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Migration and consolidation concept in SAP MDG (Master Data Governance)
Migration and consolidation
concept in SAP MDG (Master
Data Governance)
Hans-G. Emrich
August 2025
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Migration and consolidation concept in SAP MDG (Master Data Governance)
Table of Contents
1 ‘’ Foreword .................................................................................................................................1
2 ‘z’ Management summary......................................................................................................11
3 Introduction .....................................................................................................................................12
3.1 Ç A migration and consolidation concept in SAP MDG (Master Data Governance) ...12
3.1.1 ⬛³– Consolidation with SAP MDG ..................................................................................12
3.1.2 w› Ľ-- Migration in SAP MDG....................................................................................12
3.1.3 z‘’ Customizing & technical requirements .........................................................13
3.2 Recommendations for implementation .........................................................................13
4 C̨* Objectives .....................................................................................................................14
4.1 Establishment of a harmonized, quality-assured master data base .....................................14
4.1.1 1. Data consolidation & mass processing ..........................................................14
4.1.2 ) 2. Central governance ..............................................................................................14
4.1.3 ç ¡/# 3. Data quality management.................................................................................15
4.1.4 –³⬛ 4. Integration & Harmonization ..............................................................................15
4.1.5 7'•̧̇ .s 5. Organizational anchoring......................................................................................15
4.2 –' •.c‘'•⬛ 1. Data harmonization ..........................................................................................15
4.2.1 ³⬛ Data harmonization in SAP MDG – overview and key aspects .................................16
4.2.2 C̨* What does data harmonization mean in SAP MDG?.............................................16
4.2.3 ‘’z Technical implementation .......................................................................................16
4.2.4 /⬛ # Advantages of data harmonization.......................................................................17
4.3 O.̇́ • 2. Data quality management ...........................................................................17
4.3.1 ¡#ç / Data quality management with SAP MDG – overview and key functions 17
4.3.2 Key functions in data quality management with SAP MDG................................................18
4.3.3 Şeė •'7 advantages for companies ......................................................................18
4.4 −̂¹̄_ 3. Governance & Organization..............................................................................19
4.4.1 . Governance & Organization in the Context of SAP MDG – Foundation for
Sustainable Master Data Quality ...................................................................................................19
4.4.2 gĠ •● Governance structure – Who decides what?............................................................19
4.4.3 ̃-̂̇ *̂µ.,ç¡u'†̇ ’̂ Organizational anchoring – How is MDG implemented? ............................................19
4.4.4 Ç Interaction with IT & Business...........................................................................20
4.5 ’‘z MDG system landscape .................................................................................................21
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4.5.1 Objective ..........................................................................................................................21
4.5.2 Component overview........................................................................................................21
4.5.3 System landscape roles ...................................................................................................21
4.5.4 System connections & interfaces.....................................................................................22
4.5.5 Client and environment structure .....................................................................................23
4.5.6 Visualization (can be supplemented with graphics) .........................................................23
4.6 ‘’z MDG Hub or co-deployment – architecture decision ....................................................24
4.6.1 Objective ..........................................................................................................................24
4.6.2 MDG Hub implementation................................................................................................24
4.6.3 Co-deployment (embedded in ERP/S/4HANA)................................................................25
4.6.4 Decision criteria for the migration project.........................................................................25
2.6.4 Project decision made............................................................................................................26
4.7 ;□ _ Technological infrastructure in the context of SAP MDG – Foundation for
efficient master data management ......................................................................................................27
4.7.1 Ç 1. System architecture & deployment models......................................................27
4.7.2 " ” 2. Integration & Interfaces ...............................................................................28
4.7.3 ‘z’ 3. Extensibility & Customizing...................................................................28
4.7.4 çt'P 4. Security & Compliance............................................................................28
4.7.5 ⬛ 5. Monitoring & Performance...................................................................................28
4.8 ³⬛ Continuous improvement with SAP MDG – More than just master data maintenance....29
4.8.1 # ¡/ç 1. Data quality management as a driver....................................................................29
4.8.2 C̨* 2. Governance processes with a learning curve.....................................................30
4.8.3 ṡ .'7̧ • 3. Agile project methodology & quick wins..............................................................30
4.8.4 4. Extensibility & innovation...............................................................................30
4.9 Approach..............................................................................................................................30
4.9.1 Harmonization:.................................................................................................................30
4.9.2 ◆J ,ú Standardization of data structures and key fields in SAP MDG – the basis for
consistent master data....................................................................................................................31
4.9.3 ⬛ •.•c‘'' 1. Standardization of key fields...................................................................................31
4.9.4 °° 2. Name and attribute standards .................................................................................31
4.9.5 ⬛³– 3. Mapping tables for source and target systems ....................................................31
4.9.6 †5_ 4. Tools & best practices ...................................................................................32after go-live as a governance process.
4.15 Introduction of SAP MDG as the leading system for master data
4.15.1 Goal
• Establish SAP MDG as the central platform for managing material and
business partner master data.
• Ensure uniform governance processes for the creation, maintenance, and
release of master data.
• Reduce redundancies and decentralization in source systems.
4.15.2 Approach
4.15.2.1 System architecture
o Definition of SAP MDG as the "golden record" system for material and BP
master data.
o Integration into the existing system landscape (ERP, S/4HANA, non-SAP
systems).
4.15.2.2 Governance processes:
o Introduction of central workflows for creation, modification, and approval.
o Definition of responsibilities (data stewards, master data owners).
4.15.2.3 Migration & cutover:
o Transfer of consolidated master data from the source systems.
o Ensuring data quality before release in MDG.
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4.15.2.4 Interfaces & distribution:
o Setting up distribution processes (DRF, IDoc, web services).
o Ensuring consistency in all downstream systems.
4.15.3 Technical implementation
• Activation of SAP MDG data models MAT (material) and BP (business
partner).
• Use of MDG consolidation for initial harmonization.
• Setup of change requests and validation rules for governance processes.
4.15.4 Success
• Clear definition of MDG as the leading system in IT and data strategy.
• Involvement of business departments in governance processes.
• Ensuring seamless integration with downstream systems.
• Continuous monitoring of data quality and interfaces.
4.16 Establishment of governance processes for future data maintenance
The establishment of governance processes for future data maintenance is essential to
ensure the long-term quality of the data built up after migration.
4.16.1 Objective
• Ensure that master data maintenance is carried out according to
standardized and quality-assured processes.
• Avoid uncontrolled growth, duplicates, and inconsistencies after migration.
• Establish responsibilities and clear workflows for data changes.
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4.16.2 Procedure
4.16.2.1 Definition of roles and responsibilities:
o Data stewards for data quality.
o Master data owners for approvals.
o Business department roles for requests and checks.
4.16.2.2 Introduction of standardized workflows:
o Change requests for creation, modification, and deletion.
o Multi-level approvals (e.g., functional and technical).
o Automated validations and duplicate checks before approval.
