Data Migration & Quality Agent

Overview

The Data Migration & Quality Agent treats data as a transformation workstream with repeated evidence—not a late technical load.

Lifecycle: Prepare through Run
Default autonomy: Execute approved non-production actions
Human owner: Data owner or migration lead
Typical consulting roles: Data migration lead, data architect, SAP migration consultant, data-quality analyst, ETL developer

The implementation challenge

Data problems are often discovered repeatedly across extraction, testing, training and cutover. Ownership remains unclear, cleansing progress is difficult to evidence and reconciliation is compressed near go-live.

Mission and boundaries

The agent maintains data-object scope, profiles quality, proposes mappings and rules, coordinates migration cycles and assembles reconciliation evidence.

It does not decide business ownership, approve destructive cleansing, sign off migrated balances or execute sensitive production actions without authorization.

Role across SAP Activate

Prepare: define objects, ownership, tools and migration strategy.
Explore: clarify data requirements, mappings and quality rules.
Realize: support repeated loads, defect analysis and reconciliation.
Deploy: coordinate final migration evidence and cutover.
Run: monitor data quality and improvement.

Key use cases

profile source data against target requirements.
identify inconsistent mappings across countries.
prioritize cleansing by business and cutover impact.
compare migration-cycle results.
prepare reconciliation and sign-off evidence.

Required skills

Data architecture, S/4HANA business objects, Migration Cockpit, ETL, profiling, data quality, reconciliation, controls and relevant process knowledge.

How it works

The agent uses source extracts, target structures, mapping rules, ownership, defects, load results and reconciliations. It produces profiles, mapping proposals, cleansing queues, migration-cycle comparisons, exceptions and evidence.

Human–agent operating model

The agent scales analysis, comparison, mapping and cycle reporting. Business owners decide data meaning and cleansing. Migration leaders and control owners approve reconciliations and cutover.

Governance and risks

Data access, privacy, retention and cross-environment movement require strict controls. Automated cleansing must be reversible and approved. Statistical quality does not replace business validation.

Success and maturity

Measures include defect recurrence, mapping reuse, cleansing closure, load success, reconciliation timeliness and post-go-live data incidents.

Example

The agent detects that material master units differ across plants, links the issue to planning and valuation consequences and routes prioritized cleansing to named owners before the next cycle.

Process / Fit-to-Standard Agent, Integration Agent, Security & Controls Agent, Testing & Quality Agent, Cutover & Readiness Agent, Operations & Continuous Improvement Agent and Evidence Validator.