SAP How-To-Guide: How to enable rule mining for master data including classification data
This document describes how you could enable the rule mining for your master data combining classification data by using Product data as an example. Download the Document
This document describes how you could enable the rule mining for your master data combining classification data by using Product data as an example. PUBLIC
How to Enable Master Data Rule Mining with
Classification Data
Applicable Releases:
From SAP Master Data Governance on SAP S/4HANA 1909 and newer
Version 1.1
September 2024
How to Enable Master Data Rule Mining with
Classification Data
Applicable Releases:
From SAP Master Data Governance on SAP S/4HANA 1909 and newer
Version 1.1
September 2024
© 2024 SAP SE or an SAP affiliate company. All rights reserved. See Legal Notice on www.sap.com/legal-notice for use terms, disclaimers, disclosures, or restrictions related to this material. 3 / 18
Table of contents
1. Introduction ......................................................................................................................................................... 4
2. Business Scenario ................................................................................ Error! Bookmark not defined.
3. Implementation.................................................................................................................................................. 4
3.1. Build a CDS View to transform Classification Data ...................................................................................................... 4
3.1.1. Supported Characteristics .................................................................................................................................................................................... 4
3.1.2. Create CDS View........................................................................................................................................................................................................... 5
3.1.3. Test CDS View................................................................................................................................................................................................................. 7
3.2. Extend Data Model with Classification Data .................................................................................................................... 7
3.2.1. Data Model Extension .............................................................................................................................................................................................. 7
3.2.2. Generate Artifacts..................................................................................................................................................................................................... 10
3.2.3. Specify Table and Field Properties ............................................................................................................................................................ 11
4. Process Test ...................................................................................................................................................... 13
4.1. Preparation ........................................................................................................................................................................................... 13
4.2. Rule Mining ........................................................................................................................................................................................... 13
4.3. Data Quality Evaluation (optional) ....................................................................................................................................... 16
4.3.1. Data Quality Rule Setup ...................................................................................................................................................................................... 16
4.3.2. Data Quality Evaluation ........................................................................................................................................................................................ 18
Table of contents
1. Introduction ......................................................................................................................................................... 4
2. Business Scenario ................................................................................ Error! Bookmark not defined.
3. Implementation.................................................................................................................................................. 4
3.1. Build a CDS View to transform Classification Data ...................................................................................................... 4
3.1.1. Supported Characteristics .................................................................................................................................................................................... 4
3.1.2. Create CDS View........................................................................................................................................................................................................... 5
3.1.3. Test CDS View................................................................................................................................................................................................................. 7
3.2. Extend Data Model with Classification Data .................................................................................................................... 7
3.2.1. Data Model Extension .............................................................................................................................................................................................. 7
3.2.2. Generate Artifacts..................................................................................................................................................................................................... 10
3.2.3. Specify Table and Field Properties ............................................................................................................................................................ 11
4. Process Test ...................................................................................................................................................... 13
4.1. Preparation ........................................................................................................................................................................................... 13
4.2. Rule Mining ........................................................................................................................................................................................... 13
4.3. Data Quality Evaluation (optional) ....................................................................................................................................... 16
4.3.1. Data Quality Rule Setup ...................................................................................................................................................................................... 16
4.3.2. Data Quality Evaluation ........................................................................................................................................................................................ 18
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1. Introduction
Data Quality Management with SAP Master Data Governance achieves the best quality for master data in
customer’s SAP S/4HANA system(s) and their entire enterprise application landscape by extending and
integrating with all MDG-related processes, in the most efficient way, at the lowest possible implementation
efforts and TCO, by following an extensible packaged-application approach.
MDG Rule Mining supports the business / master data expert to analyze existing master data and propose
the found patterns as rules by leveraging machine learning technology.
Classification data is essential for companies to manage their flexible data structure in important master data
e.g. product. This document describes how you could enable the rule mining for your master data combining
classification data by using “product” as example.
Classification data is not included in the SAP standard delivery in rule mining for products and business
partners. This is because of the generic data structure in classification. This document explains how the
classification data can be transformed to function as a normal data source and combined with master data
(For example, Product) to apply to rule mining.
2. Implementation
This chapter explains all necessary technical steps to implement the solution.
2.1. Build a CDS View to transform Classification Data
To be able to use rule mining in a Master Data model, it is necessary to transform the generic classification
data to an understandable data source such as a normal table, here we choose an ABAP CDS view which is
standard and easiest for modelling in our environment.