4.16.2.3 Regular data quality checks:
o Monitoring of KPIs (e.g., mandatory field rate, duplicate rate).
o Periodic reports to departments and IT.
4.16.2.4 Change management:
o Training for departments and IT.
o Establishment of a data governance board.
o Adaptation of processes to new business requirements.
4.16.3 Technical implementation
• Use of SAP MDG workflows for change requests.
• BRF+ for validation and derivation rules.
• MDG Consolidation for regular duplicate checks.
4.16.4 Success
• Clear definition and communication of roles.
• Automation of checks and workflows to avoid errors.
• Regular KPI monitoring and reporting.
• Involvement of all relevant departments in governance processes
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5 Scope
5.1 Data objects – material master data
5.1.1 Object
• Definition of the scope and structures of the material master data to be migrated
and consolidated.
• Ensuring complete harmonization of relevant tables and attributes.
5.1.2 Scope of data objects
5.1.2.1 MARA (general material data):
o Global material attributes (e.g., material number, material type, base unit).
o Classification-relevant attributes (e.g., industry, product groups).
5.1.2.2 MARC (plant data):
o Plant-dependent information (e.g., scheduling indicator,
procurement type).
o Planning and production attributes.
5.1.2.3 MVKE (sales data):
o Sales area-dependent data (e.g., sales organization, sales
status).
o Price and condition relevance.
5.1.2.4 Classifications:
o Use of SAP classification systems (characteristics & classes).
o Harmonization of characteristics and value ranges.
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5.1.3 Special aspects
• Ensuring consistency between MARA, MARC, and MVKE.
• Harmonization of material numbers and material types.
• Integration into MDG material data model (MAT).
• Ensuring data quality through validations and duplicate checks at material level.
5.2 Data objects – Business partners
5.2.1 Purpose
• Define the business partner data that is to be migrated and consolidated.
• Harmonization and cleansing of customer, vendor, and address
information for SAP MDG-BP.
5.2.2 Scope of data objects
5.2.2.1 Customers (accounts receivable):
o Master data from KNA1, KNVV, and KNVP.
o Relevant attributes: Customer ID, name, address, tax information,
payment terms.
5.2.2.2 Suppliers (accounts payable):
o Master data from LFA1, LFB1, and LFM1.
o Relevant attributes: Supplier ID, name, bank information, addresses,
payment methods.
5.2.2.3 Addresses:
o Central address data from ADRC.
o Harmonization of address formats, postal code validations, and
country codes.
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5.2.3 Special aspects
• Consolidation of customer and supplier roles into a central business partner
in SAP MDG-BP.
• Duplicate check based on name, address, and tax IDs.
• Integration into the BP data model with roles (customer, supplier, contact
person).
• Ensuring consistency between address data and business
partner roles.
5.3 Source systems
5.3.1 Target
• Identification and description of the source systems for material and
business partner master data.
• Defining how to handle different system formats and data qualities.
5.3.2 System categories
5.3.2.1 SAP ERP systems:
o SAP ECC systems with classic table structures (e.g., MARA, KNA1).
o SAP S/4HANA systems with business partner model.
o Typical features: structured data, integrated processes,
standardized field logic.
5.3.2.2 Non-SAP systems:
o Third-party or legacy systems (e.g., CRM, Excel-based maintenance,
industry-specific applications).
o Partially unstructured or non-standardized data formats.
o Potential sources of inconsistencies and duplicates.
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5.3.3 Challenges & measures
• Different data models: Harmonization required via mapping tables.
• Varying data quality: Comprehensive profiling and cleansing preparation required.
• Missing mandatory fields or reference data: Supplement with derivation rules and
default values.
• Interface design: Development of ETL routes (SAP Data Services, SLT, RFC, web
services).
5.4 Target system
5.4.1 Target
• Definition of the target system for the consolidated and harmonized
master data.
• Description of the MDG components used and their respective roles in the overall
process.
5.4.2 Components of the target system
5.4.2.1 SAP MDG material:
o Governance module for central maintenance and approval of
material master data.
o Use of workflows, validations, and derivation rules for quality
assurance.
o Integration with classification, bills of materials, and purchasing/sales data.
5.4.2.2 SAP MDG-BP (Business Partner):
o Central platform for customer, supplier, and
contact person master data.
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o Implementation of the business partner role (customer+ supplier+
address)in one object.
o Use of role-based workflows for data maintenance.
5.4.2.3 SAP MDG Consolidation:
o Consolidation and cleansing of data from multiple source
systems.
o Duplicate checking, matching, and merging functionality with staging area.
o Creation of harmonized business partner and material master records for
transfer to MDG governance.
5.4.3 System landscape
• SAP MDG runs as a central instance (hub) or integrated into an S/4HANA
system.
• The maintained data is distributed via DRF (Data Replication Framework) or
interfaces to the ERP and non-SAP target landscape.
• All governance processes are carried out exclusively via the MDG system.
5.5 Not included in the scope
5.5.1 Object
• Clear separation of data and functions that are not part of the migration and
consolidation project.
• Avoidance of misunderstandings among project participants and external
interfaces.
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5.5.2 Not included in the scope:
5.5.2.1 Transaction data:
o No migration or processing of postings, orders, deliveries, invoices,
etc.
o Focus exclusively on static master data (material, business
partner).
5.5.2.2 Historical change documents:
o Change logs from source systems (e.g., CDHDR/CDPOS) are not
transferred.
o The MDG system starts with a "cleaned" and quality-assured database.
5.5.2.3 Archive data & legacy data without relevance:
o Unused, obsolete, or inactive master records outside the defined cut-off
date criteria.
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6 Project procedure
6.1 Object
• Description of the structured procedure for migrating and consolidating material and
business partner master data using SAP MDG.
• Ensuring a traceable, risk-minimized, and quality-oriented
implementation.
6.2 Phase model
6.2.1 Analysis phase
• System and data inventory.
• Data profiling (mandatory fields, formats, value ranges).
• Identification of duplicates and inconsistencies.
• Interviews with specialist departments to record requirements and data logic.
6.2.2 Design phase
• Definition of data models (MDG-MAT, MDG-BP).
• Creation of mapping tables for source to target structure.
• Definition of validations, derivations, and merge rules.
• Planning of governance processes (workflows, roles, approvals).
6.2.3 Build phase
• Technical setup in SAP MDG: data models, workflows, consolidation.
• Implementation of interfaces (ETL, DRF, web services).
• Configuration of duplicate checks and consolidation rules.
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6.2.4 Test
• Test migration with realistic data volumes.
• Functional tests: validation, consistency, merge results.
• Technical tests: performance, interface behavior, error handling.
• Acceptance by the specialist departments.
6.2.5 Go-live and hypercare
• Final duplicate run and final migration.
• Activation of governance processes in the MDG.
• Follow-up support and monitoring.
• Correction processes and post-migration as required.
6.3 Roles and responsibilities
• Project management: control, planning, reporting.
