Field-Level Integration Specs
— Generated, Not Hand-Written
Launch Layer ingests source system extracts, target architecture, and process context to produce field-level mapping documents, transformation rules, validation logic, and data quality analysis — in hours instead of months.
Manual Mapping Takes Months
Integration and data migration specs are the most labor-intensive artifacts in any ERP program. Every field, every transformation rule, every validation check is documented by hand — across dozens of interfaces and thousands of data elements.
Spreadsheet Hell
Source-to-target field mappings live in sprawling spreadsheets maintained by multiple teams. Version conflicts, missing entries, and stale data accumulate from day one. By the time UAT begins, the mapping doc is already outdated.
Undocumented Transformation Logic
Data type conversions, concatenation rules, default value logic, and conditional mappings live in the heads of senior analysts. When they move on, the knowledge moves with them — and downstream bugs multiply.
Format Conflicts Found at Go-Live
Date formats, currency precision, character encoding, and field-length mismatches are discovered during integration testing — or worse, in production. Each conflict requires root-cause analysis back through the entire mapping chain.
Data Quality Blind Spots
Source data issues — nulls, duplicates, orphaned records, invalid enumerations — are only surfaced when migration loads fail. By then, the remediation timeline threatens the entire program schedule.
Integration Specs From Context
Source-to-Target Field Mappings
Complete field-level mapping documents with source system, source field, target object, target field, data type, length, and cardinality — generated from ingested source extracts and target schema definitions.
Transformation Rules
Data type conversions, concatenation logic, default values, conditional mappings, and lookup table references — documented as executable specifications with traceability to source requirements.
Validation Rules
Required-field checks, referential integrity constraints, value-range validations, and cross-field dependencies expressed as testable rules that feed directly into integration test scripts.
Format Mismatch Flags
Automatic detection of date format conflicts (DD/MM vs MM/DD), currency precision differences, character encoding issues, field-length overflows, and enum value mismatches across source-target pairs.
Functional Specification Documents
Structured FSDs for each interface with system context, field-level logic, error handling, retry behavior, and integration architecture — ready for developer handoff.
Data Quality Profiles
Completeness scores, null-rate analysis, duplicate detection, outlier identification, and distribution analysis for every source field — surfaced before migration, not after.
Interface Dependency Maps
Visual and tabular dependency chains showing which interfaces feed which systems, enabling sequencing of migration waves and impact analysis of upstream changes.
Change Propagation
When a source field changes, every downstream mapping, transformation rule, validation check, and test script updates automatically — eliminating manual rework across integration artifacts.
Data Quality Issues Found Upfront
Launch Layer profiles source data at ingestion and flags issues that would otherwise surface months later during integration testing or post-go-live support.
Date & Format Conflicts
Detects mixed date formats within a single source column, timezone inconsistencies, locale-specific formatting, and calendar system conflicts before any transformation logic is written.
- DD/MM/YYYY vs MM/DD/YYYY detection
- Timezone offset analysis
- Currency precision mismatches
- Character encoding conflicts
Missing & Incomplete Fields
Identifies required target fields with no source mapping, source fields with high null rates, partially populated records, and orphaned reference data that would fail validation on load.
- Null-rate heatmaps per field
- Required-field gap analysis
- Orphaned foreign key detection
- Completeness scoring per entity
Duplicate & Anomaly Detection
Surfaces duplicate records, near-duplicates with fuzzy matching, statistical outliers, and value distribution anomalies that indicate data quality issues requiring remediation before migration.
- Exact and fuzzy duplicate detection
- Statistical outlier identification
- Enum value validation
- Cross-system record matching
Traditional vs. Launch Layer
See how Launch Layer generates
your integration specs from source data
Request a walkthrough to see field-level mapping documents generated from your program's source systems and target architecture.