2.1.1. Supported Characteristics
Because characteristics must be transformed into normal attributes for master data, only Single Value and
Character Format is supported. This screenshot shows an example a product classification with 3
characteristics with Single Values.
1. Introduction
Data Quality Management with SAP Master Data Governance achieves the best quality for master data in
customer’s SAP S/4HANA system(s) and their entire enterprise application landscape by extending and
integrating with all MDG-related processes, in the most efficient way, at the lowest possible implementation
efforts and TCO, by following an extensible packaged-application approach.
MDG Rule Mining supports the business / master data expert to analyze existing master data and propose
the found patterns as rules by leveraging machine learning technology.
Classification data is essential for companies to manage their flexible data structure in important master data
e.g. product. This document describes how you could enable the rule mining for your master data combining
classification data by using “product” as example.
Classification data is not included in the SAP standard delivery in rule mining for products and business
partners. This is because of the generic data structure in classification. This document explains how the
classification data can be transformed to function as a normal data source and combined with master data
(For example, Product) to apply to rule mining.
2. Implementation
This chapter explains all necessary technical steps to implement the solution.
2.1. Build a CDS View to transform Classification Data
To be able to use rule mining in a Master Data model, it is necessary to transform the generic classification
data to an understandable data source such as a normal table, here we choose an ABAP CDS view which is
standard and easiest for modelling in our environment.
2.1.1. Supported Characteristics
Because characteristics must be transformed into normal attributes for master data, only Single Value and
Character Format is supported. This screenshot shows an example a product classification with 3
characteristics with Single Values.
© 2024 SAP SE or an SAP affiliate company. All rights reserved. See Legal Notice on www.sap.com/legal-notice for use terms, disclaimers, disclosures, or restrictions related to this material. 5 / 18
2.1.2. Create CDS View
You need to use the ABAP Development tool in Eclipse to create a CDS view in your system, be aware that
you need developer authorization in your system to create it in the “Z” namespace.
Annotation @AbapCatalog.preserveKey must be set to True.
Here is an example of a CDS view which you could copy and use for your own classification transformation.
@AbapCatalog.sqlViewName: 'ZMATCLS01'
@AbapCatalog.compiler.compareFilter: true
@AbapCatalog.preserveKey: true
@AccessControl.authorizationCheck: #CHECK
@EndUserText.label: 'Classficaiton Data as extension of MARA'
define view ZKF_PROD_CLS01
as select distinct
key klah.class as Class ,
key cast( RTRIM( SUBSTRING( CLASS.objek , 1 , 40 ) , ' ' ) as matnr ) as Material ,
@EndUserText.label: 'Additional Seat on back'
cast ( AUSP0001.atwrt as zseat2 ) as ZSEAT2 ,
@EndUserText.label: 'Additional Seat on back description'
VALD0001._CharcValueDesc( P_KeyDate: $session.system_date )[1:Language = 'E' ].CharcValueDescription as
ZSEAT2_Desc ,
@EndUserText.label: 'Material of framewokr'
2.1.2. Create CDS View
You need to use the ABAP Development tool in Eclipse to create a CDS view in your system, be aware that
you need developer authorization in your system to create it in the “Z” namespace.
Annotation @AbapCatalog.preserveKey must be set to True.
Here is an example of a CDS view which you could copy and use for your own classification transformation.
@AbapCatalog.sqlViewName: 'ZMATCLS01'
@AbapCatalog.compiler.compareFilter: true
@AbapCatalog.preserveKey: true
@AccessControl.authorizationCheck: #CHECK
@EndUserText.label: 'Classficaiton Data as extension of MARA'
define view ZKF_PROD_CLS01
as select distinct
key klah.class as Class ,
key cast( RTRIM( SUBSTRING( CLASS.objek , 1 , 40 ) , ' ' ) as matnr ) as Material ,
@EndUserText.label: 'Additional Seat on back'
cast ( AUSP0001.atwrt as zseat2 ) as ZSEAT2 ,
@EndUserText.label: 'Additional Seat on back description'
VALD0001._CharcValueDesc( P_KeyDate: $session.system_date )[1:Language = 'E' ].CharcValueDescription as
ZSEAT2_Desc ,
@EndUserText.label: 'Material of framewokr'