• Data architect: Overall responsibility for data model and governance logic.
• Data stewards: Technical data quality assurance.
• Business departments: Requirements, testing, approvals.
• ETL development: interface construction and transformation.
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7 Data analysis
7.1 Objective
• Systematic analysis of existing material and business
partner master data in the source systems.
• Identification of data quality problems, duplicates, and inconsistencies.
• Creation of a basis for mappings, consolidation rules, and cleansing strategies.
7.2 Data sources & inventory
• Listing of all source systems (SAP ERP, non-SAP).
• Identification of relevant tables (e.g., MARA, KNA1, LFA1, ADRC).
• Overview of data volume, object types, and frequency of use.
7.3 Data profiling
• Analysis of data quality with a focus on:
o Completeness (mandatory fields filled in?)
o Format consistency (e.g., date formats, IBAN, postal code)
o Value ranges (valid codes, units, country codes)
• Statistical evaluations: field utilization, distribution of material types, BP types,
etc.
7.4 Duplicate analysis
• Identification of multiple assets via:
o Business partner: Name+ Address, tax ID, bank details
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o Material: material number, description, classification
• Evaluation according to matching criteria: exact match vs. fuzzy logic
• Quantification: duplicate rate, system distribution, affected
organizational units
7.5 Consistency analysis
• Checking logical field dependencies (e.g., control country+ control key, unit +
quantity type).
• Determination of inconsistencies between tables (e.g., MARA↔ MARC, LFA1
↔ ADRC).
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8 Mapping concept
The mapping concept is a central element in the technical core of the migration. It describes
how fields from the source systems are assigned to the structures in the target
system SAP MDG – including transformations, standardizations, and harmonization logic.
8.1 Goal
• Description of the assignment of fields and data structures between the source and
target systems.
• Ensuring consistent transfer and harmonization of material and business partner
master data.
8.2 Structure of the mapping tables
• Defining source and target field names (e.g., MARA-MTART→ MDG
material type).
• Typical components of a mapping table:
o Source field
o Target attribute (MDG)
o Transformation (optional)
o Default value (fallback)
o Reference table/code logic (e.g., unit codes, work numbers)
8.3 Mappings for material master data
Examples:
• Material type (MTART): Source value "FERT"→ Target value "FINISHED PRODUCT"
• Base unit (MEINS): "ST"→ "Piece"
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• Plant codes: Harmonization of different plant numbers in the target
system structure
• Classifications: Mapping of characteristic names and values
8.4 Mappings for business partners
Examples
• Address data: Postal code, country, city – format standardization
(e.g., ISO country codes)
• Customer groups/supplier groups: Source values from KNVV/LFM1 to
target codes
• Payment terms, bank details: Harmonization according to global MDG standard
8.5 Harmonization keys
• Consolidated code sets for organizational units (e.g.,
purchasing organizations, sales divisions).
• Definition of global keys to standardize country-specific special values.
8.6 Tools & implementation
• Mapping logic is maintained in:
o SAP Data Services
o Excel templates (maintained by the specialist department)
o Custom tables in MDG
• Mappings are compared iteratively during the test and migration phases.
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9 Validation concept
Validation rules ensure that only consistent and complete data is migrated. These include:
• Mandatory field checks (e.g., material number, address)
• Field combination checks (e.g., tax country+ , tax code)
• Format and value range checks
9.1 Object
• Ensuring that only correct, complete, and consistent data is transferred to and
processed in SAP MDG.
• Avoidance of incorrect data states in the target system.
9.2 Validation categories
9.2.1 Mandatory field checks
o Ensure that essential fields are filled in (e.g., material number, name,
address, tax data).
o Implementation via technical checks and business rules (BRF+).
9.2.2 Field combination rules
o Example:If "Tax country" is filled in, "Tax code" must also be
maintained.
o Logical dependencies between fields (e.g., unit+ , quantity type, BP roles +
addresses).
9.2.3 Format and value range checks
o IBAN, postal code, email addresses, country codes.
o Comparison with customizing tables and codes (ISO, SAP standard).
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9.2.4 Duplicate detection (via MDG consolidation)
o Check for multiple entries using matching algorithms.
o Manual confirmation or automatic merging (merge rules).
9.3 Validation tools
• SAP MDG Governance:
o Validation logic via BRF+ or BAdIs in the change request workflow.
• SAP MDG Consolidation:
o Preliminary validations in the staging area (e.g.,
mandatory fields, key checks).
• ETL layer (e.g., SAP Data Services):
o Preliminary checks and transformations already during data import.
9.4 Validation strategy
• Validation in multiple layers:
1. Preliminary check in ETL
2. Consolidation check in MDG Consolidation
3. Governance check in MDG Material/BP
• Error feedback to departments with correction before release.
• Coverage through defined test cases in the test phase.
9.5 Success
• Early involvement of departments in the definition of validation
rules.
• Iterative testing and adjustment of rules during test migration.
• Consistent documentation of all validation logic.
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• Integration into automated governance processes (change requests).
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10 Implementation and use of SAP MDG
Consolidation
The deployment and use of SAP MDG Consolidation is the central component for
data cleansing and harmonization within your migration and consolidation
concept.
10.1 Objective
• Description of the strategic and operational role of SAP MDG
Consolidation in the migration project.
• Differentiation from the governance process and presentation of possible
applications for data harmonization, duplicate checking, and data quality.
10.2 Target vision & role in the process
• MDG Consolidation serves as the central staging and checking instance for the
Consolidation of master data from different source systems.
• Focus is on:
o Identifying duplicates (matching)
o Consolidation of redundant data records (merging)
o Quality control & cleansing before governance takeover
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10.3 Main functions of MDG Consolidation
Function Description
Matching
Rule-based identification of potential duplicates using fuzzy and
hard matching
Merging
Merging of redundant data sets taking rules into account
Staging management Data import from source systems for verification and processing
Simulation
Preview of merge results for validation before acceptance
Transfer to
governance
Qualified data is transferred to the MDG data model via CRs
10.4 Delimitation from MDG governance
MDG consolidation MDG governance
Consolidation and cleansing of inventory data Structured maintenance processes in
regular operation
Import and verification mechanisms via staging
Workflow-based approvals in change
requests
Batch processing, no real-time operation Real-time approval by user roles
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10.5 Deployment phases in the project
Phase Use of MDG Consolidation
Analysis phase Initial duplicate runs to identify redundant data
Migration preparation
Import+ Matching+ Merging of data before governance handover
Go-live preparation Final cleanup run and simulation
Operation
Regular cleanup runs (e.g., for new imports from legacy systems)
10.6 Advantages in the project context
• Suitable for large volumes of data from different sources
• Rule-based processing reduces manual checking and
cleanup efforts
• Improved duplicate accuracy through matching engine
• Traceability through logging and review functionality
10.7 Success factors
• Early coordination of matching and merge strategies with specialist departments
• Iterative test runs with growing data volumes
• Clear acceptance process for merge proposals before release
• Documented transfer processes to MDG governance (change requests)
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11 MDG consolidation setup
The MDG consolidation setup is the technical and functional basis for duplicate checking,
merging, and harmonization of data. This chapter describes in detail how MDG
consolidation is configured and used.
