data-migration

name	data-migration
description	Use when planning, designing, or executing SAP data migration. Triggers on requests involving data migration strategy, data mapping, LTMC, BODS, legacy data conversion, reconciliation, trial migration, or cutover data load.

SAP Data Migration

This skill enforces structured, evidence-based data migration — preventing the three most common catastrophic failures: migrating without reconciliation, skipping data profiling, and assuming source data is clean.

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Iron Laws

1. NEVER MIGRATE WITHOUT RECONCILIATION. Every migrated object must be counted and reconciled: records sent = records loaded = records validated. A migration without reconciliation is a migration with unknown quality.
2. NEVER SKIP DATA PROFILING. You cannot map what you haven't profiled. Data profiling is not optional prep — it is the foundation everything else is built on.
3. NEVER ASSUME SOURCE DATA IS CLEAN. Source data is always dirty. The question is how dirty. Data profiling quantifies the dirt. Remediation cleans it. Assuming it's clean skips both steps.
4. ALWAYS RUN AT LEAST ONE TRIAL MIGRATION. A trial migration in a non-production system is non-negotiable. The first full load CANNOT be the production cutover load.
5. TRANSFORMATION RULES ARE DOCUMENTED BEFORE LOAD, NOT AFTER. If a transformation rule is not in the mapping document before loading, it does not exist. Post-hoc documentation of "what we actually did" is not a migration document.

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Rationalization Table

Agent Will Try To...	Why It Seems Reasonable	Why It Fails	Counter
Skip data profiling to save time	"We know the source system well"	Knowing the system ≠ knowing its data quality. Legacy data accumulated over years has integrity issues the source team doesn't know about.	Iron Law 2: Profiling is non-negotiable. At minimum: record counts, null rates, format violations, referential integrity checks.
Declare migration complete without reconciliation	"Everything loaded without errors"	Error-free load ≠ complete load. Records can be silently skipped, truncated, or misrouted. Only counts prove completeness.	Iron Law 1: Reconciliation report required. Records out = records in, or explain every delta.
Use big bang strategy without considering phased option	"It's simpler to do everything at once"	Big bang increases cutover window, risk of failure, and rollback complexity. Phased migration is often safer. Justify the choice with evidence.	Checklist Step 1: Strategy decision documented with justification and risk assessment.
Assume vendor/partner master data is correct	"The client maintains this data themselves"	Client-maintained data has format issues, duplicates, and gaps. Profile it the same way as any other source.	Iron Law 3: Profile all sources without exception.
Skip trial migration because "dev loads worked"	"We've done partial loads in dev"	Partial dev loads ≠ full trial migration. Trial migration tests volume, performance, error handling, and reconciliation in an integrated environment.	Iron Law 4: Full trial migration mandatory. Dev loads do not satisfy this requirement.
Defer cleansing to post-migration	"We'll clean it up once it's in SAP"	SAP has strict validation rules. Dirty data that loads creates more remediation effort in SAP than in the source. Clean before migration.	Checklist Step 3: Cleansing rules and completion criteria defined and executed before load.

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Red Flags

Watch for these phrases in your own reasoning — each signals an Iron Law violation:

• "The source data looks pretty good..." → You haven't profiled it. Stop.
• "We can reconcile after go-live..." → Post-go-live reconciliation of migrated data is triage, not validation. Stop.
• "The load ran without errors, so it's complete..." → Error-free ≠ complete. Run reconciliation. Stop.
• "We did this for the dev load, we don't need a trial migration..." → Dev load ≠ trial migration. Stop.
• "The transformation is simple enough to document later..." → Iron Law 5. Document first. Stop.
• "The client assured us the data is clean..." → Client assurance is not a data profile. Stop.
• "We'll do big bang, it's just easier..." → Justify the risk with evidence. Stop.

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Trial migration CANNOT be skipped. Production cutover data load cannot proceed until: 1. Data profiling is complete and results documented for ALL objects in scope 2. Data cleansing rules are defined, executed, and sign-off obtained from the business 3. Full field-level mapping document is approved — source field → transformation rule → target SAP field 4. At least one full trial migration has been executed in a non-production environment 5. Trial migration reconciliation report confirms record counts match within defined tolerance 6. Data migration test sign-off obtained from business data owners If any condition is unmet, the production load is NOT authorized. Do not proceed.

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Checklist

1. Define migration strategy — Choose and justify big bang vs. phased migration. Document scope: which objects, which legacy systems, cutover timeline.

   • Evidence: Migration strategy document with approach, justification, object scope, and phasing plan
   • Gate: Strategy reviewed and approved by project manager and client data owner

2. Execute data profiling — For every migration object, profile the source data: record counts, null rates, format compliance, duplicates, referential integrity, business rule violations.

   • Evidence: Data profiling report per object with quantified quality metrics
   • Gate: Profiling complete for 100% of in-scope objects; results reviewed by business owner

3. Define cleansing rules and execute cleansing — Based on profiling results, define transformation and cleansing rules. Execute cleansing in source or staging area. Measure improvement.

   • Evidence: Cleansing rules document; before/after data quality metrics; business sign-off on cleansed data
   • Gate: Cleansed data meets defined quality thresholds; business owner signs off

4. Build field-level mapping document — For every source field in scope, document: source system, field name, transformation logic, target SAP object, target field, mandatory/optional, validation rule.

   • Evidence: Data mapping document approved by functional consultants and business
   • Gate: Mapping reviewed by SAP functional lead; no unmapped mandatory SAP fields

5. Configure migration tooling — Set up LTMC, BODS, or custom migration program. Validate tool configuration matches the mapping document.

