Status
Owner	The person responsible for driving this decision and documenting it. Type @ to mention people by name
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 Revision in S/4 HANA.

Depending on how Revision is used in S4, what Revision Types are configured

Conversion Scope

The scope of this document covers the approach for converting active Revision from Legacy Source Systems into Maintenance Planning Bucket in S/4HANA.

The data from legacy system includes:

1. All valid legacy Revision 

The data from legacy system excludes:

1. Closed Revision
2. Revision with Dates (Start? End?) in the past
3. Any other criteria?

List of source systems and approximate number of records

Source	Scope	Source Approx No. of Records	Target System	Target Approx No. of Records
PF2, WP2	Valid Revisions	3000	S/4HANA	3000
DCT	Revisions for plants which do not have data existing from PF2 and PF2	nnn	S/4HANA	nnn

Additional Information

Multi-language Requirement

Revision does not have multi language support. Revision text will be migrated using EN logon.

Document Management

N/A

Legal Requirement

N/A

Special Requirements

Specify any special requirements or considerations that may impact the data conversion process based on specific locations, regulatory compliance or system limitations. Clearly outline any regional or localization requirements such as country-specific data formats, legal reporting obligations or industry standards that must be adhered to (e.g., localization rules for countries like China).

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
9003-001	C1	Invalid, Inactive or no Inactive or no Cost Centres linked to Revision	Revision as per Relevancy Criteria assigned with a Cost Centre which does not belong below: 1. Cost Centre (FI) as per Relevancy Criteria	All key fields	PF2, WP2
9003-002	C1	Invalid, Inactive or no WBS linked to Revision	Revision as per Relevancy Criteria assigned with a WBS which does not belong below: 1. Work Breakdown Structure (PS) as per Relevancy Criteria	All key fields	PF2, WP2
9003-003	C1	Invalid, Inactive or no Inactive or no Work Centres linked to Revision	Revision as per Relevancy Criteria assigned with a Work Centre which does not belong below: 1. Work Centre as per Relevancy Criteria	All key fields	PF2, WP2

Conversion Process

The high-level process is represented by the diagrams below.

The following represents the high-level process for Source System Extraction:

The following represents the high-level process for DCT:

Data Privacy and Sensitivity

Summarize Data Privacy and Sensitivity Requirements, if any

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
1	Extract data from source system based on relevancy rule	Data Team
2	Google Sheet report pre-populated with PF2 and WP2 information to be generated based on relevancy criteria.	Data Team

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

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

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

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

Transformation Dependencies

List the steps that need to occur before transformation can commence

Item #	Step Description	Team Responsible

Pre-Load Validation

Project Team

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

Completeness

Task	Action
Verify Record Count	Data team to verify that the total number of relevant records from the source systems is equal to the total number of records in the Preload and Load Sheets.

Accuracy

Task	Action
Conversion Accuracy	Data team to verify that all fields below meet pass the checks: 1. Mandatory Fields 2. Field and Value Mapping Correctness 3. Null Checks 4. Text Length Checks
Review error reports	Review and correct the errors.  Achieve a zero-error record count as much as possible. Raise defects for data remediated and requiring a correction in the source data.

Business

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

Completeness

Task	Action
Verify Record Count	Business team to verify that the total number of relevant records from the source systems is equal to the total number of records in the Preload and Load Sheets.

Accuracy

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

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
N/A

Conversion Objects

Object #	Preceding Object Conversion Approach
	list the exact title of the conversion object of only the immediate predecessor – this will then confirm the DDD (Data Dependency Diagram)

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

Post-Load Validation

Project Team

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

Completeness

Task	Action
Verify Count	Data team to verify the record count created in target S/4 HANA by accessing post load reports in dspMigrate or standard reports from S/4 HANA.
Verify Logs	Check if there is data that failed to load and perform the necessary actions (e.g. register as post load issue, or attempt to load the record again, etc.).

Accuracy

Task	Action
Conversion Accuracy	Data team to verify that the Measuring Point data in target S/4 HANA were loaded correctly via dspMigrate post load reports or standard reports from S/4 HANA.

Business

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

Completeness

Task	Action
Verify Count	Download Post Load Reports from dspMigrate and verify that the record count loaded in the target S/4 HANA is the same count as of the endorsed load file.

Accuracy

Task	Action
Conversion Accuracy	Verify that the Measuring Point data in target S/4 HANA were loaded correctly via dspMigrate post load reports or standard reports from S/4 HANA.

Key Assumptions

• Revision 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