The setup includes configuring the staging area, defining matching and merge rules, and
planning iterative consolidation runs.
Example
• Materials: Material number, description
• Business partners: Name, address, tax ID
11.1 Object
• Establishment of a central consolidation environment for cleansing,
harmonizing, and merging material and business partner master data.
• Preparation of quality-assured data for transfer to SAP MDG governance.
11.2 Staging Area
• Function: Temporary storage for data from SAP and non-SAP systems.
• Data structure: Source tables for materials (e.g., MARA, MARC) and
business partners (KNA1, LFA1, ADRC).
• Source system identifier: Each data record contains a technical origin ID for
traceability.
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11.3 Matching rules
• Definition of rules for identifying potential duplicates.
• Examples
o Material: Material number+ Description+ Classification
o Business partner: Name+ Address+ Tax ID
• Use of exact and fuzzy matching.
11.4 Merge rules
• Determination of which field from which source system is transferred to the
target data record.
• Rules:
o Priority according to source system (e.g., leading ERP system)
o Attribute-dependent prioritization (e.g., bank details from system A,
addresses from system B)
• Manual correction option via UI
11.5 Consolidation runs
• Iterative runs:
o Data import→ Duplicate check→ Proposal→ Review→ Merge → Transfer
to MDG governance
• Logging of all check results and merge decisions.
• Option to simulate before productive merge.
11.6 Tools & Technologies
• SAP MDG Consolidation component on the MDG system.
• Interfaces via SAP Data Services, IDoc, SLT, or flat file.
• Fuzzy matching engine & custom match functions.
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11.7 Success
• Early definition of matching and merge criteria with the specialist departments.
• Test runs with representative data sets.
• Documented acceptance logic for manually consolidated records.
• Seamless connection to MDG governance for approval of the cleaned data.
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12 Migration strategy
The migration strategy forms the transition from data preparation to productive
implementation. It describes when, how, and by what means the cleaned data is
transferred to SAP MDG.
The migration strategy includes extraction, transformation, and loading of data (ETL). The
data is extracted from source systems, loaded into the MDG Consolidation Staging Area,
consolidated, and then transferred to the MDG Governance data model.
Excellent – the migration strategy forms the transition from data preparation to productive
implementation and should be added as chapter 9 in your Word document. It describes
when, how, and by what means the cleaned data is transferred to SAP MDG.
12.1 Goal
• Definition of the technical and temporal procedure for transferring
consolidated master data to SAP MDG.
• Ensuring error-free,traceable, and transparent migration.
12.2 Migration objects
• Material master data: MARA, MARC, MVKE, classifications
• Business partners: Customers (KNA1/KNVV), suppliers (LFA1/LFM1), addresses
(ADRC)
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12.3 Procedure model
12.3.1 Initial migration:
o One-time transfer of harmonized master data from MDG
Consolidation to the MDG Governance data model.
o Goal: Starting point for governance-based maintenance.
12.3.2 Delta handling:
o Handling subsequent changes in the source systems before go-live.
o Option: Define cutoff date+ ely manual follow-up maintenance in MDG.
12.3.3 Post-migration correction:
o Detection and correction of errors that only become visible after release.
o Use of MDG change requests.
12.4 Migration technologies
• Data extraction: SAP Data Services, SLT, or flat files
• Loading processes:
o Consolidated data→ MDG Consolidation Staging→ Merge→
Governance models
• Data transport: Use of import services, APIs, or your own ETL route
12.5 Migration controls
• Validation during and after migration (mandatory fields, duplicates).
• Approval processes with specialist departments (review & approval).
• Logging of imports, errors, and approvals.
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12.6 Success
• Clear scheduling of cutover and migration phases.
• Prepared rollback/fallback strategy.
• Communication plan for departments during correction runs.
• Test migration runs with production data volumes.
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13 Test strategy
The test strategy is an essential part of every migration. It documents how and with what
means the migration, consolidation, and validation are checked to ensure that they run
correctly before go-live.
13.1 Object
• Ensure that all migrated and consolidated master data is technically correct,
functionally valid, and processed in a stable manner by the system.
• Identify and correct errors before going live.
13.2 Test types
13.2.1 Unit tests
o Checking individual mappings, validations, and merge rules in MDG
Consolidation.
o Technical tests of BRF+ rules and BAdIs in the governance model.
13.2.2 Integration tests (end-to-end)
o Complete test migration of a representative data volume.
o Validation of data flows: Source system→ Staging→ Merge→
Governance model.
o Interface tests (DRF, web service, ETL).
13.2.3 User acceptance tests (UAT)
o Performed by specialist departments.
o Data quality checks, duplicate checks, approval processes.
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o Documentation & technical acceptance.
13.3 Test scope
• Definition of a representative test data scope (e.g., 10% per
organizational unit).
• Focus on data with special cases (missing fields, duplicates,
format deviations).
• Consideration of multiple source systems and formats.
13.4 Test environment
• MDG system (test client or sandbox) with complete customizing.
• Separate clients for staging and governance module.
• Simulated real processes with dummy workflows.
13.5 Test documentation
• Test logs (results, errors, post-processing).
• Linkage with test cases, mapping lists, and validation rules.
• Acceptance checklists for each department.
13.6 Success
• Early involvement of departments in UAT planning.
• Iterative test runs with rule adjustments based on feedback.
• Transparent error communication and tracking.
• Acceptance protocol as a prerequisite for go-live.
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14 Cutover & Go-Live
The cutover plan defines the time window, the system freeze, and the final data migration.
After go-live, there is a hypercare phase with monitoring and correction runs.
14.1 Object
• Planned, structured, and low-risk transition from test to production.
• Ensuring that all consolidated master data is transferred to the target system
completely, correctly, and without interruption.
14.2 Cutover preparation
14.2.1 Cutover plan:
o Detailed schedule with activities, responsible parties, and sequences.
o Approval by project management and specialist departments.
14.2.2 System freeze:
o Source systems are write-protected from a defined cut-off date.
o Only MDG is enabled for master data maintenance.
14.2.3 Last validation:
o Final duplicate check in MDG Consolidation.
o Final merge approvals by specialist departments.
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14.3 Productive migration
• Data transfer to MDG Governance (Governance data model).
• Activation of all workflows and validation rules.
• Replication to target systems (DRF, IDoc, API).
14.4 Go-live
• Activation of MDG governance processes for end users.
• Information transfer to all stakeholders (go-live communication).
• Monitoring of jobs, interfaces, and approval processes.
14.5 Hypercare phase
• Duration: typically 4–8 weeks after go-live.
• Measures
o Daily monitoring reports
o Escalation path for errors
o Follow-up by data stewards
o Subsequent migration of laggard data
14.6 Success factors
• Realistic cutover schedule with sufficient buffer time.
