Status
Owner	RUAN-ext, Eric
Stakeholders	The business stakeholders involved in making, reviewing, and endorsing this decision. Type @ to mention people by name

Purpose

The purpose of this document is to define the conversion approach to create Business Partners - Customer (Sales and Service) - FLCU01 in S/4 HANA.

In SAP ECC, the Customer Sales View is part of the Customer Master Data, which is used to store customer-related information for sales transactions. It includes details such as sales area, pricing, delivery preferences, and billing information. The setup typically involves maintaining customer records separately for different sales organizations, distribution channels, and divisions.
In SAP S/4HANA, the Customer Sales View is integrated into the Business Partner (BP) model, which replaces the traditional customer/vendor objects from ECC. The Business Partner serves as a central entity, allowing a single record to hold multiple roles (e.g., customer and vendor). The Customer Sales View in S/4HANA is represented under the BP role FLCU01, which contains sales-specific data such as sales area assignments, pricing conditions, and delivery preferences

Conversion Scope

The scope of this document covers the approach for converting active <Data Object> from Legacy Source Systems into S/4HANA following the Business Partners - Customer (Sales and Service) - FLCU01 Master Data Design Standard.

The data from legacy system includes:

1. The BP general is migrated
2. The sales area under which the sales view data is maintained for the customer is within the scope of S4 Hana 
3. There is no Central Sales Block (KNA1-CASSD) in the customer general master data, which is used to block all the sales view data of the customer.
4. or there is no Deletion flag for customer (sales level) (KNVV-LOEVM)
5. or there is no Customer order block (sales area) (KNVV-AUFSD)

The data from legacy system excludes:

1. The sales org for the sales view is out of scope, such as Oil & Gas and Aroma specific sales organizations.
2. Exclusion Criteria 2
3. Exclusion Criteria n

List of source systems and approximate number of records

Source	Scope	Source Approx No. of Records	Target System	Target Approx No. of Records
WP2	Customer Sales View	80000	S4	80000
PF2	Customer Sales View	40000	S4	40000

Additional Information

Multi-language Requirement

N/A

Document Management

N/A

Legal Requirement

N/A

Special Requirements

Due to compliance requirement, there will be one SAP instance for Rest of the World and one for China specifically. For entities in China, the data will be loaded into SAP China instance while the entire migration process will remain the same as rest of the world.

To identify the record is for SAP China Instance, it will use below logic. 

Customer Master Data - General Information

1. The customer has sales area data in below entities. 
2. If the customer is used in both China entities and ROW, then the sales data needs to be created in both SAP China and ROW instances.

SAP China Instance specific Sales Organization

If the data conversion involves third-party systems or external data sources, such as Icertis, describe any additional requirements related to data mapping, transformation logic, validation rules or security measures that must be followed.

Target Design

With Functional input, document the technical design of the target fields that are in the scope of this document.

The technical design of the target for this conversion approach.

Table	Field	Data Element	Field Description	Data Type	Length	Requirement
MARA	MATNR	MATNR	Material Number	CHAR	18	Mandatory

Data Cleansing

All data cleansing should take place in the data source system as defined in this document, unless system limitations prevent it.

If data cleansing is managed outside of the source system (e.g. Syniti Migrate, 3rd Party Vendor, DCT), the necessary documentation must be produced and appended to this deliverable for sign-off.

ID	Criticality	Error Message/Report Description	Rule	Output	Source System

Conversion Process

The high-level process is represented by the diagram below:

The ETL (Extract, Transform, Load) process is a structured approach to data migration and management, ensuring high-quality data is seamlessly transferred across systems. Here’s a breakdown of its key components:
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

N/A

Extraction

Extract data from a source into Syniti Migrate. There are 2 possibilities:

1. The data exists. Syniti Migrate connects to the source and loads the data into Syniti Migrate. There are 3 methods:
   1. Perform full data extraction from relevant tables in the source system(s).
   2. Perform extraction through the application layer.
   3. Only if Syniti Migrate; cannot connect to the source, data is loaded to the repository from the provided source system extract/report.
2. 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
Extraction Scope Definition	- Identify the source systems and databases involved. - Define the data objects (tables, fields, records) to be extracted. - Establish business rules for data selection.	Synithi
Extraction Methodology	- Specify the extraction approach (full, incremental, or delta extraction). - Determine the tools and technologies used. - Define data filtering criteria to exclude irrelevant records.	Synithi
Extraction Execution Plan	- Establish execution timelines and batch processing schedules. - Assign responsibilities for extraction monitoring. - Document dependencies on other migration tasks.	Synithi
Data Quality and Validation	- Define error handling mechanisms for extraction failures.	Synithi

Selection Screen

If applicable, this section will give the details on any selection screen parameters, including the parameter type, that are required to be entered to ensure consistent data extracts.

