DataLens
Enterprise Migration Intelligence with DataLens
Turn migration data into actionable insight. DataLens brings data quality, progress, reconciliation, exception and readiness analytics together so project teams can make faster, evidence-based decisions.
Illustrative sample data
What is DataLens?
DataLens is the analytics and intelligence layer within the Syntra platform. It analyses information generated by DataMove and governed by DataVault to help migration teams understand data quality, migration progress, reconciliation outcomes, exceptions, operational performance and cutover risk. It's built for actionable migration intelligence, not generic visualization.
DataLens serves:
Project experience
DataLens 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
Core Capabilities
Source-Data Profiling
Record counts, field population, null analysis, distinct-value and frequency distribution, pattern detection, date-range analysis, invalid-format identification, duplicate and orphan-record indicators, outliers, object complexity and volume — filterable by source, country, entity, module, object, field, status and period.
Data-Quality Analytics
Quality score by object across completeness, validity, consistency, uniqueness, referential integrity and target conformity, with rule-failure trends, severity, owner, remediation status and cycle-over-cycle improvement.
See the scoring model →Migration Progress Dashboard
Records extracted, prepared, transformed, validated, generated, submitted, accepted, rejected, reconciled, excluded, archived and pending — with drill-down by cycle, source, target, module, object, country, entity, batch and status.
Reconciliation Analytics
Source-versus-target counts, missing and extra records, field mismatches, financial and payroll-balance variances, attachment-count variances, tolerance, exception severity, sign-off status and trend across cycles. DataVault preserves the reconciliation evidence; DataLens visualizes and analyses it.
See a drill-down example →Exception Analytics
Exceptions by category, severity, source, target, module, country, object and owner, with ageing, recurring-error patterns, resolved-versus-unresolved and reprocessing outcomes. Related errors are grouped to accelerate root-cause investigation.
Migration-Cycle Comparison
Compare source volumes, quality results, rule versions, reconciliation results, error rates, unmapped values, processing duration and readiness indicators across prototype, mock, UAT, dress rehearsal, production and post-go-live remediation.
See a cycle comparison →Cutover Readiness
A configurable readiness assessment based on customer-approved thresholds and migration controls — extraction completeness, mandatory mapping approval, data-quality thresholds, critical-exception resolution, reconciliation acceptance, target dependencies, load performance, delta validation, business sign-off and runbook approval.
Operational Monitoring
Pipeline status and current stage, queue depth, extraction/transformation/load throughput, processing duration, API latency, failed jobs, retry status, data freshness, estimated completion and SLA status.
A Migration Programme, at a Glance
All values below are illustrative sample data, not results from a real customer programme.
Executive Migration Status
- Overall progress
- 78%
- Objects on track
- 21 / 26
- Objects at risk
- 5 / 26
- Critical exceptions
- 4
- Reconciliation complete
- 91%
- Readiness status
- Amber
Data-Quality Status
- Completeness
- 98%
- Validity
- 95%
- Uniqueness
- 99%
- Referential integrity
- 97%
- Target conformity
- 94%
Reconciliation Status
- Matched
- 91%
- Within tolerance
- 6%
- Mismatched
- 2%
- Missing in target
- 1%
- Extra in target
- 0%
- Awaiting sign-off
- 3 objects
Exception Status
- Critical
- 4
- High
- 11
- Medium
- 18
- Low
- 4
- Assigned / Unassigned
- 29 / 8
- Oldest open (ageing)
- 6 days
Cycle Comparison
- Mock 1
- Baseline
- Mock 2
- Improving
- Dress rehearsal
- Improving
- Production
- Pending
Operational Status
- Jobs running
- 3
- Jobs completed
- 142
- Jobs failed
- 2
- Records / hour
- 18,400
- Estimated completion
- 2h 15m
A Transparent Data-Quality Model
DataLens does not conceal quality behind one headline number. Users can drill from a quality score into the contributing rules, failed records and required corrective actions.
| Quality Dimension | Example Measurement |
|---|---|
| Completeness | Required fields populated |
| Validity | Values satisfy defined rules |
| Consistency | Related fields do not conflict |
| Uniqueness | Duplicate records identified |
| Integrity | Required parent records exist |
| Conformity | Values meet target format and code requirements |
Customers can configure rule weights, severity, threshold, scope, exclusions, owner and sign-off requirement.
Reconciliation Drill-Down
Sample data, illustrating the analysis pattern only.
| Stage | Expected | Processed | Matched | Exception | Status |
|---|---|---|---|---|---|
| Source extract | 25,000 | 25,000 | 25,000 | 0 | Complete |
| Transformation | 25,000 | 24,980 | 24,960 | 40 | Review |
| Output generation | 24,980 | 24,980 | 24,980 | 0 | Complete |
| Target load | 24,980 | 24,970 | 24,950 | 30 | Review |
| Post-load reconciliation | 24,980 | 24,950 | 24,930 | 50 | Action required |
From this summary, teams can drill down to object, record, field, exception, transformation, load batch, target response and resolution owner.
Compare Cycles, Not Just Snapshots
Illustrative trend, not real customer figures.
| Metric | Mock 1 | Mock 2 | Dress Rehearsal | Direction |
|---|---|---|---|---|
| Data-quality failures | Sample | Sample | Sample | Improving |
| Unmapped values | Sample | Sample | Sample | Improving |
| Target rejections | Sample | Sample | Sample | Improving |
| Reconciliation variance | Sample | Sample | Sample | Improving |
| Processing duration | Sample | Sample | Sample | Improving |
| Critical exceptions | Sample | Sample | Sample | Improving |
Reporting and Export
Data can be exposed to approved BI platforms — including Power BI, Oracle Analytics and Tableau — through configured interfaces.