• Close communication with IT and business departments.
• Provision of fallback levels (e.g., archived source data, backup of MDG staging
data).
• Presence of key users and stewards during hypercare.
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15 Governance & operation
Ensuring that data quality and system stability remain guaranteed after go-live.
15.1 Objective
• Ensuring sustainable operation of the SAP MDG master data platform.
• Establishing clear responsibilities, processes, and control mechanisms for ongoing
data maintenance.
15.2 Roles & Responsibilities
15.2.1 Data stewards:
o Ongoing quality assurance, operational maintenance support, monitoring.
15.2.2 Master data owner:
o Strategic responsibility, final approvals, escalations.
15.2.3 Specialist departments:
o Submission of change requests, checking and review.
15.2.4 IT support/operations:
o Technical support, system monitoring, interface management.
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15.3 Operational processes
15.3.1 Change request management:
o Maintenance processes are carried out exclusively via MDG
governance workflows.
o Validations and approval stages prevent incorrect entries.
15.3.2 Monitoring & Alerts:
o Automated monitoring of jobs (e.g., duplicate checking,
replication).
o Escalation mechanisms for errors or data conflicts.
15.3.3 Master data quality:
o Regular checks based on defined KPIs (mandatory fields, duplicates,
format errors).
o Preparation of monthly reports for specialist departments and management.
15.4 Operation of MDG consolidation
• Recurring duplicate checks (e.g., monthly or for mass imports).
• Use for subsequent harmonization of data from new
sources/systems.
• Logging of all consolidation steps.
15.5 Change management
• New data requirements are evaluated via central governance.
• Adjustment of workflows, validations, or mappings after defined approval.
• Documentation of all rule changes in the operations manual.
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15.6 Success
• Clear governance structures with a coordinated role model.
• Automation of all check routines where possible.
• Close cooperation between IT, data stewards, and specialist departments.
• Ongoing maintenance and documentation of governance processes.
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16 Fiori implementation in the context of
migration and consolidation with SAP
MDG
Fiori implementation is a relevant aspect in the context of SAP MDG, especially in the
governance process after migration and consolidation. It affects both usability and
acceptance of data maintenance processes in the specialist departments.
The implementation of SAP Fiori in the context of migration and consolidation with SAP
Master Data Governance (MDG) is not just a cosmetic measure – it changes the way master
data processes are controlled and experienced.
16.1 Objective
• Description of the role of SAP Fiori apps in master data maintenance after
migration and consolidation.
• Support user acceptance and efficiency of governance processes through
modern, role-based user interfaces.
16.2 � Fiori as a bridge between user-friendliness and governance
• Lean Request Apps: Specially developed for business users without in-depth
master data knowledge – e.g., for creating new customers or suppliers
• Role-specific interfaces: Fiori offers customized views for data stewards,
departments, and IT
• Workflow integration: Approval processes and validations are embedded directly in
the UI
16.3 � Fiori in consolidation & migration
• MDG Consolidation Apps: Enable the import, management, and
consolidation of source data via intuitive Fiori interfaces
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• Data package control: Users can selectively transfer and consolidate data
packages into processes
• Error logs & validation: Direct feedback in case of inconsistencies or
missing segments
16.4 🛠� Technical requirements
• System landscape: SAP Fiori requires an activated front-end and back-end
infrastructure with appropriate OData services
• Product versions: e.g., SAP MDG 9.0 or higher, UI components such as
UIMDC001 for master data consolidation
16.5 📦 Migration tools with Fiori connection
• Data Import Framework
• SAP Data Services & Smart Data Integrator
• S/4HANA Migration Cockpit – partially with Fiori UI
16.6 � Advantages of Fiori integration
• Greater acceptance thanks to intuitive operation
• Faster error detection and correction
• Transparent processes and improved traceability
16.7 Role of Fiori in the MDG context
• SAP Fiori serves as the central user interface for MDG governance
processes after migration.
• Users interact with master data changes, duplicate checks,
approvals, and monitoring via Fiori apps.
• Fiori improves transparency, usability, and mobile usability.
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16.8 Integration into the migration process
16.8.1 During migration:
o Only indirect role – Fiori is used for data display or test
validation, if necessary.
o Consolidation via SAP GUI or special MDG Consolidation Fiori apps.
16.8.2 After migration:
o Fiori apps are the primary means of access for end users for data maintenance.
o Example apps:
▪ Manage Business Partner
▪ Change Requests (creation, approval, processing)
▪ Duplicate check results
▪ Data quality KPIs
16.9 Technical aspects
• Fiori Launchpad as entry point for role-based governance processes.
• Backend integration via OData services.
• Use of MDG Standard Fiori apps with optional extensions (e.g., custom fields,
validation status, dashboards).
16.10 Success
• Role-specific app selection depending on function in the governance process (e.g.,
data steward, approver).
• Training on using Fiori after go-live.
• UX test before going live to optimize user guidance.
• Integration into the operational management concept (support, authorizations, monitoring).
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16.11 Relevant SAP Fiori apps in the context of
SAP MDG & migration
Here is a list of relevant SAP Fiori apps in the context of migration, consolidation,
and master data maintenance with SAP MDG. These apps particularly support the
work of data stewards, business departments, and IT following migration and during
ongoing governance.
App name App ID / Technical name Area of application
Manage Business
Partner Master Data
F1602
Display, change, and check business
partners (BP)
Manage Business
Partner Relationships
F1982
Management of relationships between
business partners
Manage Supplier
Master Data
F1611 Maintain suppliers via Fiori UI
Manage Customer
Master Data
F1606 Maintain customer master data
Change request
processing
F1604
Processing and approval of change
requests
Create Change
Request
F1603
Creation of new change requests for
material or BP
My Change Requests F1605
Personal inbox for open approvals
Duplicate check –
business partner
MDG_BS_BP_DUPLICATE_SEARCH
Display results of duplicate
checks (post-consolidation)
Mass processing for
business partners
F2815
Mass change of BP data after
consolidation
Data Quality
Management
Dashboard
F2903
Monitoring of data quality metrics
(KPIs, error rates)
Consolidation
Workbench (GUI app)
NWBC integration
(MDGCONSOLWB)
Central app for duplicate checking and
consolidation
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App name App ID / Technical name Area of application
Data replication
monitoring
DRFIMG / Fiori-driven UI
Control and logging of data distribution
Manage material
master data (optional)
F1863
Maintenance of material master data via
Fiori (governance model)
16.12� Notes on use
• Many of these apps require corresponding roles via SAP Fiori
Launchpad (FLP).
• Some consolidation functions (e.g., Merge Review, Simulation) are only
available in SAP GUI / NWBC – Fiori extension is possible.
• The apps can be integrated into role-based groups/tiles in
Launchpad Designer.
