| Status | Approved |
|---|---|
| Owner | |
| Stakeholders |
Purpose
The purpose of this document is to define the conversion approach for loading Business Partner – Contact Person Relationships in SAP S/4HANA. In ECC, contact persons are linked to customers using contact-specific tables. In S/4HANA, contact persons are modeled as Business Partners with a relationship type (e.g., BUR001) established between them and the related organization.
Conversion Scope
Customer Contact Person
The scope of this document covers the approach for converting active Contact Person from Legacy Source Systems into S/4HANA Business Partner (BP) Relationship Master Data Design Standard.
The data from legacy system includes:
- An individual is created with mandatory information : Name, Last Name, telephone, email address etc.
- An individual is assigned as a contact person OR assigned with partner functions under partner type "AP - Contact Person.
- There is no Deletion flag for the contact person (sales level) (KNVK-LOEVM)
The data from legacy system excludes:
- Contacts without mandatory contact details : Last Name, Telephone, Email
- Contacts not linked to any customers within scope.
| Source | Scope | Source Approx No. of Records | Target System | Target Approx No. of Records |
|---|---|---|---|---|
| WP2 | Customer Contact Person | S4 Hana ROW | ||
| PF2 | Customer Contact Person | S4 Hana ROW | ||
| WP2 | Customer Contact Person | S4 Hana China | ||
| PF2 | Customer Contact Person | S4 Hana China | ||
| WP2 | Customer Contact Person | S4 Hana CUI | ||
| PF2 | Customer Contact Person | S4 Hana CUI |
Vendor Contact Person Relationship
The data from legacy system includes:
The data from legacy system excludes:
List of source systems and approximate number of records
| Source | Scope | Source Approx No. of Records | Target System | Target Approx No. of Records |
|---|---|---|---|---|
| WP2 | Vendor Contact Person | S4 Hana ROW | ||
| PF2 | Vendor Contact Person | S4 Hana ROW | ||
| WP2 | Vendor Contact Person | S4 Hana China | ||
| PF2 | Vendor Contact Person | S4 Hana China |
Additional Information
Multi-language Requirement
N/A
Document Management
N/A
Legal Requirement
CMMC 2.0 is a mandatory DoD cybersecurity certification for contractors handling Controlled Unclassified Information (CUI) and Federal Contract Information (FCI). CUI includes sensitive technical data (e.g., design specs, system info) related to U.S. military and space applications. The Composites Business handles CUI and is therefore within CMMC scope. Without certification, the business risks disqualification from existing and future DoD programs.
It is mandatory to implement CMMC-compliant systems and processes to for all the organizations that are dealing with CUI.
Therefore, there will be one SAP instance specifically for CUI related entities. The migration for CUI related entities will be covered by US based data consultant using separate tools.
Special Requirements
A. Different SAP Instance Migration Approach
Due to compliance requirement, there will be one SAP instance for Rest of the World (ROW), one for China and one for CUI. For BP general data, the same data will be created in all 3 SAP instances.
Target Design
The technical design of the target for this conversion approach.
| Table | Field | Data Element | Field Description | Data Type | Length | Requirement |
|---|---|---|---|---|---|---|
Data Cleansing
Same data cleansing requirements with object 3011.
| ID | Criticality | Error Message/Report Description | Rule | Output | Source System |
|---|---|---|---|---|---|
Conversion Process
The high-level process is represented by the diagram below:
1. Extraction
The process begins with extracting metadata and raw data from source systems, such as Syensqo ECC system (i.e., WP2/PF2) periodically. The extracted data is then staged for transformation.
2. Transformation
Once extracted, the data undergoes cleansing, consolidation, and governance. This step ensures data integrity, consistency, and compliance with business rules. The transformation process includes:
- Data validation to remove inconsistencies.
- Standardization to align formats across datasets.
- Business rule application to refine data for operational use.
3. Loading
The transformed data is then loaded into the target S4 Hana system.
Data Privacy and Sensitivity
The object contains the exact contact information of a specific individual. This data must be handled with great care.Extraction
Extract data from a source into Syniti Migrate. There are 2 possibilities:
- The data exists. Syniti Migrate connects to the source and loads the data into Syniti Migrate. There are 3 methods:
- Perform full data extraction from relevant tables in the source system(s).
- Perform extraction through the application layer.
- Only if Syniti Migrate cannot connect to the source, data is loaded to the repository from the provided source system extract/report.
