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) 

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.

Data Cleansing

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

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

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

Selection Screen

Data Collection Template (DCT)

DCT Rules

Extraction Dependencies

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

Transformation Rules

Transformation Mapping

Transformation Dependencies

Pre-Load Validation

Project Team

Completeness

Accuracy

Business

Completeness

Accuracy

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

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

Conversion Objects

Error Handling

Post-Load Validation

Project Team

Completeness

Accuracy

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

Accuracy

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

Change log

Workflow history