DataVault
Trusted Migration Data with DataVault
Preserve every important data state from extraction through target acceptance. DataVault provides source-to-target lineage, reconciliation, transformation evidence, masking and governed migration snapshots in one controlled data foundation.
What is DataVault?
DataVault is the governed data layer used by SyntraETL to retain and connect data from the source system, transformation process, generated migration output and target application. It gives project teams a traceable explanation of where each target value originated, how it changed and whether it was successfully accepted.
DataVault does not:
- — Replace the source application during migration
- — Perform extraction or loading — that is DataMove
- — Only store final migration files
- — Treat reconciliation as a spreadsheet produced after the event
It creates a connected evidence chain across the migration lifecycle.
Project experience
DataVault in Production
300+ successful enterprise data projects delivered · Migration · Archival · Reconciliation · Decommissioning
Migration, archival, reconciliation and decommissioning experience across ERP, HCM, payroll and enterprise platforms.
- SAP ECC Legacy source platform
- Oracle Fusion Target cloud platform
- Multi-cycle Rehearsals before cutover
- 6 months Discovery to production cutover
- 3 Migration cycles
- 6 Workforce data domains
- $1B+ Customer annual revenue
- CRM Customer & sales data migrated
- Automated Source-to-target mapping
- SAP R/3 Legacy source platform
- Oracle Fusion Target cloud platform
- Multi-cycle Controlled migration cycles
Who Owns What
- DataMove extracts an employee record.
- DataVault retains the original source record and extraction metadata.
- DataMove applies the approved transformation rule.
- DataVault records the original value, rule version and transformed value.
- DataMove generates and submits the target output.
- DataVault records the generated output, load response and resulting target value.
- DataLens presents reconciliation status, trends and risks.
Five Governed Data States
| Data State | What DataVault Retains | Purpose |
|---|---|---|
| Source state | Original extracted values, source keys and extraction metadata | Prove what was received |
| Prepared state | Standardized and cleansed values | Show preparation before transformation |
| Transformed state | Derived values and rule references | Explain how target values were created |
| Output state | HDL, FBDI, API payloads, files and manifests | Prove what was submitted |
| Target state | Load responses, target identifiers and post-load values | Confirm what reached the destination |
Where configured, DataVault also retains excluded, rejected and corrected records, manual overrides, exception notes, approval status, attachment references, migration-cycle identifiers, and country, legal-entity and business-unit context.
Core Capabilities
Governed Source Snapshots
Full and incremental snapshots, source-system identifiers, extraction timestamps, migration-cycle labels, source object/table and batch references, structured and unstructured data, attachment associations, and snapshot comparison. Source data stays distinguishable from corrected or transformed data.
Transformation Evidence
Original field value, prepared value, transformed value, the transformation rule and its version, lookup or cross-reference used, execution timestamp, override reason, approval reference, and record consolidation or splitting references.
Source-to-Target Lineage
Field-level, record-level, file-level and attachment lineage, cross-system identifier mapping, migration-cycle lineage, and exportable lineage evidence.
See a lineage example →Automated Reconciliation
Source-to-staging, staging-to-output and output-to-target comparison across record counts, field values, control totals, financial values, payroll balances and attachment counts, with tolerance management, exception classification and sign-off.
See reconciliation levels →Exception & Correction History
Failed-record capture, error classification, original target response, correction history, selective-reprocessing reference, resolution owner and timestamp, approval evidence, and the relationship between original and corrected submissions. DataMove performs the reprocessing; DataVault preserves the evidence.
Data Masking & Privacy Controls
Field-level and format-preserving masking, deterministic masking, tokenization, pseudonymization, synthetic values, date shifting, country-specific masking policies, consistent masking across related records, sensitive-field classification and masking audit history — configured per project.
Document & Attachment Governance
Source attachment metadata, parent-transaction association, filename and file-type information, original and target filenames, valid/deleted/replaced status, migration status, target document identifier, attachment-count and file-size reconciliation, and missing-or-corrupt-file exceptions.
Retention & Archive Handoff
Dataset classification, retention category, country/entity policy, legal-hold flag, archive destination, approved purge status, export history, access history, and archive handoff reconciliation into SyntraETL's archival solution.
Source-to-Target Reconciliation
| Reconciliation Level | Example |
|---|---|
| Record count | Employees extracted versus employees loaded |
| Object count | Assignments, salaries, invoices, suppliers or attachments |
| Field value | Source employee ID versus derived target person number |
| Financial total | Invoice, balance, earning or deduction totals |
| Payroll balance | Source statutory/YTD balance versus target initialization |
| Attachment | Expected files versus successfully migrated files |
| Status | Loaded, rejected, excluded, archived or pending |
| Employee/transaction drill-down | Complete migration outcome for one business entity |
Totals can be filtered by source system, migration cycle, country, legal entity, business unit, module, object, load batch, status and exception type.
A Lineage Example
Illustrative example with fictional sample data — not a real employee record.
