Purpose

The purpose of this document is to define the conversion approach to create Customer Hierarchy in S/4 HANA.

There will be different scenarios for the customer hierarchy information in various legacy systems. 

First scenario is in SAP ECC, customer hierarchy is used to save the information. The customer hierarchy is a tree-like hierarchy where each node is a customer (including parent and child customers). The primarily purpose is used for pricing, rebates, and reporting across related customers. It is maintained via transaction code VDH1N.

Second scenario is in Customer General data, the field Group Key is used to assign the Parent company to each customer.  The Group key is defined in PRS system, and synced to SAP ECC (WP2/PF2) in a custom table (ZZRGROUP in PF2. ZWPUT068  in WP2).

Third scenario is in Salesforce, Corporate group is defined as a type of Account in Salesforce, then it is used to link related accounts (such as Prospect) accordingly. The Corporate Group definition is the same as Group Key mentioned in the second scenario. 

In SAP S/4HANA, customers or prospect are managed as Business Partners (BP), enabling a more flexible and integrated data model. In the meantime, Global Hierarchies are used in the the new S4 Hana design. Global hierarchies are any characteristic hierarchies that are maintained centrally in the Fiori app Manage Global Hierarchies. This conversion spec will capture on the migration approach on how to convert the ECC customer hierarchy and Salesforce Corporate group to S4 Hana Global Hierarchies. In the S4 Hana global hierarchy design, the sales area assignment between Business Partners are not required.

The table below summarizes the three scenarios and the S4 solution to be used to manage the customer hierarchy information.

Conversion Scope

The scope of this document covers the approach for converting active Customer Hierarchy from Legacy Source Systems into S/4HANA following the document "DD-FUN-050 Master Data Standard_3005-Customer Hierarchy". 

The Customer hierarchy data from legacy systems includes:

The customer hierarchy data from legacy system excludes:

1. The Corporate Group / Group key is not in migration scope of S4 Hana system
2. The Business Partner General data is not in migration scope for the Customer or Prospect

Additional Information

Multi-language Requirement

N/A

Document Management

N/A

Legal Requirement

N/A

Special Requirements

There will be 3 SAP instances, ROW (Rest of the World), China, CUI. This data object will be replicated to all 3 SAP instances.

Target Design

The technical design of the target for this conversion approach.

Data Cleansing

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
For SAP ECC, the process begins with extracting raw data (KNVH) from source systems, such as Syensqo ECC system (i.e., WP2/PF2) . The extracted data is then staged for transformation. For Salesforce, a flat file will be shared, 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

Extraction

Extract data from a source into Syniti Migrate for SAP S4 Hana Syniti Migrate connects to the source and loads the data into Syniti Migrate. There are 2 methods:

a. Perform full data extraction from relevant tables (KNVH) in the SAP ECC (WP2/PF2).

b. Data is loaded to the repository from the Salesforce system extract/report.

The agreed Relevancy criteria is applied to the extracted records to identify the records that are applicable for the Target loads.

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

The Import template from Fiori app 'Manage Global Hierarchies' will be used.

Load Phase and Dependencies

The Customer Hierarchy 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.

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

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Customer hierarchy 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