V3Qtor AG/Systems/Master Data Management Layer

One record. Every ERP. The V3Qtor Master Data Management Layer.

Materials, customers, suppliers, assets and lots kept identical across SAP, Oracle, Microsoft, MES, PLM and the systems nobody admits to running. Real-time synchronisation, governance rules, conflict resolution and lineage, so that an ERP transformation moves the system and keeps the data.

Book a margin review ERP integrations and transformations

30+ integrations in a typical group. 100% consistency is a number the layer reports, not a target.

Master Data Management Layer Live

Golden recordLast sync 00:00:41

Golden records1.84MMaterials, customers, suppliers, assets

Connected systems31SAP, Oracle, Dynamics, MES, PLM, legacy

Consistency100%0 silent conflicts

Conflicts resolved, 30 days3,11292% by rule, 8% by owner

SystemsDomainsGovernance rulesConflictsLineage

System	Domain	Records	Direction	Lag	State
SAP S/4HANA, plant Nord	Materials, BOM, assets	612,440	Two-way	< 1 s	✓In sync
SAP ECC 6.0, plant Süd	Materials, customers	388,120	Two-way	2 s	Migration wave 2
Oracle EBS, finance	Customers, suppliers	214,900	Two-way	< 1 s	✓In sync
Microsoft Dynamics 365, sales	Customers, prices	96,310	Inbound	< 1 s	✓In sync
MES, lines 1–14	Assets, lots, routings	1,204,880	Outbound	< 1 s	✓In sync
Access database, quality lab	Certificates	4,120	Inbound, nightly	6 h	Legacy, read-only

Illustrative. Volumes and lags are typical for a mid-size industrial group.Conflict rule set v14 · 212 rules

What it does

The golden record, and the rules that keep it golden.

Every connected system reads and writes the same record. When two systems disagree, a governance rule decides; when no rule applies, the data owner does, once, and the decision becomes a rule.

1
DomainsMaterial and BOM, customer, supplier, asset, lot and batch, employee and roster, price and contract. Each with an owner, a rule set and a lineage.
2
Real-time synchronisationTwo-way where both systems are authoritative, one-way where one is. Lag measured in seconds; nightly only for read-only legacy sources.
3
Conflict resolutionRules first, owners second. Ninety-two percent of conflicts in a typical month resolve by rule; the rest go to a named person with the evidence attached.
4
LineageEvery field traces to its source system, its last change and the rule or person who made it. Regulatory reports and audit packs are built on it.
5
What it feedsPredictive maintenance needs clean asset data; 8D needs lot genealogy; dynamic pricing needs one customer record; every AI model needs the data RAND lists as the second root cause of failure¹.

The domains

What a golden record holds, and who is allowed to change it.

The layer governs attributes rather than whole tables. Every attribute has a source of truth, an owner and a rule; the rule is what resolves most conflicts without a person.

Domain	Attributes governed	Typical sources	Rules that keep it golden
Materials	Material number, description, unit of measure, plant data, BOM, routing	SAP MM, PLM, MES	One unit of measure per material. BOM changes only arrive from PLM.
Customers	Legal entity, ship-to and bill-to addresses, payment terms, credit limit, tax number	CRM, SAP SD, Oracle receivables	One legal entity per VAT number. Credit limit owned by finance.
Suppliers	Legal entity, bank details, certificates, lead times, approved parts	SAP MM, procurement, ShieldOS	Bank changes need a second approver. Expired certificate blocks new orders.
Assets	Tag, location, criticality, maintenance plan, Ex certificate, sensors	SAP PM, CMMS, historian	One tag per physical asset. Criticality set by maintenance, not by IT.
Lots and batches	Genealogy, quantities, quality status, certificates of analysis	MES, LIMS, warehouse system	A lot cannot ship without a released certificate of analysis.

ERP integrations

Integrated at the record.

Each ERP keeps its native interfaces. The layer sits between them and holds the record both sides agree on.

SAP S/4HANA and ECC

OData, RFC, IDoc and CDS views. Two-way synchronisation for materials, BOMs, assets, customers and suppliers. ECC and S/4 side by side during migration.

Oracle

E-Business Suite and Fusion Cloud through REST and database adapters. Finance master data, suppliers and customers.

Microsoft Dynamics 365

Dataverse and Business Central APIs. Customers, prices, sales orders inbound to the golden record.

MES, PLM and legacy

Shop-floor systems, product lifecycle data, Access and spreadsheet sources read-only on a schedule, with lineage.

ERP transformations

Move the system. Keep the data.

Mainstream maintenance for SAP Business Suite 7 core applications, including ECC 6.0, runs to the end of 2027, with optional extended maintenance to 2030². Most groups will migrate with the data they have, which is where transformations fail. The layer is the part of the landscape that survives the migration.

Phase 1

Assess

Every domain profiled: duplicates, conflicts, orphans, fields nobody uses. The cost of the current state, in rework and failed postings.

Output: data-quality baseline, per domain

Phase 2

Cleanse

Duplicates merged by rule, owners named, conflicts resolved once. The golden record established in the layer, before any system moves.

Output: golden record, 100% consistent

Phase 3

Harmonise

Numbering, units, classifications and hierarchies aligned across plants and entities. Governance rules written and tested against live traffic.

Output: rule set, lineage live

Phase 4

Migrate

Wave by wave. The target system is loaded from the golden record; the old system stays in sync until its last wave. No big-bang cut-over.

Output: each wave reconciled to zero

Phase 5

Run

The layer stays. The next system, the next acquisition, the next AI model plugs into the record that already exists.

Output: an operating rhythm that outlives the project

Why it matters to the P&L

Bad master data is the quietest cost line in the group.

1streason named

Weak data quality is the first reason Gartner gives for GenAI projects abandoned after proof of concept³.

Gartner, 2024

2ndroot cause

RAND's second root cause of AI project failure: organisations lack the data needed to train an effective model¹. The layer is that data.

RAND Corporation, 2024

2027end of mainstream maintenance

SAP Business Suite 7 core applications, including ECC 6.0; extended maintenance optional to 2030². The migration is coming; the data decides whether it works.

SAP, February 2020

Talk to us about one data domain.

Materials, customers, suppliers or assets, whichever gives you the most trouble. We will profile it with your data owners, show you the duplicates and conflicts and what they cost in failed postings, and tell you what one record would take.

Book a margin review

Notes and sources

1RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, RRA2680-1, August 2024. More than 80% of AI projects fail, twice the rate of IT projects without AI; 65 experienced practitioners interviewed. Source
2SAP maintenance commitment announced February 2020: mainstream maintenance for SAP Business Suite 7 core applications (including ECC 6.0) runs to end of 2027, with optional extended maintenance to end of 2030. Source
3Gartner, August 2024: at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, citing data quality, risk controls, cost and unclear business value. Source