You're leading a critical SAP system upgrade or conversion project. Data integrity is paramount; ensuring accurate data migration is crucial for a successful go-live. The data transition validation tool offers a powerful way to validate your data, but maximizing its effectiveness requires careful planning and execution. This lesson outlines best practices to optimize your data transition validation, categorized into four key project phases:
- Project Planning
- System Preparation
- Data Transition Validation Tool Configuration
- Data Transition Validation Execution
Project Planning: Laying the Foundation for Success
Before you even begin configuring data transition validation, thorough planning is essential. This includes:
- Understanding data transition validation scope: Clearly define what data you'll validate and which reports will be used. This involves identifying the critical business processes and the data elements essential to their functionality. A well-defined scope prevents scope creep and ensures focused testing.
- Finalizing test and data scope: Determine the specific reports to validate and the data volume (number of company codes, GL accounts, fiscal years, etc.) to be included in the data transition validation process. This allows for realistic time estimations and resource allocation.
- Incorporating data transition validation into each project cycle: Integrate data transition validation into each project phase (dress rehearsal, performance runs, etc.). This iterative approach allows for early identification and resolution of issues, improving the overall quality of the data migration. Early testing also helps refine data transition validation run times, leading to faster execution during the final production run. Remember to factor the initial setup time for data transition validation testing into your project timeline.
System Preparation: Setting the Stage for Optimal Performance
Proper system preparation is key to efficient data transition validation:
- Utilizing the latest version of the data transition validation tool: Employ the most recent version of the tool (refer to SAP Note 3117879 for details). This ensures access to the latest features, performance improvements, and bug fixes.
- Implementing Relevant SAP Notes: Apply all necessary SAP Notes related to data transition validation to ensure smooth operation and avoid potential compatibility issues.
- Optimizing System Resources: Effectively configure system resources (servers, background job scheduling, memory parameters). This includes adjusting parameters such as rdisp/PG_KEEP, rdisp/PG_MAXFS, rdisp/PG_SHM, and ABAP/shared_objects_size_MB (refer to SAP Note 3226794 for guidance). Maintain an optimal number of background jobs to avoid resource contention.
- Importing Pre-delivered Content: Import the latest pre-delivered data transition validation content (refer to SAP Note 3258383) to leverage pre-built configurations and reports.
- Maintaining a Source System Copy (Recommended): Consider creating a copy of the source system after data transition validation source execution. This allows for post-execution reconciliation and comparison with the target system.
- Leveraging Project Export/Import: Utilize data transition validation's project export/import functionality to save time and effort when configuring data transition validation across multiple systems in your landscape.
Data Transition Validation Configuration: Fine-tuning for Efficiency
Careful configuration maximizes data transition validation's effectiveness:
- Data Splitting for Faster Extraction: Divide large datasets into smaller chunks to improve extraction speed and avoid long-running jobs. In the data transition validation tool you can also perform executions at the work item level.
- Multiple Report Imports: Import reports multiple times if different variants are used for the same report.
- Consistent Variants Across Systems: Ensure that report variants used for data transition validation execution exist in all relevant systems.
- ALV Output: Data transition validation only uses ALV (output type) and background executable reports. Ensure your chosen report variant produces an ALV output (not a classical list).
- Multiple Project Global Data Definitions: Create multiple project global data definitions for the same parameter (e.g., company code for fixed assets, company code for MM) to allow for different sets of relevant codes.
- Split Parameter Granularity: Carefully define split parameters to control the granularity of the execution. Experiment with simulation to find the optimal split size.
- Consistent Input Parameters: Use identical input parameters for source and target systems.
- Avoid Target System Changes: Refrain from modifying project global data in the target system after initial configuration.
- Exclude Parameters: Utilize the exclude parameter to specify fields that should be excluded from validation
- Custom Report Handling: For custom reports, adjust output keys as needed to ensure proper evaluation.
Data Transition Validation Execution and Post-Execution Analysis: Ensuring Accuracy
- Import Simulation Results: If simulation is used, import simulation results to avoid redundant extractions.
- Result Data Handling: The initial result display might show only 10,000 records. Click the refresh button in the ALV toolbar to view all results.
- Post-Execution Reconciliation: Remember, data transition validation is a validation tool, not a reconciliation tool. Discrepancies identified by data transition validation require manual reconciliation by comparing the validation results with reports run directly in SE38 using the same parameters. Reconcile results in both source and target systems to ensure accuracy.
Summary
- Optimized data transition validation requires careful planning, system preparation, and configuration.
- Iterative data transition validation usage throughout the project lifecycle allows for early problem detection.
- Proper resource allocation and configuration maximize data transition validation performance.
- Post-execution analysis and reconciliation are crucial for ensuring data integrity.