Central governance for material, vendor, customer, and financial data domains on SAP MDG, configured to your data model, approval workflows, and business rules.
Structured profiling of your current master data landscape: duplication, completeness gaps, consistency failures, and field-level quality scoring before migration or MDG rollout begins.
MDG domain setup aligned to your enterprise structure: single-object and multiple-object processing, key mapping, value mapping, and hierarchy management.
End-to-end workflow design for data creation, change, and retirement: request routing, approval hierarchy, SLA controls, and audit trail.
We define the roles, responsibilities, escalation paths, and governance cadence your organization needs to sustain data quality without depending on periodic cleanup projects.
MDG-to-S/4HANA replication, SAP Business Data Cloud and Datasphere alignment, and master data feeds to BI and planning layers so every downstream system works from a single governed source.
Master data quality is the single most common cause of delayed cutovers and post-go-live rework. An MDG program started before the blueprint is signed reduces migration risk and post-production firefighting.
Agentic AI and intelligent process automation depend on consistent, governed master data. When the data feeding these systems is inconsistent, the outputs are too. MDG is the prerequisite, not the follow-on.
When finance, operations, and commercial teams are reconciling reports rather than acting on them, the root cause is almost always master data. MDG closes the gap at the source.
Candent’s Autonomous Enterprise practice builds agentic AI and intelligent process automation directly on SAP. The precondition for that work is governed master data.
An AI agent handling vendor onboarding, invoice matching, or demand forecasting does not tolerate duplicate records, inconsistent classifications, or stale pricing hierarchies. It returns wrong outputs, stalls at exception handling, or triggers unnecessary human review, which defeats the purpose entirely.
MDG is not background infrastructure. It is what determines whether automation delivers at scale or accumulates a backlog of exceptions.
When master data is governed centrally through SAP MDG, AI and process automation run on a foundation that is auditable, correctable, and built to scale with the enterprise.
No MDG engagement starts with a standard configuration checklist. The right program depends on which data domains are in scope, the state of existing master data, the migration timeline, and the downstream systems that depend on it.
data volumes, domain coverage, duplication rates, and quality scoring across material, vendor, customer, and financial master data
where current data quality falls short of the S/4HANA, AI, or analytics requirements
which master data domains to govern first based on business risk and project timeline
team structure, workflow design, stewardship model, and tooling
how data is cleaned, validated, and loaded into SAP MDG before or alongside the S/4HANA program
