Optimize · Master Data Cleansing & Enrichment
Your migration score and your AI readiness both start with the same data.
Duplicate business partners. Incomplete material masters. Missing vendor bank data. Inconsistent addresses. Every data quality problem that exists today is a migration blocker tomorrow and an AI failure the day after. DEBCOR runs three flavours of agents — surface, enrich, and fix — to drive your master data to the score your programme needs.
SAP Partner Ranking
#5 in the USA
Founded
1997 · 29 Years
Partner Tier
Gold · Expert BTP, BDC & Business Transformation
Delivery Model
Senior-Led, Global Delivery
Outcome
Higher Migration & AI Readiness Scores
The Problem
Data quality problems don't announce themselves. They surface at the worst possible time.
Most organisations running mature SAP landscapes have never done a systematic master data quality assessment. The data works well enough for day-to-day operations — transactions post, reports run, payments go out. The problems only become visible when something puts pressure on the data: a migration, a consolidation, an AI deployment, or an audit.
At that point, the issues that have been accumulating for years become the critical path. The migration readiness score comes back with hundreds of violations. The AI pilot works in the clean demo environment and fails in production. The audit finds inconsistencies across vendor records that nobody can explain.
The right time to address data quality is before any of those moments arrive.
Bad data fails the readiness check
SAP's migration tooling scores your master data before it moves. Duplicate business partners, incomplete material classifications, missing vendor bank data, and malformed addresses all generate violations that block go-live or create rework cycles that extend your timeline by months.
AI amplifies what it learns from
A model trained on duplicate customer records learns that two records for the same company are different companies. A procurement AI working from incomplete material masters makes purchasing recommendations that don't reflect your actual supplier base. Dirty data doesn't just produce wrong answers — it produces confidently wrong answers at scale.
The daily cost of incomplete records
Duplicate vendors create duplicate payments. Incomplete material masters trigger manual intervention on every purchase order. Missing tax classifications cause compliance exposure. These aren't data quality problems — they're operational costs that compound every day the data stays uncleaned.
Reports that can't be trusted
Consolidated financial reporting, spend analytics, customer segmentation, and inventory analysis all run on master data. When that data is fragmented, inconsistent, or duplicated, every report that consumes it requires a manual credibility check before it can be presented. Senior time spent qualifying numbers is senior time not spent on decisions.
The DEBCOR Approach
Three agent flavours. One clean dataset.
Most data quality tools stop at surfacing. We don't stop until the data is fixed. Three distinct agent types handle the distinct phases of a cleansing programme — each doing what it does best, all working under senior human oversight.
Surface Agents
Enrichment Agents
Fix Agents
Agent 01
Surface Agents
Find what's wrong — all of it
Our surface agents scan your master data landscape systematically — not by sampling, but completely. They identify duplicates across business partner records, flag incomplete mandatory fields against your target data model, surface format inconsistencies and encoding violations, detect orphaned records with no active business relationships, and score every object against SAP's migration readiness criteria. The output is a complete quality map with every issue classified by type, severity, and downstream impact — before any human analyst touches the data.
Agent 02
Enrichment Agents
Fill the gaps — intelligently
Flagging incomplete data is not the same as fixing it. Where records are incomplete but the correct data is knowable — from external reference sources, from patterns in your own landscape, or from cross-system reconciliation — our enrichment agents apply it. Missing postal codes corrected. DUNS and tax identification numbers sourced and validated. Material classifications completed against industry standards. Vendor bank data verified against payment history. The goal is to arrive at the fix phase with as much pre-populated, validated data as possible — so your team is reviewing and approving, not typing.
Agent 03
Fix Agents
Apply the corrections — at scale
Once issues are surfaced and enrichment is validated, fix agents execute the corrections at scale — under human oversight, with full audit trails. Duplicate business partners are merged under golden records with relationships preserved. Format violations are corrected systematically. Inactive records are flagged for archival. Field values are standardised across thousands of objects simultaneously. Every action is logged, reversible, and reviewed before it touches production. What would take a data team months of manual work runs in a controlled programme that completes in weeks.
Coverage
Every master data domain that matters.
Engagement scope is agreed during the baseline phase. The domains below represent the full range we cover — driven by migration scope, AI use case requirements, and operational data quality priorities.
Business Partner
Mandatory S/4HANA conversion — customer and vendor consolidation under unified BP model
Material Master
Classification completion, unit of measure consistency, plant data validation
Vendor / Supplier
Duplicate elimination, bank data verification, payment term standardisation
Customer Master
Address validation, credit data consistency, sales area completeness
Chart of Accounts
Account assignment completeness, cost element/centre alignment, intercompany consistency
Cost Centre / Profit Centre
Hierarchy integrity, validity period coverage, responsible person data
Asset Master
Depreciation key completeness, cost centre assignment, asset class consistency
Purchasing Info Records
Price completeness, validity period coverage, source list alignment
Business Partner conversion is non-negotiable for S/4HANA.
S/4HANA requires all customers and vendors to be migrated to the Business Partner model. Every duplicate, inconsistency, or missing field in your customer and vendor master data becomes a BP conversion error. This is consistently the highest-volume source of migration readiness violations — and the domain where our surface and enrichment agents deliver the most immediate impact.
The Outcome
Two scores. One programme.
Every remediation action we take moves both scores simultaneously. The data you need for a clean migration is the same data you need for AI to work in production. This is one investment, not two.
