Business outcome
Use this capability when the framework shows that people cannot trust the records, measures, or exceptions needed to make decisions across demand, supply, production, inventory, fulfillment, delivery, or service.
Services
Data & Analytics · Legacy Modernization · Data Migration
Data Migration & Validation
Move manufacturing history without losing control of what the records mean.
Customer, product, supplier, BOM, routing, inventory, quality, pricing, and transaction history require more than row counts. We cleanse, transform, reconcile, and validate the records against defined business rules and accountable owners.
A strong fit when
- Legacy data formats don't match modern system structures -- field mappings are ambiguous and lossy
- Decades of data quality issues surface during migration -- duplicates, orphans, invalid formats, missing fields
- Historical data that seems unimportant turns out to be critical for compliance, reporting, or customer service
- Sample-based testing passes while full-volume migration reveals unmapped exceptions, relationships, or operating rules
Why this service exists
Connect the technology decision to the work the manufacturing business must control.
System responsibility
Data engineering turns records from ERP and connected systems into governed information for shared visibility, reporting, reconciliation, automation, and accountable action.
Ownership and control
The manufacturer must own the definitions, source authority, access, lineage, quality rules, reconciliations, and decision context behind every important measure.
01
The Problem
Data can migrate successfully at a technical level and still fail the manufacturing operation.
Analytics problems begin when a dashboard is built before records, definitions, timing, lineage, and accountability have been reconciled across the operation.
What leaders see
Reports disagree about current conditions.
Teams debate demand, workload, capacity, inventory, service, delivery, and margin instead of acting on them.
What is actually happening
Measures inherit unresolved record conflicts.
Sources, identifiers, definitions, refresh timing, adjustments, and ownership differ across systems and teams.
What gets worse
Faster reporting accelerates the wrong answer.
More dashboards spread inconsistent measures and make exception ownership harder to establish.
02
What changes
Make the operating responsibility visible and governable.
Customer, product, supplier, BOM, routing, inventory, quality, pricing, and transaction history require more than row counts. We cleanse, transform, reconcile, and validate the records.
01
Operating outcome
Move manufacturing history without losing control of what the records mean.
02
Operating records to reconcile
demand and orders, products, services, and inventory, work and capacity
03
Decision and exception path
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
04
Ownership and continuity
Measures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.
03
Architecture
Build the service around the business record and decision.
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
01Source record
02Governed connection
03Validation
04Business system
05Accountable owner
Operating records to reconcile
demand and ordersproducts, services, and inventorywork and capacityquality and customer servicedelivery and financial results
04
Engineering scope
What Metrotechs engineers for Data Migration & Validation.
The exact scope follows the approved business objective, source records, dependencies, controls, and delivery sequence.
01
Data Profiling
Profile the tables, fields, relationships, volumes, and formats in scope. Identify quality issues and transformation requirements before migration begins.
02
Mapping & Transformation
Define field-by-field mappings between legacy and target systems. Handle format conversions, code translations, concatenations, and business rule transformations.
03
Data Cleansing
Fix quality issues during migration -- standardize addresses, deduplicate customers, resolve orphaned records, and fill missing required fields with governed defaults.
04
Validation Framework
Use record counts, checksums, relationship tests, exception reports, and business-rule validation to reconcile source and destination before acceptance.
05
Incremental Migration
Migrate historical data in advance, then incrementally sync changes during the transition period. Minimize cutover window and reduce go-live risk.
06
Rollback Capability
Define fallback conditions and recovery procedures for each migration stage. When validation fails, stop progression, resolve the cause, and repeat the affected step from a controlled state.
05
Delivery sequence
From operating reality to a solution the business can own.
Data Profiling
Analyze source data volumes, quality, formats, and relationships. Produce a data quality report with issues ranked by severity and migration impact.
Mapping Design
Design field-by-field mappings with transformation rules. Review with business stakeholders to validate that mappings preserve business meaning.
ETL Development
Build extraction, transformation, and loading pipelines. Automated, repeatable, and version-controlled -- not manual copy-paste.
Test Migration
Run full-volume test migrations against a non-production target. Validate record counts, data accuracy, and business rule compliance.
Production Migration
Execute the production migration with active monitoring, validation checkpoints, and rollback readiness. Verify the agreed records and business scenarios before declaring success.
Related services and systems
Continue through the connected operating environment.
Use these connected services and references to understand the records, workflows, and systems surrounding this work.
06
FAQ
Questions to answer before implementation begins.
Clear answers for manufacturing leaders evaluating the work, operating responsibility, and delivery path.
We categorize issues by severity: critical (blocks migration), major (requires business decision), and minor (automated fix). Critical and major issues are resolved before migration. Minor issues are fixed in-flight with documented rules.