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17 Technical tools & infrastructure
Here is a structured overview of the relevant migration tools in the context of SAP
MDG and MDG Consolidation. These tools are used in the various phases of migration
– from data extraction to consolidation and governance processing.
17.1 Overview of migration tools for SAP MDG & MDG Consolidation
Tool / Technology Area of application Special features
SAP MDG
Consolidation
Data consolidation, duplicate checking,
matching, merging
Central component for consolidation in
the staging model
SAP MDG staging
area
Temporary data storage for checking
before transfer to the governance data
model
Part of MDG Consolidation, separate
from the active data pool
SAP MDG Data
Import Framework
Technical data transfer from consolidation
or external sources to MDG governance
Transfer of consolidated data to the
active data model with CR process
SAP Data Services
(DS)
ETL tool for data extraction, transformation,
cleansing, and loading
Highly powerful for mass
data migration & pre-
validation
SAP Landscape
Transformation
(SLT)
Real-time data replication from SAP source
systems
Real-time synchronization of master
data possible
SAP LSMW (legacy
tool)
One-time migration of small amounts of
data from legacy systems
Hardly used in MDG projects anymore
– replaced by Data Services / CR
Import
SAP S/4HANA
Migration Cockpit
Standardized data migration to S/4HANA
(including MDG preparation)
More relevant for greenfield projects or
parallel implementation
Web services / APIs
Integration of external systems (e.g., CRM,
MDM) with MDG staging
Ideal for recurring, automated data
transfers
MDG File Upload
(CSV/XML)
Upload data to staging via file
Often used for initial transfers or test
data
DRF (Data
Replication
Framework)
Replication of MDG-maintained data to
ERP or target systems
After go-live for data distribution in
operation
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17.2 � Application scenarios – rough classification by phase
Phase Tool(s)
Data extraction SAP Data Services, SLT, Web Services, File Upload
Data cleansing
SAP Data Services (profiling), MDG consolidation,
proprietary validation logic
Duplicate checking
& merging
SAP MDG consolidation (matching & merging), custom
matching strategies
Data migration (import) MDG Data Import Framework, File Upload, Web Services,
S/4HANA Migration Cockpit (optional)
After go-live /
distribution
DRF (Data Replication Framework), Web Services
18 Design of validation rules,
derivation rules, and change requests
The topic of validation rules, derivation rules, and change requests is a central,
process-related technical component of your migration and consolidation concept in
SAP MDG.
18.1 Objective
• Ensure that all migrated and consolidated master data is transferred to the
SAP MDG governance model completely, correctly, and in accordance with the
system.
• Support users with automated derivations and structured change
processes for sustainable data quality.
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18.2 Validation rules
Purpose: Prevent incorrect, incomplete, or illogical entries/migrations.
18.2.1 Typical validations in the context of migration:
• Mandatory field check: e.g., material type, base unit, BP address.
• Field combinations: Tax code only if tax country is maintained.
• Formats: IBAN check, email validation, ISO code check.
• Domain checks: e.g., unit→ only valid values from T006.
18.2.2 Technical implementation:
• BRF+ rules (Business Rule Framework Plus) for simple checks.
• BAdIs (e.g., USMD_RULE_SERVICE_CROSS_ET) for complex logic or
cross-checks.
• Pre-check in MDG Consolidation: already possible in staging.
18.3 Derivation rules
Purpose: Automated pre-filling of dependent fields to reduce user workload and ensure
consistent logic.
18.3.1 Examples of derivations:
• Material group→
• ZIP code+ Country→ City (based on reference table)
• Customer role→ Payment terms
• Plant→ Purchasing organization (depending on client)
18.3.2 Technical implementation:
• BRF+ derivations as decision tables or expressions.
• Optional: Extensions via BAdI if BRF+ is not sufficient.
• Derivation also possible when importing from MDG Consolidation.
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18.4 Change requests in the migration context
Purpose: Structured, traceable transfer and release of consolidated master data to the active
MDG model.
18.4.1 Types of change requests:
• Mass creation: e.g., via file upload, mapping lists,
consolidation results.
• Individual creation: Manually by the department (e.g., new suppliers).
• Correction CR: For manual follow-up maintenance after migration.
18.4.2 Process logic:
• Initial creation→ Pre-validation→ Approval by data steward/department →
Activation
• Optional: Simulation before release
• Option to integrate Fiori interfaces (e.g., "Create Change Request" app, "My
Change Requests")
18.5 Success
• Joint coordination of validation and derivation logic with the specialist
departments.
• Simulation of CRs with migration data in test runs.
• Reuse of rule sets for regular operation (after go-live).
• Versioning and documentation of all rules (e.g., in rule catalog or BRF+
export).
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19 Use of CDQ and DQM in the migration and
consolidation context.
The use of CDQ (Cloud Data Quality) and SAP DQM (Data Quality Management) is a
modern and strategically important component for
improving data quality during and after migration in the SAP MDG context.
19.1 Objective
• Integration of cloud-based and system-supported verification and
enrichment services to improve data quality during migration and ongoing
governance operations.
• Automated validation, completion, and standardization of business partner
and material master data.
19.2 What is CDQ?
CDQ (Collaborative Data Quality) is a cloud-based data quality service that:
• Uses reference data from public and commercial sources (e.g.,
company registers, LEI registers)
• Duplicates identified, addresses standardized, and missing fields (e.g.,
industry, tax ID number) added,
• can be integrated directly into SAP MDG via an API.
Use in migration:
• Use prior to consolidation for external enrichment of BP data.
• Better duplicate identification thanks to external company data.
• Completion of missing information in incomplete records.
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19.3 What is SAP DQM?
SAP Data Quality Management (DQM) is a solution (on-premise or cloud) that:
• Checks data for completeness, structure, and format (addresses, email,
phone numbers, etc.)
• Provides geodata, ISO codes, and address standards,
• Partially integrated in SAP Data Services, SAP BTP, or directly in MDG.
Use in MDG projects:
• Checking and standardizing data when transferring it from source
systems.
• Real-time checking during entry into MDG (via BRF+ or service integration).
• Integration into consolidation runs (staging→ → Merge checks).
19.4 Integration into the migration process
Phase CDQ/DQM deployment
Analysis
Data quality check with DQM; detection of incomplete or
incorrect addresses
Consolidation
Use of CDQ for external duplicate detection & data
enrichment
Validation
before import
Use of DQM for structure checking (ISO codes, postal codes,
formats, etc.)
Go-live &
operation
DQM/service checks during governance processes in real time
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19.5 Benefits
• Significant increase in data quality before and after migration.
• Automated checking and completion reduces manual effort.
• Improved duplicate accuracy through external data sources.
• Scalable for additional data objects and international locations.
19.6 Success
• Early integration of CDQ and DQM checks into the rules and regulations.
• Provision of interfaces (e.g., via SAP BTP or API Gateway).
• Approval of external data enrichment by the specialist departments (data
protection & compliance).
• Documentation of all sources and rules used in the governance
manual.
