SAP data you can actually trust.
Your SAP landscape generates more transactional data than any other system in the business — and most of it is raw, duplicated, and siloed across modules. We bridge that raw SAP data into clean, governed, decision-ready outputs your finance, supply chain, and analytics teams can rely on.
Decades of customization leave a trail
Every SAP rollout, add-on module, and acquisition leaves its own fingerprint on the data. The longer the system has run, the more those fingerprints pile up.
From SAP source tables to governed output
A four-stage pipeline purpose-built for SAP's data model — its tables, its module boundaries, and the way master data actually breaks.
SAP Data Extraction
Pull structured data directly from ECC, S/4HANA, and BW tables — IDocs, BAPIs, and direct table access — without disrupting live transaction processing.
Profiling & Assessment
Score completeness, validity, and uniqueness for every module's master and transactional tables, then baseline exactly where vendor, material, and customer data stands today.
Cleansing & Standardisation
Deduplicate vendor and material masters, standardise units of measure and naming conventions, and reconcile field mismatches between ECC and S/4HANA.
Enrichment & Governance
Layer in DUNS numbers, plant hierarchies, and classification data, then put MDG-backed stewardship and validation rules in place so quality holds after go-live.
Know your readiness score before you cut over
Before any ECC-to-S/4HANA migration, we score your master data against the fields, tolerances, and mandatory checks SAP's own conversion tooling will enforce — so surprises show up in a report, not in production.
One data practice, every module
Whichever combination of modules your landscape runs, we work inside SAP's actual table structures and business rules — not a generic data model bolted on afterward.
Migrations don't fail on technology — they fail on data
~40% of S/4HANA migrations slip their original timelineMost S/4HANA conversion projects are scoped around code, configuration, and custom developments. The data is treated as something the legacy system will simply "hand over" — until SAP's own conversion checks start rejecting vendor masters with missing tax fields, material records with invalid units of measure, and customer records that exist three different ways.
By the time those errors surface, they're blocking the cutover. Teams scramble to clean years of accumulated SAP data under deadline pressure, often manually, often more than once.
Profiling and maturing that data before migration turns a deadline crisis into a planned workstream — one with a baseline, a target, and a visible score moving toward it.
Fewer cutover surprises
Conversion-blocking errors are caught and fixed weeks before go-live, not discovered during the cutover window.
One trusted vendor and material record
Deduplicated, standardised masters mean spend analysis and procurement reporting reflect reality, not duplication.
Faster, calmer migration timelines
Clean data going in means fewer rejected loads and re-runs during the technical conversion itself.
Governance that outlasts the project
MDG-backed rules and stewardship keep quality from decaying the month after go-live.
Find out exactly where your SAP data stands
Start with a SAP Data Assessment — a clear, module-by-module read on data quality and migration readiness across your landscape.