Status
Owner	LIU-ext, Ekawati
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 data conversion approach to create Cross Selling records in S/4 HANA.

Cross selling is a sales technique where a customer is offered related or complementary products to what they have already purchased or are purchasing. The system can automatically suggest these items during sales order creation, potentially increasing sales volume and customer satisfaction. 

The structure and usage of Cross Selling in S/4HANA remain consistent as the records in ECC are maintained at sales organization level.

This conversion aims to migrate active and relevant cross selling records from existing ECC systems into S/4HANA by applying required transformation logic using Syniti as the data migration and transformation platform. The converted records will be loaded into the target S/4HANA system using standard SAP mechanisms such as IDOCs, BAPIs, or direct table loads where applicable.

Conversion Scope

The scope of this document covers the approach for converting active Cross Selling from Legacy Source Systems into S/4HANA following the Cross Selling Master Data Design Standard: DD-FUN-050 Master Data Standard_1037-Cross selling. 

From the current system landscape, Cross Selling records only exist in the legacy systems - WP2. Harmonization and validation are required to ensure accurate and consolidated data in S/4HANA. While WP2 serve as source systems, a mapping and transformation logic will be necessary to produce properly formatted load templates in line with the target design.

The data from legacy system includes: 

1. All active Cross Selling, where "valid to date is beyond S/4HANA Go-Live date" AND each of the below as per table combination:
   1. Sales organization in scope of S/4HANA
   2. Material sales view migrated as per CNV-2003 Materials - Sales view with sales long text.

The data from legacy system excludes:

1. Invalid Cross Selling, e.g. table KOTD904 (sales organization/ distribution channel/ customer) and KOTD905 (sales organization/ distribution channel) 

List of source systems and approximate number of records

Source	Scope	Source Approx No. of Records	Target System	Target Approx No. of Records
WP2	Cross Selling table: KOTD906	150	S/4HANA ROW	150
WP2	Cross Selling table: KOTD906	75	S/4HANA China	75
WP2	Cross Selling table: KOTD906	150	S/4HANA CUI	150
PF2	Cross Selling	N/A	S/4HANA ROW/ China/ CUI	N/A

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 (ROW), one for China and one for CUI. Based on the sales organization the cross selling records will be migrated to respective SAP instances.

Target Design

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.

ID	Criticality	Error Message/Report Description	Rule	Output	Source System
1037-001		Change the 'Validity Period - TO' to before SyWay go live date.	For main material, if it is in-active/ obsolete.		PF2/ WP2
1037-002		Delete the alternative material.	For alternative materials, if it is in-active/ obsolete.		PF2/ WP2

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. 

For CUI instance, the ETL process will be similar, but it will not use Syniti tool.

Data Privacy and Sensitivity

For SAP CUI instances, the data will be processed by US Based consultants.

Extraction

Extract data from a source into Syniti Migrate for S/4HANA ROW and S/4HANA China relevant entities. 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

For SAP CUI related entities, it will be alternative extraction process and the data will be stored in approved tools.

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.	Syniti / US Based Consultant for SAP CUI instance Syniti / LTC Data team
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.	Syniti / US Based Consultant for SAP CUI instance
Extraction Execution Plan	- Establish execution timelines and batch processing schedules. - Assign responsibilities for extraction monitoring. - Document dependencies on other migration tasks.	Syniti / US Based Consultant for SAP CUI instance
Data Quality and Validation	- Define error handling mechanisms for extraction failures.	Syniti / US Based Consultant for SAP CUI instance

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 Cross Selling 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.	Syniti / US Based Consultant for SAP CUI instance
3	Referential Integrity Ensure dependent records are extracted together.	Syniti / US Based Consultant for SAP CUI instance
4	Extraction Methodology Define whether extraction is full, incremental, or delta-based. Establish batch processing schedules for large datasets.	Syniti / US Based Consultant for SAP CUI instance
5	Performance and Scalability Considerations Optimize extraction queries to prevent system overload. Ensure network bandwidth supports data transfer volumes.	Syniti / US Based Consultant for SAP CUI instance
6	Security and Compliance Adhere to regulatory standards for sensitive information if applicable	Syniti / US Based Consultant for SAP CUI instance
7	Data cleansing of legacy Cross Selling data must be completed. If standardization within the DCT begins using relevant data from PF2 and WP2 before the cleansing is finalized, it is understood that the business will take due diligence to ensure any subsequent delta cleansing is verified and aligned within the DCT.	Business

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	Syniti / US Based Consultant for SAP CUI instance

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 SyWay - Sales Area.pptx --> All the actual distribution channels and divisions won't exist in the to-be solution Check current Dist.Ch. and discuss with Functional team and how to do the mapping
MAP_KUNNR	Customer Mapping Table
MAP_MATNR	Material 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.	Syniti / US Based Consultant for SAP CUI instance
2	Referential Integrity - Ensure dependent records are transformed together or in advance	Syniti / US Based Consultant for SAP CUI instance
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	Syniti / US Based Consultant for SAP CUI instance
5	Logging and Error Handling - Maintain detailed logs of transformation activities. - Define error-handling procedures for failed transformations	Syniti / US Based Consultant for SAP CUI instance

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, i.e., if there is same BP number. 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
Compare Data Counts	Verify row counts between source and target databases. Identify missing or duplicated records.
Review populated templates for missing or incorrect values	Use checklists to verify completeness and correctness before submission

Accuracy

Task	Action
Conversion Accuracy	Business Data Owner/s to verify that all the data in the load table/file is accurate as per endorsed transformation/ mapping rules (and signed-off DCT data).

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).	Syniti / US Based Consultant for SAP CUI instance
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 / US Based Consultant for SAP CUI instance
4	Load Execution Plan - Establish execution timelines and batch processing schedules. - Assign responsibilities for monitoring execution. - Document dependencies on other migration tasks	Syniti / US Based Consultant for SAP CUI instance
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 / US Based Consultant for SAP CUI instance

Load Phase and Dependencies

The Cross Selling will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration and data objects in the S/4HANA. The configuration needs to be transported into the respective system first.

Configuration

Item #	Configuration Item
1	Sales Organization
2	Distribution Channel
3	Cross Selling Condition Table
4	Cross Selling Access Sequence
5	Cross Selling Condition Type
6	Cross Selling Procedure
7	Maintain Customer/ Document Procedures for Cross Selling
8	Define Cross Selling Profile
9	Assign Cross Selling Profile

Conversion Objects

Object #	Preceding Object Conversion Approach
3003	Business Partners - Customer (Sales and Service) - FLCU01
2003	Materials - Sales view with sales long text

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
Validate Record count in the backend	Validate all tables has the same records as the loading file
Display Records	Pick up a few random Sales Organization and Customer, and run t-code: VD43 to validate the Cross Selling 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
Perform Source-to-Target Comparisons	Validate that migrated data matches source records. Check for discrepancies in numerical values, text fields, and timestamps
Conduct Post-Migration Reconciliation	Go through reports comparing pre- and post-migration data.

Accuracy

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

Key Assumptions

• Cross Selling Master Data Standard is up to date as on the date of documenting this conversion approach and data load. 
• Cross Selling is in scope based on data design and any exception requested by business.
• There will be 3 SAP instances, one for ROW, one for China and one for CUI only.
• For SAP CUI instance, the migration activity will be handled by US based data consultant. 

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