Status
Owner	REDDY-ext, Naren
Stakeholders	NICASTRI-ext, Michele manoj.kumar-ext@syensqo.com

Purpose

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

Sampling Procedures are master data in SAP Quality Management (QM) that determine how the inspection scope is defined, such as the number of units to be inspected from a lot or the percentage of the lot to be checked. They provide standardized rules for sample determination and ensure consistency across inspection lots, inspection plans, and inspection characteristics. Sampling procedures can be based on fixed sample sizes, percentage samples, or inspection severity levels defined by sampling schemes.

In SAP S/4HANA, the structure and usage of sampling procedures remain consistent with SAP ECC. Sampling procedures are typically defined at the plant level, with key attributes such as sampling type, sample size, code group assignment, validity dates, and indicator settings. They can be assigned to master inspection characteristics (MICs) or directly within inspection plans, ensuring harmonized inspection strategies across materials and processes.

In SAP ECC, aside from the standard structure of sampling procedure master data (procedure ID, plant, type, and parameters), there may be additional variants, such as procedures linked to specific inspection severity levels, schemes that determine dynamic modification rules, or customized procedures with client-specific enhancements. Some legacy systems may also include obsolete or unused sampling procedures, which will require cleansing and validation before migration (pending MDS).

This conversion aims to migrate active and relevant sampling procedure records from existing ECC systems into S/4HANA by applying the 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 BAPIs (e.g., BAPI_INSPSAMPLINGPROCEDURE_CREATE), IDOCs, or direct table loads where applicable, ensuring data accuracy, compliance, and usability in the target system.

Conversion Scope

The scope of this document covers the approach for converting active Sampling Procedure from Legacy Source Systems into S/4HANA following the Sampling Procedure Master Data Design Standard.

The data from legacy system includes:

1. Active Sampling Procedures that have been used in inspection plans or inspection lots in the last four (4) years. 
   a. Inspection plan
       i. QDSV-KZVWSVPL ='X'(Used in Inspection plan/Task list)
      ii. PLMK-STICHPRVER = QDSV-STICHPRVER, Refer Inspection plan relevancy for active inspection plans
   b. Inspection Lots(QALS-ERSTELDAT >= CURRENT DATE -4, QALS-STICHPRVER = QDSV-STICHPRVER)
      
2. Sampling procedure referenced in Material master QM view. Relevancy rules for Material master QM view are applicable. Refer Material master QM view for active Material master QM data.
   
   
3. Sampling Procedures with valid sampling type, such as:
   • Fixed sample size,
   • 100% inspection,
   • Percentage-based sampling,
   • Sampling schemes (AQL, inspection severity levels) etc.

The data from legacy system excludes:

1. Inactive Sampling Procedures not used in inspection plans or inspection lots for more than four (4) years.
2. Sampling procedure not referenced in Material master QM view. Relevancy rules for Material master QM view are applicable.
3. Sampling Procedures with invalid sampling type, not in below:
   • Fixed sample size,
   • 100% inspection,
   • Percentage-based sampling,
   • Sampling schemes (AQL, inspection severity levels) etc.

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	Sampling Procedure data will be extracted from client PF2 and WP2	PF2 = 45 records WP2 = 535 records	S/4 HANA	580

Additional Information

Multi-language Requirement

Sampling Procedure description will be maintained in English by default.

Since multi-language support is available for Sampling Procedure, users logging in with a different language will see the description displayed in their logon language, provided that the corresponding language key has been maintained in the Sampling Procedure.

Document Management

N/A

Legal Requirement

N/A

Special Requirements

N/A

Target Design

The technical design of the target for this conversion approach.