SuccessFactors Employee ID: SG-004582
↓
Country/legal-entity transformation rule
↓
Oracle Person Number: 1004582
↓
HDL file: Worker_SG_Cycle03_001.dat
↓
Oracle load response: Accepted
↓
Post-load Oracle value: 1004582- Source object
- Employee Master
- Source field
- employeeId
- Raw value
- SG-004582
- Transformation rule
- SG-LegalEntity-Map
- Rule version
- v3
- Transformed value
- 1004582
- Output file
- Worker_SG_Cycle03_001.dat
- Load batch
- Cycle03-Batch001
- Target value
- 1004582
- Reconciliation status
- Matched
Compare Every Migration Cycle
Enterprise migrations run through repeatable cycles. DataVault retains dataset version, mapping version, rule version, record counts, exclusions, exceptions, target outcomes and sign-off status for each one.
Comparing cycles helps teams see which records were added or changed, which errors were resolved, which new errors appeared, whether reconciliation improved, and which rule changes affected outputs. DataVault retains the evidence — DataMove executes the cycle itself.
DataVault Use Cases
Oracle Fusion Migration Reconciliation
Trace source values through transformations, HDL/FBDI generation and Oracle target validation.
HCM and Payroll Migration
Track effective-dated workers, assignments, identifiers, balances, payslips and statutory records.
ERP and Financial Migration
Reconcile suppliers, invoices, purchase orders, assets, journals and financial totals.
Multi-Source Consolidation
Preserve the contribution of each source system when records are consolidated into one target.
Attachment Migration
Maintain document-to-transaction association and reconcile expected versus migrated content.
Legacy-System Retirement
Transfer reconciled historical data into a searchable archive while retaining decommissioning evidence.
DataVault Governance and Historical Archival
DataVault governs data throughout migration and preserves the evidence connecting source, transformation and target states. SyntraETL's archival solution provides long-term business access to historical records after a legacy application is retired. The two work together, but they address different stages and user needs.
| Capability | DataVault | Legacy Archive |
|---|---|---|
| Migration source snapshots | Primary | Optional reference |
| Transformation lineage | Primary | Summary or retained evidence |
| Target reconciliation | Primary | Final evidence |
| Migration exception history | Primary | Not the primary function |
| Long-term historical business access | Supporting | Primary |
| Employee/transaction search portal | Supporting | Primary |
| Retention-based historical access | Supporting | Primary |
| System decommissioning | Provides evidence | Delivers the business solution |
Move Data with DataMove. Trust It with DataVault. Understand It with DataLens.
DataMove
Move and transform data
- Extraction
- Transformation
- Target generation
- Loading
- Synchronization
- Reprocessing
DataVault
Trust and govern data
- Snapshots
- Lineage
- Reconciliation
- Masking
- Evidence
- Retention controls
DataLens
Analyze and understand data
- Data profiling
- Quality analytics
- Progress dashboards
- Exception trends
- Risk indicators
- Readiness insights
Security and Governance
DataVault supports customer-defined compliance, retention and audit requirements.
Frequently Asked Questions
What is DataVault?+
DataVault is the governed data foundation used by SyntraETL to retain and connect data from the source system, transformation process, generated migration output and target application. It gives project teams a traceable explanation of where each target value originated, how it changed, and whether it was successfully accepted.
How is DataVault different from SyntraETL?+
SyntraETL is the complete migration and integration solution. DataVault is the platform component within SyntraETL responsible for preserving, governing, tracing, masking and reconciling data — it does not perform extraction or loading itself, which is handled by DataMove.
How does DataVault support source-to-target reconciliation?+
DataVault compares data at each stage of the migration — source to staging, staging to output, and output to target — across record counts, field values, financial totals and attachment counts, so discrepancies can be identified, classified and resolved before or after go-live.
Can DataVault trace a target value back to its original source?+
Yes. DataVault is designed to connect a target value back through the generated output, the transformation rule that produced it, and the originating source record, so project teams can answer where a value came from and how it changed.
Does DataVault retain transformation-rule history?+
Yes. DataVault retains the original field value, the prepared value, the transformed value, and a reference to the rule (including its version) that produced the transformation, along with any override reason and approval reference recorded during the migration.
Can DataVault reconcile attachments and unstructured content?+
Yes. DataVault can track attachment metadata, parent-transaction association, and migration status, and supports attachment-count and file-size reconciliation between source and target where this is configured for the project.
Does DataVault support data masking?+
DataVault supports field-level masking approaches — including format-preserving masking, tokenization, pseudonymization and date shifting — that can be configured for sensitive fields identified during a migration or integration project.
How does DataVault work with legacy-system archival?+
DataVault governs data during migration and can hand off reconciled, approved historical datasets to SyntraETL's archival solution once a legacy application is retired. DataVault focuses on migration-stage evidence; the archive solution provides long-term business access to historical records.
Is DataVault a master data management platform?+
DataVault is a governed data foundation for migration lineage, reconciliation, masking, snapshots and historical evidence — not a full master data management (MDM) platform. Capabilities such as entity matching, golden-record publication and steward workflows are not part of DataVault today.
Can failed and corrected records be tracked across migration cycles?+
Yes. DataVault preserves exception and correction history — the original target response, the correction applied, who approved it and when, and the relationship between the original and corrected submission — across mock runs, UAT, dress rehearsal and production cycles.
Make Every Migrated Record Traceable
See how DataVault connects source values, transformation rules, generated outputs and target results in one governed migration evidence chain.