- The data does not exist (or cannot be converted from its current state). The data is manually collected by the business directly in Syniti Migrate. This is to be conducted using DCT (Data Collection Template) in Syniti Migrate
The agreed Relevancy criteria is applied to the extracted records to identify the records that are applicable for the Target loads
Extraction Run Sheet
| Req # | Requirement Description | Team Responsible |
|---|---|---|
| 1 | Extraction Scope Definition - Identify the source systems and databases involved. | Syniti / LTC Data team |
| 2 | Extraction Methodology - Specify the extraction approach (full, incremental, or delta extraction). | Syniti |
| 3 | Extraction Execution Plan - Establish execution timelines and batch processing schedules. | Syniti |
| 4 | Data Quality and Validation - Define error handling mechanisms for extraction failures. | Syniti |
Selection Screen
| Selection Ref Screen | Parameter Name | Selection Type | Requirement | Value to be entered/set |
|---|---|---|---|---|
| SE16N | Contact person for in-scope customers | To select all contact person assigned to in-scope customers | Select all contact person from table KNVK Where KNVK-KUNNR is in object 3007 customer migration list. | |
Data Collection Template (DCT)
Target Ready Data Collection Template will be created for data with exception of some fields which require transformation as mentioned in the transformation rule.| Field Name | Field Description | Rule |
|---|---|---|
Extraction Dependencies
| Item # | Step Description | Team Responsible |
|---|---|---|
| 1 | Source System Availability
| Syensqo IT |
| 2 | Data Structure
| Syniti |
| 3 | Referential Integrity
| Syniti |
| 4 | Extraction Methodology
| Syniti |
| 5 | Performance and Scalability Considerations
| Syniti |
| 6 | Security and Compliance
| Syniti |
Transformation
The Target fields are mapped to the applicable Legacy field that will be its source, this is a 3-way activity involving the Business, Functional team and Data team. This identifies the transformation activity required to allow Syniti Migrate
to make the data Target ready:
- Perform value mapping and data transformation rules.
- Legacy values are mapped to the to-be values (this could include a default value)
- Values are transformed according to the rules defined in Syniti Migrate
- Prepare target-ready data in the structure and format that is required for loading via prescribed Load Tool. This step also produces the load data ready for business to perform Pre-load Data Validation
Transformation Run Sheet
| Item # | Step Description | Team Responsible |
|---|---|---|
| 1 | Transformation Scope Definition - Identify the source and target data structures. - Define business rules for data standardization. - Establish data cleansing requirements to remove inconsistencies. | Data Team, Functional Team |
| 2 | Data Mapping and Standardization - Align source fields with target fields. - Ensure unit consistency (e.g., currency, measurement units) | Data Team, Functional Team |
| 3 | Business Rule Application - Implement data enrichment/collection if applicable - Apply conditional transformations based on predefined logic/business rules | Data Team, Functional Team |
| 4 | Transformation Execution Plan - Define batch processing schedules. - Assign responsibilities for monitoring execution. - Establish error-handling mechanisms | Synithi |
| 5 | Configure transformation rules in Syniti Migrate (including calculated fields, formatting rules, etc.) | Data Team (Syniti), Data Team (L2C) |
| 6 | Review transformation logic and mappings with Business for confirmation | Business Team + Functional Team (L2C) |
| 7 | Perform initial transformation run and generate draft target-ready dataset | Data Team (Syniti), |
| 8 | Review draft target-ready data for structure and completeness | Data Team (L2C), Functional Team (L2C) |
| 9 | Share transformed data with Business for Pre-load Validation | Business Team |
| 10 | Incorporate feedback from Business and refine mappings or transformation logic as needed | Data Team (L2C) |
| 11 | Finalize and approve transformed data as Target Ready Load File | Business + Functional (L2C) + Data Team (L2C) |
| 12 | Handover final file to Load Team or trigger the load via Syniti Load Workbench | Data Team (Syniti), Data Load Team |
Transformation Rules
| Rule # | Source system | Source Table | Source Field | Source Description | Target System | Target Table | Target Field | Target Description | Transformation Logic |
|---|---|---|---|---|---|---|---|---|---|
| XXXX | |||||||||
Transformation Mapping
| Mapping Table Name | Mapping Table Description |
|---|---|
Transformation Dependencies
List the steps that need to occur before transformation can commence| Item # | Step Description | Team Responsible |
|---|---|---|
| 1 | Source Data Integrity - Ensure extracted data is complete, accurate, and consistent. - Validate that data types and formats align with transformation requirements. | Synithi |
| 2 | Referential Integrity - Ensure dependent records are transformed together or in advance | Synithi |
| 3 | Transformation Logic and Mapping - Define data mapping rules between source and target schemas. | Data Team |