Migration Readiness Score
- SAP's migration tools validate master data against target-system requirements before any object moves
- Violations generate errors and warnings that must be resolved before go-live is approved
- Every issue surfaced and fixed during the cleansing programme is one fewer blocker on go-live day
- Programmes that invest in pre-migration data cleansing consistently see shorter migration timelines and fewer post-go-live data incidents
AI Readiness Score
- AI models require clean, consistent, deduplicated training and inference data
- Duplicate business partners produce conflicting signals that degrade model accuracy on every use case that touches customer or supplier data
- Incomplete material masters make procurement AI recommendations unreliable at the point where they matter most
- Every enrichment applied during cleansing is training signal the AI can use — every gap left unfilled is noise it has to work around
How We Deliver
Five steps. Measurable score at every stage.
Progress is visible from the first week. Migration readiness and AI readiness scores are re-run after every fix batch — so leadership always has a current picture of where the programme stands.
Scope and baseline
We establish the full scope of master data domains in scope, extract a representative sample for initial quality assessment, and baseline the current migration readiness and AI readiness scores. This gives leadership a quantified picture of the gap before any cleansing work begins — and a benchmark to measure progress against.
Surface — full landscape scan
Surface agents run across all in-scope domains. Every object classified. Every issue catalogued. Migration readiness violations mapped to their go-live impact. AI readiness risks scored by downstream use case. The output is a prioritised remediation backlog — ordered by migration criticality first, AI impact second, and operational cost third.
Enrich — fill the gaps before you fix
Enrichment agents run on the subset of records with completeness issues. Where the correct data can be sourced or validated externally, it is. Where it requires business confirmation, we surface it for the smallest possible human review surface — decisions per domain, not record by record. This is where most programmes lose time — we compress it by pre-populating as much as the data allows.
Fix — apply at scale, under governance
Fix agents execute the approved corrections under human oversight. Golden record creation, format standardisation, duplicate merge, field-level corrections. Every change is logged. Every batch is reviewable and reversible. Migration readiness scores are revalidated after each batch so progress is visible in real time.
Validate and govern
Post-cleansing, we run final migration readiness and AI readiness scoring, document the baseline for ongoing governance, and optionally establish monitoring rules that catch data quality regressions before they reaccumulate. Data quality is not a project with an end date — we make sure the work doesn't need to be done again.
“DEBCOR's white-glove approach to consulting isn't just a tagline — it's the reality of their service. They've been our trusted partner for many years. At our request, they cleaned up our technical debt and prepared us for S/4HANA. The migration only took four months — we were down for maybe a weekend, then back to business with no loss in revenue.”
FAQ
Common questions.
What's the difference between data cleansing and data governance?
Cleansing is fixing the accumulated debt — the duplicates, gaps, and inconsistencies that have built up over years. Governance is the discipline that prevents them from reaccumulating. Most organisations need both: a cleansing programme to establish a quality baseline, and governance rules embedded in the process to maintain it. We deliver the cleansing programme and, where appropriate, help design the governance layer that keeps it clean.
Why does master data matter so much for S/4HANA migration?
S/4HANA has a fundamentally different data model from ECC — the most significant change being the mandatory Business Partner model, which consolidates customer and vendor master records that existed as separate objects in ECC. That conversion alone surfaces data quality issues that were invisible in ECC but become migration blockers in S/4. Beyond BP conversion, SAP's migration tooling scores your data before it moves. Low scores generate errors that must be resolved before go-live is approved. Programmes that treat data cleansing as a migration pre-requisite — rather than discovering the issues during migration — consistently go live faster and with fewer post-go-live data incidents.
What is a migration readiness score?
SAP's migration tools validate your data against the target system's structural and business requirements. Each object is assessed: mandatory fields present, format compliance met, referential integrity intact, business partner conversion viable. The aggregate pass/fail rate across domains gives a readiness score. A low score means significant rework before the data can be loaded into S/4HANA. Our cleansing programme is designed to drive that score up measurably — with progress visible after each remediation batch.
How is AI readiness different from migration readiness?
Migration readiness measures whether data meets the structural requirements of the target system. AI readiness measures whether data is reliable enough to train models on and act on in production. A record can be migration-ready — all mandatory fields populated, correctly formatted — and still be AI-problematic if it's a duplicate, if key fields contain placeholder values, or if it's inconsistent with related records across domains. Our enrichment and fix agents address both dimensions simultaneously: migration readiness first (unblocking go-live), AI readiness second (unblocking the use cases you want after go-live).
What master data domains do you cover?
Business Partner (including customer and vendor conversion), Material Master, Chart of Accounts, Cost Centre and Profit Centre hierarchies, Asset Master, Purchasing Info Records, and Customer/Vendor payment and bank data. The scope of any engagement is agreed during the baseline phase based on what's in scope for migration and what's required for the AI use cases the organisation wants to activate post-go-live.
Can this run in parallel with our S/4HANA migration programme?
Yes — and it often should. Data cleansing that runs ahead of migration reduces the migration team's rework burden during extraction, transformation, and load. Running both in parallel requires sequencing discipline: cleansing needs to complete for each domain before that domain's migration objects are extracted. We design the cleansing programme workplan to align with the migration programme's domain schedule so neither workstream blocks the other.
Cleaning data as part of an S/4 programme?
See RISE with SAP →Custom code also needs remediation?
See Custom Code Remediation →Need ongoing data stewardship post-cleansing?
See AMS / Managed Services →Know your score before your migration does.
Start with a baseline assessment. We'll give you a current migration readiness score and AI readiness rating across your master data domains — so you know exactly what you are working with before the programme begins.