Data Discovery & Legacy Assessment
We catalogue all data objects in your legacy systems, understand volumes, quality issues, and business ownership. This forms the foundation of the migration scope and strategy document.
Data migration is one of the highest-risk activities in any SAP transformation. Get it right and your go-live is clean. Get it wrong and the fallout extends for months. Aevitas IT brings the tools, governance, and experience to migrate your critical business data with confidence.
Every SAP transformation — whether greenfield, brownfield, or selective data migration — requires a robust data migration workstream. Aevitas IT manages the complete migration lifecycle: legacy data analysis, data cleansing and harmonisation, mapping, migration execution, reconciliation, and sign-off.
We work with SAP’s primary migration toolset — SAP Business Object Data Services (BODS), SAP LTMC (Legacy Transfer Migration Cockpit), and LSMW — as well as custom ABAP-based migration programmes for complex or non-standard objects. Our data migration planning begins in the Prepare phase, ensuring sufficient lead time for data quality activities that often determine project success or failure.
STEP 2
Extract &
Profile
STEP 3
Cleanse &
Map
STEP 4
Mock Runs
& Validate
STEP 5
SAP
S/4HANA
We catalogue all data objects in your legacy systems, understand volumes, quality issues, and business ownership. This forms the foundation of the migration scope and strategy document.
Legacy data is rarely clean. We work with business owners to identify and resolve duplicates, gaps, outdated records, and inconsistencies — improving data quality before it reaches the new system, not after.
Each data object is mapped from its legacy structure to the SAP target structure. Transformation rules handle data conversion, code harmonisation, and field population logic — documented in a migration mapping specification.
Multiple migration dress rehearsals are executed in development and QA environments. Each run is reconciled against source data, and issues are resolved iteratively — so the production migration is a rehearsed event, not an experiment.
The final migration is executed during the cutover window with real-time monitoring. Post-migration reconciliation confirms completeness and accuracy before business sign-off is obtained and the system goes live.


Data quality is the foundation of effective AI in SAP. Joule and SAP’s embedded AI models rely on accurate, consistent master and transactional data to deliver reliable insights and automation. Aevitas IT uses AI-assisted data profiling tools to identify quality issues faster, suggest transformation rules, and detect anomalies in migration output — improving both migration quality and your long-term AI readiness.
We are proficient across SAP’s full migration toolset — LTMC, BODS, LSMW, and custom development — selecting the right tool for each object type and migration complexity.
Data migration is not just a technical activity. We engage business owners throughout — ensuring data quality decisions are made by people who understand the business impact of getting it wrong.
We start data migration planning in the Prepare phase — months before the migration runs begin. This gives sufficient time to address quality issues that a rushed approach would carry forward into production.
Whether extracting data from non-SAP legacy systems (greenfield) or using SAP’s native migration options for ECC-to-S/4HANA conversions (brownfield), we have the experience for both.
Not everything in a legacy system deserves a seat in the new one. We work with business owners to classify data as migrate, archive, or discard, moving clean master data and open transactions, while historical records that only need to be retrievable (not actively processed) are often better handled through an archive or reporting layer instead.
LTMC (SAP's Migration Cockpit) is well suited to master data and standard object migration in S/4HANA-native scenarios. SAP Data Services (BODS) handles more complex transformations, larger volumes, and non-standard source systems. Many projects use both, matched to the object type and complexity involved.
Through iterative mock migration runs rather than a single validation pass at the end, each cycle surfaces data quality issues early enough to fix at the source, with AI-assisted validation tooling helping flag anomalies across large data volumes faster than manual spot-checks alone.