| 4 | Performance and Scalability Considerations - Optimize transformation processes for large datasets. - Ensure system resources can handle transformation workloads | Synithi |
| 5 | Logging and Error Handling - Maintain detailed logs of transformation activities. - Define error-handling procedures for failed transformations | Synithi |
Pre-Load Validation
Project Team
The following pre-load validations will be performed by the Project Team.Completeness
| Task | Action |
|---|---|
Validate the record count | Verify that the data extracted from the legacy system is complete and accurate by comparing record counts and contact person against the source system |
Validate the mandatory fields | Validate there is value for all the mandatory fields |
Accuracy
| Task | Action |
|---|---|
Validate the transformation | Verify that all transformation rules have been correctly applied and that transformed fields display the expected target values |
Validate the extraction | Verify that all copied fields accurately reflect the source system values to ensure the extraction process has correctly transferred the data. |
Business
The following pre-load validations will be performed by the Business.Completeness
| Task | Action |
|---|---|
Validate the record count | Verify that the data extracted from the legacy system is complete and accurate by comparing record counts and contact person against the source system |
Validate the mandatory fields | Validate there is value for all the mandatory fields |
Accuracy
| Task | Action |
|---|---|
Validate the transformation | Validate the fields which require transformation have the value after transformation instead of the original field value |
Validate the extraction | Verify that all copied fields accurately reflect the source system values to ensure the extraction process has correctly transferred the data. |
Load
The load process includes:
- Execute the automated data load into target system using load tool or product the load file if the load must be done manually
- Once the data is loaded to the target system, it will be extracted and prepared for Post Load Data Validation
Load Run Sheet
| Item # | Step Description | Team Responsible |
|---|---|---|
| 1 | Load Scope Definition - Identify the target system and database structure. - Define data objects (tables, fields, records) to be loaded. - Establish business rules for data validation. | Data team |
| 2 | Load Methodology - Specify the loading tools and technologies (Migration Cockpit, LSMW, custom loading program). | Syniti |
| 3 | Data Quality and Validation - Ensure data integrity checks (null values, duplicates, format validation). - Perform pre-load validations to verify completeness. - Define error handling mechanisms for load failures | Syniti |
| 4 | Load Execution Plan - Establish execution timelines and batch processing schedules. - Assign responsibilities for monitoring execution. - Document dependencies on other migration tasks | Syniti |
| 5 | Logging and Reporting - Maintain detailed logs of loading activities. - Generate summary reports on loaded data volume and quality. - Define escalation procedures for errors | Syniti |
Load Phase and Dependencies
Configuration
| Item # | Configuration Item |
|---|---|
| XXX | |
Conversion Objects
| Object # | Preceding Object Conversion Approach |
|---|---|
| 3011 | list the exact title of the conversion object of only the immediate predecessor – this will then confirm the DDD (Data Dependency Diagram) |
Error Handling
| Error Type | Error Description | Action Taken |
|---|---|---|
| 1 | The value XXX for field XXX doesn't exist |
|
| 2 | There is mandatory field XXX missing |
|
Post-Load Validation
Project Team
The following post-load validations will be performed by the Project Team.Completeness
| Task | Action |
|---|---|
Validate Record count in the backend | Validate the main tables BUT100 is loaded. |
Display Records | Pick up few random BP numbers, and Run the BP Report to validate the BUP001 information can be displayed without any error |
Accuracy
| Task | Action |
|---|---|
| Check values in key fields for accuracy | Post-load reports will have the same structure as the load file and some additional columns as required to facilitate the post load validation. Leverage on tool to create a Post Load report that reports S/4HANA loaded records along with the legacy values side-by-side to allow for 100% check of all these fields in the shortest possible time. Any mismatch will be reported under the Post Load - Error report. |
Business
The following post-load validations will be performed by the Business.
Completeness
| Task | Action |
|---|---|
| Record Count Check | Review the record count report from the Data Team and ensure it is correct by cross-checking with the record count confirmed during Pre-load Business Validations |
Accuracy
| Task | Action |
|---|---|
| Spot check | Business should choose some business partners, display the contact person with transaction code "BP" and perform comprehensive check on all loaded details and relationship |
Key Assumptions
- Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
- 3010 is in scope based on data design and any exception requested by business.
See also
Change log
Workflow history
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