SAP Migration Accelerators
SAP migrations rarely slip because a team doesn't understand SAP. They slip on scope creep, unexpected custom code work, integrations that break in testing, messy data, and a cutover plan written too late. Our accelerators exist to close exactly those gaps before they cost you a go-live date.
An industrialised, repeatable approach — not a one-off script every time
Data quality is the most common reason migrations slip. A factory approach treats data preparation as a tracked workstream with its own KPIs, not a parallel activity nobody owns.
ECC extract profiling
Source data profiled in a staging layer to surface duplicates, missing fields and inconsistencies before they reach a load.
Master data cleansing
Customer, vendor and material master data corrected before it's ever loaded, not discovered as an error mid-migration.
Reusable mapping templates
Migration object mappings built once and reused across waves, instead of rebuilt for every entity or rollout phase.
Delta migration via SLT
Ongoing changes captured incrementally, minimising the final data transfer window and allowing fast delta reloads if needed.
Data quality as a weekly KPI
Data readiness tracked on a cadence with named owners per domain, not left until the week before cutover.
Audit-documented corrections
Every data correction logged and documented, so the migration trail holds up under later scrutiny.
SAP's standard tool, run by people who know where loads actually fail
The Migration Cockpit handles the transfer and technical validation of migration objects — but most load failures trace back to data that wasn't prepared before the load was attempted.
Migration object configuration
Standard and custom migration objects configured against your actual source structures.
Staged, validated loads
Data loaded in controlled batches with validation at each stage, not a single all-or-nothing run.
Balance & reconciliation checks
Migrated balances checked against source trial balances before cutover is confirmed, account by account.
Mock migration cycles
Multiple dry runs to detect and resolve load issues well before the live cutover window.
Knowing what breaks before the simplification items do
S/4HANA's simplified data model changes some of the classic tables and patterns custom reports and extracts rely on — assessed early, not discovered during integration testing.
ATC readiness scanning
Custom objects scanned against simplification items and deprecated table references.
Effort & risk classification
Each finding scored by remediation effort, so the backlog can be prioritised realistically.
Retire-or-remediate decisions
Unused or obsolete custom objects flagged for retirement instead of carried forward by default.
Interface catalogue, built early
Every integration documented and tested end to end with real data, before it has a chance to break in production.
Realistic scenarios, run repeatedly — not a single manual pass before go-live
Multiple test cycles, automated where possible, are what actually catch problems before they reach a live cutover window.
Automated regression suites
Core business processes retested automatically against every build, not just before go-live.
Parallel environment testing
Testing conducted in a non-production environment that closely mirrors production, before anything touches live.
Realistic UAT scenarios
Test scripts built on real business scenarios, with the business actually participating — not just IT signing off alone.
Data migration validation testing
A structured test plan verifying migrated data is correct, complete and usable, separate from process testing.
Interface & integration testing
Every integration tested end to end with real data, not assumed to work because it did in the old system.
Defect tracking & retest cycles
Issues tracked to closure with a documented retest, not marked resolved on a verbal confirmation.
A migration is IT-led, but finance and the business own the data and the outcome
Collaboration across functional units isn't a nice-to-have on a migration — it's what determines whether the new system gets adopted or quietly worked around.
Stakeholder alignment
Business and IT expectations aligned early, since migration outcomes depend on more than the technical build.
Named data owners
Each data domain has a named business owner accountable for its quality, not a shared, diffused responsibility.
Structured communication plan
Changes that affect day-to-day work communicated to users before they encounter them live, not after.
Early-start user training
Training begins as soon as possible — before or immediately upon migration — not as an afterthought at go-live.
Where "perfect plans" meet reality — rehearsed enough times that reality cooperates
A clear, well-tested cutover procedure with defined lines of accountability is what separates a controlled go-live from a chaotic one.
Detailed cutover runbook
Every step sequenced with an owner and a time window, drafted during planning rather than written at the last minute.
Multiple rehearsals
The cutover sequence practised more than once, with results reviewed between each run, not executed live for the first time.
Go/no-go criteria
Objective thresholds defined in advance for whether cutover proceeds, instead of a judgement call under pressure.
Parallel run validation
Old and new systems run in parallel where appropriate, confirming results match before fully decommissioning the legacy system.
Hypercare standby
The project team on standby through the first transactions, not handed off the moment the system goes live.
Rollback contingency
A documented fallback path, decided in advance rather than improvised if something goes wrong mid-cutover.
Common pitfalls, and the fix that actually addresses each one
Every programme is different, but the same handful of problems show up again and again. Knowing them in advance is most of the battle.
Worried your migration will hit one of these exact problems?
A free readiness assessment checks your data, custom code and cutover plan against the pitfalls that actually derail projects.