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20 Appendix – Definitions and
Glossary (Migration & MDG
Consolidation)
A glossary/list of definitions is essential for clearly explaining key terms
and abbreviations in the context of migration, MDG consolidation, and
SAP MDG.
Term Definition
SAP MDG (Master Data
Governance)
SAP component for the centralized, rule-based maintenance and
release of master data (material, BP, etc.).
MDG governance Process-driven maintenance and approval of master data using
workflows, rules, and roles.
MDG Consolidation MDG component for consolidating master data from multiple
sources, including matching and merging.
Staging area Intermediate area in MDG Consolidation where data is checked
before being transferred to governance.
Matching rules Rules for identifying duplicates based on defined fields (e.g.,
name + address).
Merge rules Rules for prioritizing fields when merging duplicate data records.
Change request (CR) Master data change process in the governance process with
approval steps.
Data steward Technical role for data quality and operational responsibility for
maintenance.
Master Data Owner Strategically responsible for technical control and decision-
making regarding master data.
SAP Data Services (DS) ETL tool for extracting, transforming, and loading data (also for
migration to MDG).
SLT (SAP Landscape
Transformation)
Real-time data replication tool for transferring data from SAP
source systems.
DRF (Data Replication
Framework)
SAP component for distributing data maintained in MDG to target
systems.
Fiori apps Role-based web applicationsfor maintaining, approving, and
monitoring master data in MDG.
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Term Definition
ETL (Extract, Transform,
Load)
Process chain for data migration from source systems to a target
system.
UAT (User Acceptance
Test)
Technical acceptance test to ensure requirements and data
quality prior to go-live.
Cutover Planned transition from test to production system, including final
migration and activation.
Hypercare Support phase immediately after go-live for monitoring,
correction, and stabilization.
Golden Record The centrally maintained, consolidated, and approved master
data record in MDG.
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21 Literature & sources
21.1 Official SAP documentation & resources
1. SAP Help Portal – SAP Master Data Governance
https://help.sap.com/mdg
→ Documentation on data models, governance, MDG consolidation,
Fiori applications.
2. SAP Note 1685257 – SAP MDG Consolidation
→ Technical information on activating and configuring the
consolidation functions.
3. SAP Community Blog – MDG Best Practices
https://community.sap.com/topics/master-data-governance
→ Experience reports, solution scenarios, and
implementation tips.
4. SAP Press Book: "Master Data Governance with SAP MDG"
Authors: Andreas Bauer, Michael Göttert, Lars Rölz, ISBN: 978-1-
4932-1850-7
Rheinwerk Verlag, 2021
→ In-depth introduction to functions, processes, architecture, and
customization.
21.2 Technical literature & methodology
5. Data Management Body of Knowledge (DMBOK)
Data Management Association International (DAMA-DMBOK)
→ Professionally recognized standard for data quality and
governance methodology.
6. DSAG guidelines on master data management and migration
DSAG – German-speaking SAP User Group
→ Practical guidelines and experience reports from the industry.
https://help.sap.com/mdg
https://community.sap.com/topics/master-data-governance
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21.3 � Reference books & manuals
Title Author Contents
Handbook on SAP Master Data
Governance – Data Migration
Hans-Georg
Emrich
Practical guide with best practices for MDG
data migration and consolidation, including
mass processing
System consolidation and
data migration as success
factors
Sabine
Wachter,
Thomas
Zaelke
Case studies and solutions for SAP system
landscapes during mergers and restructuring
21.4 � Online resources & blogs
• SAP MDG Data Migration – Part 1: Blog series with detailed explanations of tools such
as Data Import Framework, SAP Data Services, Smart Data Integrator, and MDG
Consolidation.
• SAP Help Portal – MDG Consolidation: Official SAP documentation on
configuration and process control for consolidation.
• SAP Master Data Governance product documentation: Overview of versions,
functions, and implementation notes.
21.5 � Content that is particularly helpful
• Tool comparisons: Which data loading tools are suitable for MDG Central
Governance vs. MDG Consolidation
• Best practices: Sequence of data migration via staging area to ensure
data quality
• Case studies: Harmonization of system landscapes during company mergers
21.6 Tool-specific sources
7. SAP Data Services – Migration Guides & Best Practices
SAP Help Portal: https://help.sap.com/dataservices
→ Configuration of ETL routes and migration to MDG projects.
8. SAP Fiori Apps Library – MDG-relevant applications
https://fioriappslibrary.hana.ondemand.com
→ Technical details, app IDs, and usage scenarios.
21.7 Internal project sources
9. Internal workshops, interviews with specialist departments
and IT stakeholders
https://community.sap.com/t5/technology-blog-posts-by-members/sap-mdg-data-migration-part-1/ba-p/13437535
https://bing.com/search?q=Literatus%2Bzu%2BMigrations-%2Bund%2BKonsolidierungskonzept%2Bin%2BSAP%2BMDG
https://bing.com/search?q=Literatus%2Bzu%2BMigrations-%2Bund%2BKonsolidierungskonzept%2Bin%2BSAP%2BMDG
https://help.sap.com/dataservices
https://fioriappslibrary.hana.ondemand.com/
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10. Mapping lists, validation rule catalogs, customizing tables from the
project context
11. Reports from data quality analyses and consolidation runs4.9.7 Data backup.....................................................................................................................33
4.9.8 Governance: ....................................................................................................................33
4.10 C ̨* Technical implementation with SAP MDG data models MAT & BP.........................33
4.10.1 Ç 1. Use of predefined SAP data models.....................................................................33
4.10.2 ⬛–³ 2. Extension & adaptation.............................................................................34
4.10.3 ”" 3. Integration & Governance......................................................................................34
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4.10.4 # 4. Quality assurance & monitoring..........................................................................34
4.11 C̨* MDG consolidation for duplicate checking & harmonization across multiple
source systems ................................................................................................................................35
4.12 ‘’z Data import from multiple source systems...................................................................35
4.12.1 Objective ..........................................................................................................................35
4.12.2 Source system types........................................................................................................35
4.12.3 Import paths in MDG Consolidation.................................................................................36
4.12.4 Technical requirements ...................................................................................................36
4.12.5 Success factors ...............................................................................................................36
4.12.6 , 2. Duplicate checking & matching strategies ....................................................37
4.12.7 ç_'±́ ’ 3. Golden record & harmonization ...............................................................37
4.12.8 .̧̇ •'7s 4. Replication & integration .....................................................................37
4.12.9 ̂·•⬛̇̇ Iterative consolidation & validation before go-live – a success factor for SAP
MDG projects................................................................................................................................37
4.12.10 ^̀ 2 . Validation & quality assurance .................................................................38
4.12.11 , 3. Dry runs & simulations ............................................................................38
4.12.12 |̄⬛ 4. Acceptance & Approval ...........................................................................38
4.12.13 '̧ .7•ṡ 5. Final go-live.......................................................................................................39
4.13 Success factors ....................................................................................................................40
4.14 ‘
’
z
Elimination of duplicates and inconsistencies ...............................................................40
4.14.1 Objective ..........................................................................................................................40
4.14.2 ●◎́ ....................................................................................................................................40
4.14.3 •̇ Q Typical causes of duplicates and inconsistencies.......................................................41
4.14.4 ‘z’ Methods for duplicate detection...................................................................41
4.14.5 Ç Consolidation strategies...........................................................................................41
4.14.6 .•9̇ e tools and technologies ...................................................................................41
4.14.7 Approach..........................................................................................................................42
4.14.8 Technical implementation ................................................................................................42
4.14.9 Success factors................................................................................................................42
4.15 ‘’z Introduction of SAP MDG as the leading system for master data...................................43
4.15.1 Objective ..........................................................................................................................43
4.15.2 Approach..........................................................................................................................43
4.15.3 Technical implementation ................................................................................................44
4.15.4 Success factors................................................................................................................44
4.16 Establishment of governance processes for future data maintenance..........................44
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4.16.1 Objective ..........................................................................................................................44
4.16.2 Approach..........................................................................................................................45
4.16.3 Technical implementation ................................................................................................45
4.16.4 Success factors................................................................................................................45
5 ‘’z Scope .....................................................................................................................................46
5.1 ‘’z Data objects – material master data............................................................................46
5.1.1 Purpose............................................................................................................................46
5.1.2 Scope of data objects ......................................................................................................46
5.1.3 Special aspects................................................................................................................47
5.2 ‘’z Data objects – Business partners................................................................................47
5.2.1 Target...............................................................................................................................47
5.2.2 ‘’z Scope of data objects ...............................................................................................47
5.2.3 Special aspects................................................................................................................48
5.3 ........................................................................................................................................4
5.3.1 Target...............................................................................................................................48
5.3.2 System categories............................................................................................................48