Table	Field	Data Element	Field Description	Data Type	Length	Requirement
QDSV	STICHPRVER	QSTPRVER	Sampling Procedure	CHAR	8	R
QDSV	STICHPRART	QSTPRART	Sampling Type	CHAR	3	R
QDSV	BEWERTMOD	QBEWMOD	Valuation Mode	CHAR	3	R
QDSV	KZOHI	QKZOHI	No Stage Change	CHAR	1	NU
QDSV	KZUMFS	QKZUMFS	Multiple Samples	CHAR	1	NU
QDSV	KZNOCUT	QKZNOCUT	Recurring inspections	CHAR	1	NU
QDSV	STPRANZ	QSTPRANZ	No. of samples	INT1	3	NU
QDSV	STPRUMF	QSTPRUMF	Sample size	INT4	10	C
QDSV	ANNAHMEZ	QANNAHMEZ	Acceptance no.	INT2	5	NU
QDSV	KFAKTOR	QKFAKTOR	K-factor	FLTP	16	NU
QDSV	KFAKTORNI	QNINITIAL	Not Initial	CHAR	1	NU
QDSV	KZNVWSV	QKZNVWSV	Usage Blocked	CHAR	1	NU
QDSV	KZVWSVPL	QKZVWSVPL	In Task List	CHAR	1	S
QDSV	FBKEY	QFBKEY	Determination Rule	CHAR	2	C
QDSV	FBKEYMFS	QFBKEYMFS	Valuation Rule	CHAR	2	C
QDSV	STPRPLAN	QSTPRPLANV	Sampling Scheme	CHAR	3	NU
QDSV	PRSCHAERFE	QPRSCHAERV	Inspection severity	NUMC	3	NU
QDSV	AQLWERT	QAQLWERTV	AQL Value	DEC	7	NU
QDSV	PROZUMF	QPROZUMF	Size as lot %	FLTP	16	C
QDSV	PROZUMFNI	QNINITIAL	Not Initial	CHAR	1	C
QDSV	PROZAZL	QPROZAZL	AccNo. as %	FLTP	16	NU
QDSV	PROZAZLNI	QNINITIAL	Not Initial	CHAR	1	NU
QDSV	ERSTELLER	QERSTELLER	Created By	CHAR	12	S
QDSV	AENDERER	QAENDERER	Changed By	CHAR	12	S
QDSV	ERSTELLDAT	QDATUMERST	Created On	DATS	8	S
QDSV	AENDERDAT	QDATUMAEND	Changed On	DATS	8	S
QDSV	KZRAST	QKZRAST	With inspection points	CHAR	1	NU
QDSV	RASTER	QRASTER	Inspection Frequency	NUMC	3	NU
QDSV	QRKART	QQRKART	Ctrl Chart Type	CHAR	3	NU
QDSV	DUMMY_QDSV_INCL_EEW_PS	DUMMY	Dummy function in length 1	CHAR	1	NU
QDSVT	STICHPRVER	QSTPRVER	Sampling Procedure	CHAR	8	R
QDSVT	SPRACHE	SPRAS	Language Key	LANG	1	R
QDSVT	KURZTEXT	QKURZTEXT	Short Text	CHAR	40	R

Data Cleansing

ID	Criticality	Error Message/Report Description	Rule	Output	Source System
1064-001	C1	Sampling Procedure not used in last 4 years	Sampling Procedures (QDSV) not referenced in any Inspection Plan (PLMK-STICHPRVER) or Inspection Lot for ≥ 4 years will not be migrated.	Active Sampling Procedures used in last 4 years	PF2/WP2
1064-002	C1	Sampling Procedure blocked for usage	Procedures with "blocked for usage" indicator (QDSV-KZNVWSV = X), but referred in active Inspection plans or Inspection lots or Material master Inspection setup.	Sampling Procedures with blocked for usage Flag	PF2/WP2
1064-003	C1	Invalid Sampling Type	STICHPRART (type: fixed %, 100%, scheme) not configured or not valid in target system.	Sampling Procedures with valid type	PF2/WP2
1064-004	C1	Missing Sample Size / %	Sampling Procedures missing values for sample size, percentage, sampling type or valuation mode will not be migrated.	Procedures with complete sample definition	PF2/WP2
1064-005	C2	Duplicate Sampling Procedures	Sampling procedures with similar values. Use the below combination to identify. STICHPRART,BEWERTMOD,STPRUMF,FBKEY,FBKEYMFS,PROZUMF Based on the outcome business will suggest for the dedup logic to pick the right one.	Unique Sampling Procedure	PF2/WP2
1064-006	C2	Missing short text/Language is not configured in Target system	QDSVT-KURZTEXT missing or one of the values in QDSVT-SPRACHE is not configured in Target system	Sampling Procedures with multilingual texts	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 S/4HANA system. 