Selection Ref Screen	Parameter Name	Selection Type	Requirement	Value to be entered/set
N/A

Data Collection Template (DCT)

Target Ready Data Collection Template will be created for Data Object data with exception of some fields which require transformation as mentioned in the transformation rule.

<Object> DCT Rules

Field Name	Field Description	Rule

Extraction Dependencies

List the steps that need to occur before extraction can commence

Item #	Step Description	Team Responsible
1	Source System Availability Ensure that the source database or application is accessible. Confirm that necessary credentials and permissions are granted	Syensqo IT
2	Data Structure Identify relationships between tables, views, and stored procedures.	Synithi
3	Referential Integrity Ensure dependent records are extracted together.	Synithi
4	Extraction Methodology Define whether extraction is full, incremental, or delta-based. Establish batch processing schedules for large datasets.	Synithi
5	Performance and Scalability Considerations Optimize extraction queries to prevent system overload. Ensure network bandwidth supports data transfer volumes.	Synithi
6	Security and Compliance Adhere to regulatory standards for sensitive information if applicable	Synithi

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:

1. Perform value mapping and data transformation rules.
   1. Legacy values are mapped to the to-be values (this could include a default value)
   2. Values are transformed according to the rules defined in Syniti Migrate
2. 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
2	Data Mapping and Standardization - Align source fields with target fields. - Ensure unit consistency (e.g., currency, measurement units)	Data Team
3	Business Rule Application - Implement data enrichment/collection if applicable - Apply conditional transformations based on predefined logic/business rules	Data Team
4	Transformation Execution Plan - Define batch processing schedules. - Assign responsibilities for monitoring execution. - Establish error-handling mechanisms	Synithi

Transformation Rules

Rule #	Source system	Source Table	Source Field	Source Description	Target System	Target Table	Target Field	Target Description	Transformation Logic

Transformation Mapping

Use the exact name and reference this section in the “Transformation rules” above

Mapping Table Name	Mapping Table Description
MAP_VKORG	Sales Organization Mapping Table
MAP_VTWEG	Distribution Channel Mapping Table
MAP_SPART	Division Mapping table
MAP_ZTERM	Payment terms Mapping table
MAP_PARVW	Partner Function Mapping table

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, such as CNV-3007 Business Partner General	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
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Business

The following pre-load validations will be performed by the business.

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Load

The load process includes:

1. Execute the automated data load into target system using load tool or product the load file if the load must be done manually
2. 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

Load Phase and Dependencies

Identify the phase as to “when” the load for this object will occur. <Pre-Cutover, Cutover, Post Cutover> and list the steps that need to occur before the load can commence

Configuration

List the Configurations required before loading can commence

Item #	Configuration Item

Conversion Objects

Object #	Preceding Object Conversion Approach
3007	Business Partner General (Role 000000)
	Employee Personal Information
3011	Business Partners - Contact Persons (BUP001)

Error Handling

The table below depicts some possible system errors for this data object during data load. All data load error is to be logged as defect and managed within the Defect Management

Error Type	Error Description	Action Taken
Configuration / Data Transformation	The value XXX for field XXX doesn't exist	Check the mapping/conversion is done properly in the loading file Validate the target value is configured/transported in the target system Reach out to function team to validate the configuration
Configuration	There is mandatory field XXX missing	Validate MDS if the fields are set as mandatory Validate if there is value in the pre-loading file Validate if the configuration for the mandatory fields are done properly

Post-Load Validation

Project Team

The following post-load validations will be performed by the Project Team.

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Business

The following post-load validations will be performed by the business.

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Data Object is in scope based on data design and any exception requested by business.

Any additional key assumptions.

See also

Insert links and references to other documents which are relevant when trying to understand this decision and its implications. Other decisions are often impacted, so it's good to list them here with links. Attachments are also possible but dangerous as they are static documents and not updated by their authors.

Change log

Workflow history