive Mapping

Entity Enrichment

Visual Rules for Targets

Impact Analysis

Service Integration

Visualize & Explore

Data Lineage Exploration

Hierarchical Schema Inspection

Source‑to‑Target Layouts

Govern & Integrate

Data Provenance

Draft & Publish Workflow

Schema & Map Version Control

Governance Built In

RBAC through Lines of Business

Hierarchical Access Groups

CI/CD & Automation

How the ledger satisfies AU-2, AU-3, AU-9, and AU-11

Evidence-to-control mapping

Bring any schema, from any source

Start with what you already have

Import Existing Spreadsheets

Automatic MetaModel Conversion

Validation & Enrichment

Immediate Predictive Enhancement

Hand your ETL stack a hash‑chained, audit‑ready spec

Informatica & Talend

dbt & Modern Data Stack

NiFi, Azure Data Factory & AWS Glue

CI/CD Pipelines

Open Informatica Cloud Mapping Designer

Sign in to IDMC

Open the My Services launcher

Switch to the Explore view

Filter by Mappings

Open the Mapping Designer

Import. Map. Govern. Every step on the ledger.

Import

Map

Govern

The three questions every investor asks

Why now?

How is it auditable?

Who is it for?

Have questions?