   • Evidence: Migration program / LTMC project configured; test load of sample records confirms correct field mapping
   • Gate: Sample load of 10–50 records validates all transformations correctly

6. Execute trial migration — Run full migration load for all objects in a non-production environment. Do not skip objects or volumes.

   • Evidence: Trial migration execution log; record counts per object (source vs. loaded)
   • Gate: Load completes without critical errors; record counts within tolerance

7. Reconcile trial migration — Compare source record counts to loaded SAP counts. Investigate all discrepancies. Document resolution for each delta.

   • Evidence: Reconciliation report: source count, load count, delta, resolution per object
   • Gate: All deltas explained and resolved or formally accepted; zero unexplained discrepancies

8. Obtain data migration sign-off — Present trial migration results and reconciliation to business data owners. Obtain written approval to proceed to production load.

   • Evidence: Signed data migration sign-off document
   • Gate: Sign-off obtained before production cutover window opens

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Deliverable Templates

Data Migration Plan

PROJECT: [Name]
DATE: [Date]
VERSION: [Version]

MIGRATION STRATEGY
------------------
Approach:            [Big Bang / Phased — with justification]
Legacy systems:      [List source systems]
Cutover window:      [Date range, duration]
Rollback trigger:    [Conditions that trigger rollback]

OBJECTS IN SCOPE
----------------
| Object          | Source System | Tool  | Volume (records) | Priority | Owner     |
|-----------------|--------------|-------|-----------------|----------|-----------|
| Vendor Master   | Legacy ERP   | LTMC  | ~12,000         | P1       | [Name]    |
| Customer Master | Legacy CRM   | BODS  | ~8,500          | P1       | [Name]    |
| Open Items (AP) | Legacy ERP   | LTMC  | ~45,000         | P1       | [Name]    |
| Material Master | Legacy ERP   | LTMC  | ~30,000         | P1       | [Name]    |

MIGRATION TIMELINE
------------------
Data profiling:      [Start] → [End]
Data cleansing:      [Start] → [End]
Mapping:             [Start] → [End]
Trial migration 1:   [Date]
Trial migration 2:   [Date]
Production load:     [Date] [Time window]

RECONCILIATION TOLERANCE
------------------------
Standard objects:    0% variance (exact count match required)
Complex objects:     Up to [X]% variance with documented justification

Data Mapping Document (per object)

OBJECT: [e.g., Vendor Master]
SOURCE SYSTEM: [e.g., Oracle EBS]
TARGET: SAP LFM1 / LFA1
MAPPING VERSION: [Version]

| Source Field     | Source Type | Transformation Rule                    | Target Table | Target Field | Mandatory | Validation          |
|-----------------|------------|---------------------------------------|-------------|-------------|-----------|---------------------|
| VENDOR_CODE     | VARCHAR(10) | Prefix with 'V', zero-pad to 10 chars | LFA1        | LIFNR       | YES       | Unique in LFA1      |
| VENDOR_NAME     | VARCHAR(80) | Trim whitespace, UPPER case            | LFA1        | NAME1       | YES       | Max 35 chars        |
| COUNTRY_CODE    | VARCHAR(2)  | Map legacy to ISO 3166-1 alpha-2       | LFA1        | LAND1       | YES       | Valid T005 entry    |
| PAYMENT_TERMS   | VARCHAR(10) | Map to SAP ZTERM via lookup table      | LFB1        | ZTERM       | YES       | Valid T052 entry    |

Reconciliation Report

TRIAL MIGRATION: [Trial # and Date]
ENVIRONMENT:     [System ID]

| Object          | Source Count | Loaded Count | Delta | Delta % | Status      | Resolution             |
|-----------------|-------------|-------------|-------|---------|-------------|------------------------|
| Vendor Master   | 12,043      | 12,043      | 0     | 0.0%    | PASS        | —                      |
| Customer Master | 8,521       | 8,517       | 4     | 0.05%   | INVESTIGATE | 4 records missing LAND1|
| Open Items (AP) | 45,230      | 45,230      | 0     | 0.0%    | PASS        | —                      |

OVERALL STATUS: [PASS / FAIL / CONDITIONAL PASS]
SIGN-OFF REQUIRED FROM: [Data owner names]

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Verification

This skill is complete ONLY when ALL of the following are true:

• Migration strategy documented and approved (big bang vs. phased with justification)
• Data profiling complete for 100% of in-scope migration objects
• Cleansing rules defined and executed; business sign-off on cleansed data obtained
• Full field-level mapping document approved by functional consultants
• Migration tooling configured and sample load validated
• At least one full trial migration executed in a non-production environment
• Reconciliation report confirms zero unexplained discrepancies
• Data migration sign-off obtained from business data owners in writing

Evidence required: Data profiling report, cleansing sign-off, mapping document, trial migration execution log, reconciliation report, signed data migration sign-off.

If any item is unchecked, this skill is NOT complete. Do not authorize the production data load.

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Next Skill

After completing this skill, invoke: cutover-planning Conditions for handoff: Data migration trial complete and signed off. Cutover planning must sequence the production data load as a critical path task within the cutover runbook.

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Cross-References

• testing-strategy — Data migration testing (trial run, reconciliation) is part of the overall test strategy
• cutover-planning — Production data load is a key step in the cutover runbook
• go-live-readiness — Data migration sign-off is a required input to the go/no-go matrix
• sap-data-analyst agent — Use for automated data profiling and anomaly detection