SAP Data Quality Assessment: Evidence Readiness Framework

Decision context

SAP data quality assessment is a critical step in ensuring reliable, compliant, and efficient SAP ERP operations, especially during modernization or change initiatives. Adranum’s governed software ingests authorized SAP data-quality evidence alongside readiness and process data to provide a holistic view of data health and coverage. This enables teams to identify unsupported inputs, data bottlenecks, and quality gaps that could jeopardize project success or operational continuity.

Unlike generic data profiling tools, Adranum integrates SAP-specific artifacts such as ABAP, DDIC, and connector evidence, mapping observed and inferred dependencies between data elements and processes. This comprehensive approach supports informed keep/replace/remediate/retire decisions for SAP data assets, aligning with governance policies and operational realities.

Adranum’s assessment outputs include reviewable proposals for data remediation, implementation packages, and impacted test cases. It records human approvals and coordinates cutover and reconciliation activities, enabling traceable, controlled improvements to SAP data quality within customer infrastructure boundaries.

Integrating SAP Data Quality Evidence

Adranum collects and normalizes multiple SAP data quality inputs including readiness scans, ABAP and DDIC analyses, and connector logs. This integration ensures a comprehensive baseline for data assessment, highlighting unsupported or incomplete data sources.

The tool’s ability to ingest process-event and requirement evidence further enriches context, linking data quality issues to business processes and compliance requirements. This end-to-end visibility is essential for prioritizing remediation efforts effectively.

• Supports authorized SAP readiness and ATC data-quality inputs
• Includes ABAP, DDIC, and connector evidence ingestion
• Maps data dependencies to business processes and events

Mapping Dependencies and Impact Analysis

Adranum infers and observes dependencies between SAP data elements, processes, and interfaces, enabling precise impact analysis for data quality issues. This mapping helps identify bottlenecks and variant patterns affecting data integrity.

By visualizing these dependencies, teams can simulate remediation scenarios and predict downstream effects, reducing risks of unintended consequences during data changes or modernization.

• Dependency graphs linking data elements and processes
• Immutable simulations of remediation impacts
• Identification of data bottlenecks and variant behaviors

Governed Remediation Planning and Execution

The platform supports keep, replace, remediate, or retire decisions for SAP data assets, generating reviewable code and data proposals. These proposals remain under customer control until compilation and runtime validations succeed.

Adranum records human approvals and coordinates cutover, reconciliation, and exact-state recovery activities. This governance framework ensures traceability and compliance throughout the data quality improvement lifecycle.

• Reviewable remediation proposals with customer-controlled validation
• Human approval recording and audit trails
• Cutover and reconciliation coordination with exact-state recovery

Customer Infrastructure Data Sovereignty

Adranum allows signed customer operators to retain raw service data, source code, and credentials within their own infrastructure. Only bounded commands, hashes, aggregate results, and reconciliation evidence leave the environment.

This design supports compliance with data privacy and security policies, reducing exposure risks while enabling centralized governance and reporting.

• Raw data and credentials remain on-premises or in customer cloud
• Only bounded commands and aggregate outcomes transmitted
• Supports compliance with internal data governance policies

Process Variant and Bottleneck Analysis

The tool analyzes observed process variants and bottlenecks affecting data quality, providing immutable simulations and improvement history tracking. This insight helps teams understand root causes and measure remediation effectiveness over time.

By capturing improvement history, Adranum supports continuous data quality governance and operational resilience.

• Observed variant detection and bottleneck identification
• Immutable simulations for impact prediction
• Improvement history and trend analysis

What the workflow must cover

• Comprehensive Evidence Ingestion. Adranum ingests multiple SAP data quality and readiness sources, ensuring a thorough baseline for assessment.
• Dependency Mapping and Impact Simulation. The software maps data dependencies and simulates remediation impacts to support risk-aware decision making.
• Governed Remediation Proposal Generation. It generates reviewable remediation proposals with human approval workflows and audit trails for compliance.
• Cutover and Reconciliation Coordination. Adranum coordinates cutover activities and exact-state recovery to minimize operational disruption during data quality improvements.
• Customer-Controlled Data Sovereignty. Raw data and credentials remain within customer infrastructure, supporting privacy and security requirements.
• Process Variant and Bottleneck Analysis. The tool identifies process variants and bottlenecks affecting data quality, enabling targeted improvements and continuous governance.

Implementation workflow

Start with a bounded customer scenario and explicit acceptance criteria. Preserve native SAP permissions and accountable review while the software creates a repeatable evidence chain.

1. Collect authorized SAP data quality evidence including readiness scans, ABAP, DDIC, and connector logs.
2. Normalize and integrate evidence to establish a comprehensive data quality baseline.
3. Map observed and inferred dependencies among data elements, processes, and interfaces.
4. Generate reviewable remediation proposals for keep/replace/remediate/retire decisions.
5. Record human approvals and audit trails for governance compliance.
6. Coordinate cutover, reconciliation, and exact-state recovery activities.
7. Track improvement history and simulate future remediation impacts for continuous governance.

Evidence to require

A transformation claim should resolve to observable artifacts, decisions, and execution receipts. Ask for the following evidence in a representative evaluation:

• SAP readiness scan results
• ABAP and DDIC static code analysis reports
• Connector data quality logs
• Process-event and requirement traceability records
• Dependency mapping graphs
• Remediation proposal documents
• Human approval and audit trail records
• Cutover and reconciliation activity logs
• Improvement history reports
• Simulation outcome records

Boundaries and non-claims

Adranum separates analysis, proposal, human review, package creation, customer-local validation, and production execution. A later state never rewrites the evidence that supported an earlier decision.

• Does not replace SAP licensing or Basis administration responsibilities.
• Generated remediation code is a proposal until customer-controlled compilation and runtime validation.
• Missing or incomplete data quality inputs remain visible and require customer action.
• Does not provide full replacement for specialist SAP testing products in all scenarios.

Public comparison sources

Competitor statements are limited to current public materials. Verify them during procurement because products and packaging change.

• Data Quality Management
• Data Quality with SAP? : r/dataengineering
• Boost your data quality with Data Quality Service
• Introduction SAP Data Quality Management (DQM) - Avelon
• Data Quality in SAP Systems

Buyer checklist

• What SAP data quality evidence sources does the tool support?
• How does Adranum map data dependencies and simulate remediation impacts?
• Can remediation proposals be customized before implementation?
• How does the platform ensure data sovereignty and security?
• What governance and approval workflows are included?
• How are cutover and reconciliation activities coordinated?
• Does the tool integrate with existing SAP testing or monitoring solutions?
• What visibility is provided into missing or unsupported data inputs?