DataLens Use Cases
Oracle Fusion Migration Intelligence
Track HDL/FBDI preparation, target submission, rejection and reconciliation across Oracle modules.
HCM and Payroll Migration Analytics
Analyse worker history, assignments, payroll balances, country-specific errors and reconciliation.
ERP and Financial Migration Analytics
Monitor suppliers, invoices, purchase orders, assets, journals and financial control totals.
Multi-Country Programme Governance
Compare progress, quality and risk by country, legal entity, payroll or deployment wave.
System-Integrator Portfolio View
Governed progress and exception visibility across customers, workstreams and migration cycles.
Archive Operational Analytics
Analyse archive volume, usage, retention eligibility and storage growth.
Migration Intelligence vs Historical Reporting
DataLens helps delivery teams understand an active migration or integration programme. SyntraETL Historical Reporting gives business, audit and compliance users access to archived transactions after a legacy system has been retired.
| Requirement | DataLens | Historical Reporting |
|---|---|---|
| Data profiling before migration | Yes | No |
| Migration progress | Yes | No |
| Data-quality trends | Yes | No |
| Reconciliation analytics | Yes | Supporting |
| Cutover readiness | Yes | No |
| Pipeline performance | Yes | No |
| Historical invoice and journal search | No | Yes |
| Prior-year payroll reports | No | Yes |
| Audit access after decommissioning | No | Yes |
| Long-term archived business reporting | No | Yes |
A View for Every Role
| User | Most Relevant View |
|---|---|
| Programme executive | Overall status, risk and readiness |
| Migration manager | Progress, exceptions and cycle comparison |
| Data lead | Profiling, quality rules and remediation |
| Functional lead | Object-level validation and business exceptions |
| Technical lead | Pipeline, throughput and target errors |
| Payroll lead | Balance, statutory and employee-level reconciliation |
| Auditor | Controls, sign-off and evidence status |
| System integrator | Cross-workstream delivery dashboard |
Move Data with DataMove. Trust It with DataVault. Understand It with DataLens.
DataLens analyses the operational and governed information produced by DataMove and DataVault. It does not replace either engine.
DataMove
Move and transform data
- Connect
- Extract
- Transform
- Generate
- Load
- Synchronize
DataVault
Trust and govern data
- Preserve
- Trace
- Reconcile
- Mask
- Retain
- Evidence
DataLens
Analyze and understand data
- Profile
- Measure
- Compare
- Monitor
- Explain
- Recommend
Security and Governance
Outcomes
Frequently Asked Questions
What is DataLens?+
DataLens is the analytics and intelligence layer within the Syntra platform. It analyses information generated by DataMove and governed by DataVault to help migration teams understand data quality, migration progress, reconciliation outcomes, exceptions, operational performance and cutover risk.
How is DataLens different from SyntraETL?+
SyntraETL is the complete migration and integration solution. DataLens is the platform component that turns the data DataMove processes and DataVault governs into dashboards, scorecards and trend analysis — it does not execute migrations or store the permanent evidence record itself.
How does DataLens work with DataMove and DataVault?+
DataMove extracts and transforms data, DataVault preserves the source values, transformation history and target outcomes as governed evidence, and DataLens analyses that information to show completeness, exceptions, reconciliation variance and migration readiness.
What data-quality metrics can DataLens analyse?+
DataLens can analyse completeness, validity, consistency, uniqueness, referential integrity and conformity to target rules, broken down by object, with drill-down to the rules evaluated, records passed and failed, and the records requiring correction — not a single unexplained score.
Can DataLens compare different migration cycles?+
Yes. DataLens can compare data-quality results, transformation-rule versions, reconciliation results, error rates and readiness indicators across cycles such as prototype, mock runs, UAT, dress rehearsal and production, so teams can see whether reconciliation is improving cycle over cycle.
Does DataLens provide source-to-target reconciliation analytics?+
Yes. DataVault preserves the reconciliation evidence, and DataLens visualizes and analyses it — source-versus-target counts, missing and extra records, field mismatches, financial and payroll-balance variances, and reconciliation trends across cycles.
How does DataLens measure cutover readiness?+
DataLens presents a configurable readiness assessment based on customer-approved thresholds and migration controls — for example extraction completeness, mapping approval, data-quality thresholds, critical-exception resolution and reconciliation acceptance — rather than a single universal readiness score.
Can dashboards be filtered by country, entity or module?+
Yes. DataLens views can be filtered by dimensions such as source system, migration cycle, country, legal entity, business unit, module, object and status, so different roles can drill into the slice of the programme relevant to them.
Can DataLens connect with Power BI or Oracle Analytics?+
Data can be exposed to approved BI platforms such as Power BI or Oracle Analytics through configured interfaces. Availability depends on the specific project's approved integration scope.
How is DataLens different from historical reporting?+
DataLens helps delivery teams understand an active migration or integration programme — quality, progress, reconciliation and readiness. SyntraETL's Historical Reporting solution gives business, audit and compliance users access to archived transactions after a legacy system has been retired. They serve different stages and different users.
Turn Migration Data into Actionable Insight
See how DataLens helps teams understand data quality, migration progress, reconciliation results, exceptions and cutover readiness in one controlled view.