Data Privacy and Sensitivity

Not applicable

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.	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
Extraction Execution Plan	- Establish execution timelines and batch processing schedules. - Assign responsibilities for extraction monitoring. - Document dependencies on other migration tasks.	Syniti
Data Quality and Validation	- Define error handling mechanisms for extraction failures.	Syniti

Selection Screen

Selection Ref Screen	Parameter Name	Selection Type	Requirement	Value to be entered/set
Not applicable

Data Collection Template (DCT)

A Target-Ready Data Collection Template will be created for all required fields in the QM Sampling Procedure, except for fields that require transformation in accordance with the defined transformation rules. Each template will follow the structure and format required by the target S/4HANA Material Master configuration for Quality Management.

Table	Field Name	Field Description	Data Type	Length	Requirement	Rule	Mapping
QDSV	STICHPRVER	Sampling Procedure	CHAR	8	Required	Sampling procedure	Copy from DCT
QDSV	STICHPRART	Sampling Type	CHAR	3	Required	The sampling type in sampling procedure determines how sample size is calculated with the values typically includes: 100        Fixed sample 200        100% inspection 400        Percentage sample"	Copy from DCT
QDSV	BEWERTMOD	Valuation Mode	CHAR	3	Required	Valuation mode determines the rule for accepting or rejecting a characteristics. The valuation mode must be selected to match the inspection type - qualitative and quantitative MICs 400        Valuation according to char.attrib.code 500        Manual valuation 700        Mean value within tolerance range"	Copy from DCT
QDSV	STPRUMF	Sample size	INT4	10	Conditional	Sample size is to be defined for fixed and 100% sampling type	Copy from DCT
QDSV	FBKEYMFS	Valuation Rule	CHAR	2	Conditional	The valuation rule must always be assigned and be compatible with the sampling type and valuation mode to ensure valid and consistent inspection results. This rule prevents saving the sampling procedure if the valuation rule is missing or incompatible, thereby enforcing compliance with defined quality management standards. 10        Fixed sample 20        100% inspection 40        Percentage sample A1        Manual valuation"	Copy from DCT
QDSV	PROZUMF	Size as lot %	FLTP	16	Conditional	Sample size as lot % is to be defined only if sampling type is percentage sample	Copy from DCT
QDSVT	STICHPRVER	Sampling Procedure	CHAR	8	Required	Sampling procedure	Copy from DCT
QDSVT	SPRACHE	Language Key	LANG	1	Required	Language key of short text	Copy from DCT
							Copy from DCT
QDSVT	KURZTEXT	Short Text	CHAR	40	Required	Short text of sampling procedure	Copy from DCT
	Note	Please check the link attached for Layout:

Extraction Dependencies

Before data extraction can commence, several prerequisite steps and conditions must be met to ensure a smooth and accurate extraction process. These dependencies involve confirming system readiness, validating data structures, and ensuring that appropriate access rights and credentials are in place.

Each step must be clearly defined, assigned to responsible teams, and completed prior to extraction activities. Proper coordination across stakeholders is required to mitigate risks and avoid delays in the migration timeline.

