SAP Data Profiling: Governed Evaluation Guide

Decision context

SAP data profiling is a critical process for understanding the quality, structure, and completeness of data within SAP systems before modernization or migration efforts. It involves systematically analyzing SAP tables, fields, and records to detect anomalies, inconsistencies, and gaps that could impact downstream processes or reporting accuracy.

Modern ERP modernization projects require evidence-based insights into data quality to mitigate risks associated with corrupted or incomplete data. Profiling helps uncover issues such as missing values, invalid formats, duplicates, and referential integrity violations, which are often hidden in complex SAP data models.

Adranum integrates SAP data profiling by ingesting data-quality evidence alongside other SAP readiness inputs, enabling comprehensive coverage reporting and unsupported input identification. This supports informed decisions on data remediation, retention, or retirement as part of SAP change governance.

Understanding SAP Data Profiling Capabilities

SAP data profiling examines SAP data elements to produce metrics on completeness, uniqueness, conformity, and accuracy. It typically involves scanning ABAP tables, DDIC metadata, and transactional data to identify data quality dimensions relevant to SAP business processes.

Profiling results highlight data anomalies such as null values in mandatory fields, inconsistent key relationships, and format deviations that can cause runtime errors or faulty analytics. These insights are essential for prioritizing data cleansing and transformation activities during SAP upgrades or migrations.

• Completeness checks for missing or null values in critical fields
• Uniqueness analysis detecting duplicate records in key tables
• Format and pattern validation against SAP DDIC definitions

Integrating Data Profiling into SAP Change Governance

Incorporating data profiling into SAP change governance ensures that data quality issues are visible alongside code and process readiness evidence. Adranum’s ingestion of data-quality evidence enables mapping data issues to impacted SAP objects and processes.

This integration supports keep, replace, remediate, or retire decisions on data elements and related code, improving the accuracy of modernization proposals and reducing post-deployment defects. It also facilitates traceability of data quality improvements over time through immutable simulation and improvement history tracking.

• Mapping data anomalies to SAP ABAP and DDIC dependencies
• Generating remediation proposals linked to specific data issues
• Recording approvals for data remediation and retention decisions

Data Profiling Evidence Artifacts and Their Role

Effective SAP data profiling produces concrete evidence artifacts such as data quality reports, anomaly lists, and statistical summaries. These artifacts serve as inputs for automated impact analysis and test selection in SAP modernization.

Adranum captures these artifacts alongside other evidence types, enabling a holistic view of SAP system readiness. The ability to retain raw profiling data within customer infrastructure while sharing bounded summaries ensures compliance and security.

Profiling evidence also supports reconciliation and exact-state recovery by documenting baseline data conditions prior to change execution.

• Data quality scorecards for SAP tables and fields
• Anomaly detection logs with timestamps and affected records
• Statistical distributions of key data attributes

Common Challenges and Failure Modes in SAP Data Profiling

Profiling SAP data can be hindered by incomplete metadata, unsupported custom objects, or transient data states that obscure true data quality conditions. Missing inputs reduce coverage and may lead to overlooked defects.

Data profiling tools must handle large volumes of SAP data efficiently without impacting system performance. Profiling results require validation to avoid false positives or negatives that misguide remediation efforts.

Adranum highlights unsupported inputs and missing data quality evidence, enabling teams to address gaps proactively and improve profiling completeness.

• Handling incomplete or inconsistent DDIC metadata
• Detecting and reporting unsupported custom SAP objects
• Mitigating profiling impact on SAP system performance

Evaluating SAP Data Profiling Solutions for Modernization

When selecting SAP data profiling capabilities, consider integration with SAP change governance workflows, ability to ingest diverse evidence types, and support for dependency mapping between data and code.

Assess solutions on their capacity to generate actionable remediation proposals and track approvals, as well as their security model for handling sensitive SAP data within customer boundaries.

Adranum’s approach emphasizes evidence discipline, traceability, and coordination of cutover and reconciliation activities, making it suitable for complex SAP modernization scenarios.

• Support for multiple SAP data-quality evidence formats
• Automated mapping of data issues to ABAP and DDIC objects
• Secure handling of raw data within customer infrastructure

What the workflow must cover

• Comprehensive Data Quality Evidence Ingestion. Adranum ingests authorized SAP data-quality evidence including profiling reports and anomaly logs, enabling centralized visibility of data issues alongside code and process readiness.
• Dependency Mapping Between Data and Code. It maps observed and inferred dependencies linking data anomalies to impacted ABAP programs and DDIC objects, facilitating targeted remediation efforts.
• Remediation Proposal Generation. Adranum creates reviewable proposals for data remediation actions such as cleansing, retention, or retirement, integrating these into SAP change governance workflows.
• Approval and Traceability Recording. The system records human approvals for data remediation decisions, maintaining an auditable trail that supports compliance and governance requirements.
• Secure Data Handling Within Customer Boundaries. Raw profiling data and credentials remain inside customer infrastructure, while only bounded commands and aggregate outcomes are exchanged, preserving data security.
• Integration with Cutover and Reconciliation Processes. Data profiling evidence supports coordination of cutover, reconciliation, continuity, and exact-state recovery activities during SAP modernization.

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. Identify critical SAP data domains and tables for profiling based on business impact and change scope.
2. Ingest authorized SAP data-quality evidence such as profiling reports and anomaly detection outputs into Adranum.
3. Map data quality issues to ABAP and DDIC dependencies to understand impact on SAP programs and processes.
4. Generate remediation proposals for data cleansing, retention, or retirement and route for human approval within SAP change governance.
5. Coordinate cutover and reconciliation activities incorporating data quality improvements to ensure continuity and exact-state recovery.
6. Track improvement history and update data profiling evidence iteratively during SAP modernization phases.
7. Report coverage gaps and unsupported inputs to address profiling completeness and reduce risk of undetected data defects.

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 data profiling reports detailing completeness and anomaly metrics
• Anomaly detection logs with affected record identifiers
• Statistical summaries of data attribute distributions
• Mapping tables linking data issues to ABAP and DDIC objects
• Remediation proposals for data quality improvements
• Approval records for remediation decisions
• Cutover and reconciliation logs referencing data quality status
• Improvement history documenting profiling iterations
• Raw profiling data retained within customer infrastructure

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 for data management.
• Generated remediation proposals require customer validation and runtime checks before deployment.
• Profiling coverage depends on availability and completeness of authorized data-quality evidence.
• Does not provide official SAP certification or guarantee ROI from data profiling activities.

Public comparison sources

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

• DATA PROFILING OVERVIEW – SAP INFORMATION ...
• Data Profiling
• SAP Data Profiling: The Missing Step in Every Data Migration ...
• Is Data Profiling Really Needed in the SAP World? Absolutely
• Data Stewardship and Integrity | SAP Information Steward

Buyer checklist

• What SAP data domains and tables are critical to profile for this modernization?
• How does the profiling tool integrate with SAP ABAP and DDIC dependency mapping?
• Can raw data profiling outputs remain within our infrastructure for compliance?
• How are remediation proposals generated and approved within SAP change governance?
• What evidence artifacts are produced to support reconciliation and exact-state recovery?
• How does the solution handle unsupported custom SAP objects during profiling?
• What performance impact can profiling have on our SAP systems?
• How are missing or incomplete data-quality inputs identified and managed?