Status
Owner	RUAN-ext, Eric CHAN-ext, Beng Chee MADHOK-ext, Jasleen NAIDU-ext, Prasad NG-ext, Bernard RAYUDU-ext, Narasimha Kumar
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 - General in S/4 HANA.

In SAP ECC, customer and vendor master data are maintained separately as distinct entities. Customers are managed through Customer Master Data, while vendors are handled via Vendor Master Data. These records store essential details such as company name, address, payment terms, and tax information.
In SAP S/4HANA, the Business Partner (BP) concept replaces the traditional customer and vendor master data approach. The BP model integrates both customer and vendor roles into a single entity, simplifying data management and ensuring consistency across different business functions

Conversion Scope

The scope of this document covers the approach for converting active Customer Master Data General and Vendor Master General from Legacy Source Systems into S/4HANA Business Partner (BP) General (Role 000000) Master Data Design Standard. 

Customer Master Data - General Information

The data from legacy system includes:

1. Customer with AR Balance under the company codes within S4 Hana implementation scope.
2. or (Customer doesn't have central deletion indicator AND Customer has sales transaction within the sales organizations in scope), e.g., the customer is used as any partner function in the sales document, such as sales order, delivery or billing.
3. or (Customer doesn't have central deletion indicator and customer has financial posting under the company codes within S4 Hana implementation scope)
4. or Customer is part of Customer Hierarchy Higher Node, and the sales area data is part of the sales areas within migration scope
5. or there is customer consignment stock and the plant for the consignment stock is within S4 Hana implementation scope. 
6. or (Customer is created within 2 year and there is not central block) and it has sales view within sales organization in scope
7. or (Customer is created within 2 year and there is not central block) and it has company code view within company code in scope
8. or 

The data from legacy system excludes:

1. Customer has central deletion indicator and without AR/AP Balance under the company codes within S4 Hana implementation scope.
2. Customer is used exclusively by entities not in scope, such as Oil & Gas and Aroma.

Vendor Master Data - General Information

The data from legacy system includes:

The data from legacy system excludes:

1.  

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 Master Data General Information	50000	S4 Hana System	50000
PF2	Customer Master Data General Information	50000	S4 Hana System	50000

Additional Information

Multi-language Requirement

The customer and vendor general data may contain international address. Therefore, the conversion will also need to support the multi-language address.  Below languages (International versions) are supported. 

International Version	Description
C	Simplified Chinese
R	Cyrillic
K	Kanji (Japanese)
A	Arabic
3	Korean
T	Thai
H	Hangul

Document Management

It is possible the customer has attachment in the legacy system. The migration of attachment will be captured in conversion spec CNV-3004 - Attachment for customer master data. 

Legal Requirement

Summarize Legal Requirement/s, if any

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 instance while the entire migration process will remain the same as rest of the world.

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.

Customer Master Data - General Information

ID	Criticality	Error Message/Report Description	Rule	Output	Source System
3007-001		Missing Postal code in the general data			PF2/WP2
3007-002		Missing Street in the general data			PF2/WP2
3007-003		Missing region in the general data			PF2/WP2
3007-004		Review the international version address maintained for the customer			PF2/WP2
3007-005		Review the customer with obsolete region code			PF2/WP2
3007-006		Identify duplicate BP 1. customer vs customer			PF2/WP2
3007-007		Identify duplicate BP 2. vendor vs customer if applicable			PF2/WP2
		Block customer general data without any usage for more than 2 years	For customer without general block and customer is not used in any sales or finance transaction for more than 2 years under company company code

Vendor Master Data - General Information

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_BU_GROUP	BP Grouping Mapping Table
MAP_REGION	Country/Region Code Mapping Table
MAP_BPKIND	BP Type 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	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
Compare Data Counts	Verify row counts between source and target databases. Identify missing or duplicated records.
Validate the mandatory fields	Validate there is value for all the mandatory fields
Validate Primary Keys and Unique Constraints	Check for duplicate or missing primary key values. Ensure unique constraints are maintained.
Test Referential Integrity	Confirm dependent records exist in related tables

Accuracy

Task	Action
Validate the transformation	Validate the fields which require transformation have the value after transformation instead of the original field value
Check Data Consistency	Compare field values across systems Validate data formats and structures

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
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).	Synithi
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	Synithi
4	Load Execution Plan - Establish execution timelines and batch processing schedules. - Assign responsibilities for monitoring execution. - Document dependencies on other migration tasks	Synithi
5	Logging and Reporting - Maintain detailed logs of loading activities. - Generate summary reports on loaded data volume and quality. - Define escalation procedures for errors	Synithi

Load Phase and Dependencies

The Business Partner General will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration. The configuration needs to be transported into the respective system first, including the manual configuration such as the number range set up.

Configuration

List the Configurations required before loading can commence 

Item #	Configuration Item
1	BP Grouping
2	Customer/Vendor Account Group
3	International Version
4	Tax Category
5	BP Type
6	BP Number Range/Customer/Vendor Number range

 Conversion Objects

Object #	Preceding Object Conversion Approach
1083	Bank Master

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
Configuration	The BP grouping is External or Internal Number range	Validate the number range set up if this is External or Internal number range

Post-Load Validation

Project Team

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

Completeness

Task	Action
Display Records	Pick up few random BP numbers, and Run the BP Report to validate the BP general information can be displayed without any error
Perform Source-to-Target Comparisons	Validate that migrated data matches source records. Check for discrepancies in numerical values, text fields, and timestamps

Accuracy

Task	Action
Execute Sample Queries and Reports	Run queries to validate business logic. Generate reports to compare expected vs. actual results
Conduct Post-Migration Reconciliation	Generate reports comparing pre- and post-migration data.

Business

Post-load validation is a critical step in data migration, ensuring that transferred data is accurate, complete, and functional within the target system.

1. Ensuring Data Integrity
After migration, data must be consistent with its original structure. Post-load validation checks for missing records, incorrect mappings, and formatting errors to prevent discrepancies.
2. Business Continuity
Faulty data can disrupt operations, leading to financial losses and inefficiencies. Validating post-load data ensures that applications function as expected, preventing downtime.
3. Error Detection and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections

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
Perform Manual Testing	Conduct manual spot-checks for additional assurance.

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load. 
• BP General (Role 000000) is in scope.

Any additional key assumptions.

Change log

Workflow history