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
3	Referential Integrity Ensure dependent records are extracted together.	Syniti
4	Extraction Methodology Define whether extraction is full, incremental, or delta-based. Establish batch processing schedules for large datasets.	Syniti
5	Performance and Scalability Considerations Optimize extraction queries to prevent system overload. Ensure network bandwidth supports data transfer volumes.	Syniti
6	Security and Compliance Adhere to regulatory standards for sensitive information if applicable	Syniti

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

Transformation Rules

Rule #	Source system	Source Table	Source Field	Source Description	Target System	Target Table	Target Field	Target Description	Transformation Logic
1	PF2/WP2	QDSV	STICHPRVER	Sampling Procedure	S/4 HANA	QDSV	STICHPRVER	Sampling Procedure	R.Copy from Source system
2	PF2/WP2	QDSV	STICHPRART	Sampling Type	S/4 HANA	QDSV	STICHPRART	Sampling Type	R.Copy from Source system
3	PF2/WP2	QDSV	BEWERTMOD	Valuation Mode	S/4 HANA	QDSV	BEWERTMOD	Valuation Mode	R.Copy from Source system
4	PF2/WP2	QDSV	KZOHI	No Stage Change	S/4 HANA	QDSV	KZOHI	No Stage Change	Not used
5	PF2/WP2	QDSV	KZUMFS	Multiple Samples	S/4 HANA	QDSV	KZUMFS	Multiple Samples	Not used
6	PF2/WP2	QDSV	KZNOCUT	Recurring inspections	S/4 HANA	QDSV	KZNOCUT	Recurring inspections	Not used
7	PF2/WP2	QDSV	STPRANZ	No. of samples	S/4 HANA	QDSV	STPRANZ	No. of samples	Not used
8	PF2/WP2	QDSV	STPRUMF	Sample size	S/4 HANA	QDSV	STPRUMF	Sample size	C.Copy from source system
9	PF2/WP2	QDSV	ANNAHMEZ	Acceptance no.	S/4 HANA	QDSV	ANNAHMEZ	Acceptance no.	Not used
10	PF2/WP2	QDSV	KFAKTOR	K-factor	S/4 HANA	QDSV	KFAKTOR	K-factor	Not used
11	PF2/WP2	QDSV	KFAKTORNI	Not Initial	S/4 HANA	QDSV	KFAKTORNI	Not Initial	Not used
12	PF2/WP2	QDSV	KZNVWSV	Usage Blocked	S/4 HANA	QDSV	KZNVWSV	Usage Blocked	Not used
13	PF2/WP2	QDSV	KZVWSVPL	In Task List	S/4 HANA	QDSV	KZVWSVPL	In Task List	S.Internal
14	PF2/WP2	QDSV	FBKEY	Determination Rule	S/4 HANA	QDSV	FBKEY	Determination Rule	C.Copy from source system
15	PF2/WP2	QDSV	FBKEYMFS	Valuation Rule	S/4 HANA	QDSV	FBKEYMFS	Valuation Rule	C.Copy from source system
16	PF2/WP2	QDSV	STPRPLAN	Sampling Scheme	S/4 HANA	QDSV	STPRPLAN	Sampling Scheme	Not used
17	PF2/WP2	QDSV	PRSCHAERFE	Inspection severity	S/4 HANA	QDSV	PRSCHAERFE	Inspection severity	Not used
18	PF2/WP2	QDSV	AQLWERT	AQL Value	S/4 HANA	QDSV	AQLWERT	AQL Value	Not used
19	PF2/WP2	QDSV	PROZUMF	Size as lot %	S/4 HANA	QDSV	PROZUMF	Size as lot %	C.Copy from source system
20	PF2/WP2	QDSV	PROZUMFNI	Not Initial	S/4 HANA	QDSV	PROZUMFNI	Not Initial	S.Copy from source system
21	PF2/WP2	QDSV	PROZAZL	AccNo. as %	S/4 HANA	QDSV	PROZAZL	AccNo. as %	Not used
22	PF2/WP2	QDSV	PROZAZLNI	Not Initial	S/4 HANA	QDSV	PROZAZLNI	Not Initial	Not used
23	PF2/WP2	QDSV	ERSTELLER	Created By	S/4 HANA	QDSV	ERSTELLER	Created By	S.Internal
24	PF2/WP2	QDSV	AENDERER	Changed By	S/4 HANA	QDSV	AENDERER	Changed By	S.Internal
25	PF2/WP2	QDSV	ERSTELLDAT	Created On	S/4 HANA	QDSV	ERSTELLDAT	Created On	S.Internal
26	PF2/WP2	QDSV	AENDERDAT	Changed On	S/4 HANA	QDSV	AENDERDAT	Changed On	S.Internal
27	PF2/WP2	QDSV	KZRAST	With inspection points	S/4 HANA	QDSV	KZRAST	With inspection points	Not used
28	PF2/WP2	QDSV	RASTER	Inspection Frequency	S/4 HANA	QDSV	RASTER	Inspection Frequency	Not used
29	PF2/WP2	QDSV	QRKART	Ctrl Chart Type	S/4 HANA	QDSV	QRKART	Ctrl Chart Type	Not used
30	PF2/WP2	QDSV	DUMMY_QDSV_INCL_EEW_PS	Dummy function in length 1	S/4 HANA	QDSV	DUMMY_QDSV_INCL_EEW_PS	Dummy function in length 1	Not used
31	PF2/WP2	QDSVT	STICHPRVER	Sampling Procedure	S/4 HANA	QDSVT	STICHPRVER	Sampling Procedure	R.Copy from Source system
32	PF2/WP2	QDSVT	SPRACHE	Language Key	S/4 HANA	QDSVT	SPRACHE	Language Key	R.Copy from Source system
33	PF2/WP2	QDSVT	KURZTEXT	Short Text	S/4 HANA	QDSVT	KURZTEXT	Short Text	R.Copy from Source system

Transformation Mapping

Mapping Table Name	Mapping Table Description
Not Applicable

Transformation Dependencies

List the steps that need to occur before transformation can commence

Item #	Step Description	Team Responsible
1	Value Mappings are according to the latest design - <List of Value Mappings>	SyWay Data Team

Pre-Load Validation

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

Completeness

Task	Action
Compare Data Count	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
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
4	Load Execution Plan - Establish execution timelines and batch processing schedules. - Assign responsibilities for monitoring execution. - Document dependencies on other migration tasks	Syniti
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

Load Phase and Dependencies

The Sampling procedure will be loaded in the pre-cutover (PreCutover 4 phase) period.

Before loading, it will have dependency on the following configuration and data objects in the S/4 HANA.

Configuration

Item #	Configuration Item
1	QDBM - Valuation mode: Define how inspection results are interpreted
2	QDFB - Function modules for the individual procedure categories: Defines how sample sizes are calculated based on the procedure type
3	QDFM - Function modules for valuation mode: Enables custom logic for sample size calculation or valuation
4	QDEP - Allowed inspection severities: Defines how long a stage lasts, how many lots are needed to move to the next stage, and what triggers a reset.
5	QPSH - Control chart types: Used for reporting and compliance to show how inspection scope evolved over time
6	QDSA - Sampling type: Ensures the correct sampling procedure is applied as inspection intensity changes.

Conversion Objects

Object #	Preceding Object Conversion Approach
CNV-2009	Material master along with QM view

Error Handling

Error Type	Error Description	Action Taken
1	Material number exists and extended to required Plant and QM view	Verify that the Material exists in the target system and mapping is correctly maintained. Reprocess once mapping is updated.

Post-Load Validation

Project Team

Completeness

Task	Action
Validate Record count in the backend	Validate all tables with prefix “QDSV” has the same records as the loading file
Display Records	Pick up a few random Sampling procedures, and run t-code: QDV3 to validate the Sampling procedures and 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
Validation reports

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

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Sampling procedure is in scope based on data design and any exception requested by business.
• Data cleansing has met the required percentage threshold for the specified mock cycle and all preparation activities have been completed.
• Data entries in DCT are target-ready data unless a specific transformation rule is stated for that field in the transformation rules.

See also

CNV-1041 QM Inspection Plan

CNV-2009 Material master QM view

Change log