5.3.3 Challenges & measures...........................................................................................49
5.4 ‘ ’z Target system...........................................................................................................49
5.4.1 Goal .................................................................................................................................49
5.4.2 Components of the targetsystem ....................................................................................49
5.4.3 System landscape............................................................................................................50
5.5 z’‘ Not within scope ......................................................................................50
5.5.1 Target...............................................................................................................................50
5.5.2 Not included in scope: .....................................................................................................51
6 ‘’z Project approach ....................................................................................................................52
6.1 Objective...............................................................................................................................52
6.2 Phase model.........................................................................................................................52
6.2.1 Analysis phase .................................................................................................................52
6.2.2 Design phase ...................................................................................................................52
6.2.3 Build phase ......................................................................................................................52
6.2.4 Test phase .......................................................................................................................53
6.2.5 Go-live and hypercare......................................................................................................53
6.3 Roles and responsibilities.....................................................................................................53
7 Data analysis .................................................................................................................54
7.1 Goal......................................................................................................................................54
7.2 Data sources & inventory .............................................................................................54
7.3 Data profiling ........................................................................................................................54
7.4 Duplicate analysis.................................................................................................................54
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7.5 Consistency analysis ............................................................................................................55
8 ‘ ’z mapping concept ................................................................................................................56
8.1 Objective...............................................................................................................................56
8.2 Structure of the mapping tables............................................................................................56
8.3 Mappings for material master data .......................................................................................56
8.4 Mappings for business partners ...........................................................................................57
8.5 Harmonization keys ..............................................................................................................57
8.6 Tools & implementation ................................................................................................57
9 ‘’z Validation concept ...................................................................................................................58
9.1 Objective...............................................................................................................................58
9.2 Validation categories ............................................................................................................58
9.2.1 Mandatory field checks.....................................................................................................58
9.2.2 Field combination rules.....................................................................................................58
9.2.3 Format and value range checks.......................................................................................58
9.2.4 Duplicate detection (via MDG consolidation) ....................................................................59
9.3 Validation tools .....................................................................................................................59
9.4 Validation strategy ................................................................................................................59
9.5 Success factors ....................................................................................................................59
10 'z' Implementation and use of SAP MDG Consolidation.....................................................61
10.1 Objective...............................................................................................................................61
10.2 Target image & role in the process...............................................................................61
10.3 Main functions of MDG Consolidation ..................................................................................62
10.4 Differences from MDG Governance .....................................................................................62
10.5 Phases of use in the project .................................................................................................63
10.6 Advantages in the project context.........................................................................................63
10.7 Success factors ....................................................................................................................63
11 ‘’z MDG Consolidation Setup....................................................................................................64
11.1 Objective...............................................................................................................................64
11.2 Staging Area.........................................................................................................................64
11.3 Matching rules ......................................................................................................................65
11.4 Merge rules...........................................................................................................................65
11.5 Consolidation runs ................................................................................................................65
11.6 Tools & Technologies ...................................................................................................65
11.7 Success factors ......................................................................................................................6
12 ‘’z migration strategy...............................................................................................................67
12.1 Goal......................................................................................................................................67
12.2 Migration objects...................................................................................................................67
12.3 Procedure model ..................................................................................................................68
12.3.1 Initial migration .................................................................................................................68
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12.3.2 Delta handling ..................................................................................................................68
12.3.3 Post-migration correction:.................................................................................................68
12.4 Migration technologies..........................................................................................................68
12.5 Migration controls .................................................................................................................68
12.6 Success factors ....................................................................................................................69
13 ‘ z’ Test strategy.........................................................................................................70
13.1 Objective...............................................................................................................................70
13.2 Test types.............................................................................................................................70
13.2.1 Unit tests ..........................................................................................................................70
13.2.2 Integration tests (end-to-end)............................................................................................70
13.2.3 User acceptance tests (UAT)...........................................................................................70
13.3 Test scope............................................................................................................................71
13.4 Test environment..................................................................................................................71
13.5 Test documentation ..............................................................................................................71
13.6 Success factors ....................................................................................................................71
14 z’‘ Cutover & Go-Live..................................................................................................72
14.1 Objective...............................................................................................................................72
14.2 Cutover preparation ..............................................................................................................72
14.2.1 Cutover plan.....................................................................................................................72
14.2.2 System freeze: .................................................................................................................72
14.2.3 Final validation .................................................................................................................72
14.3 Productive migration .............................................................................................................73
14.4 Go-live ..................................................................................................................................73
14.5 Hypercare phase ..................................................................................................................73
14.6 Success factors ....................................................................................................................73
15 'z' Governance & Operation...........................................................................................74
15.1 Objective...............................................................................................................................74
15.2 Roles & Responsibilities .......................................................................................................74
15.2.1 Data stewards..................................................................................................................74
15.2.2 Master Data Owner:.........................................................................................................74
15.2.3 Specialist departments .....................................................................................................74
15.2.4 IT support / Operations ....................................................................................................74
15.3 Operational processes..........................................................................................................75
15.3.1 Change request management: ........................................................................................75
15.3.2 Monitoring & Alerts ..........................................................................................................75
15.3.3 Master data quality ...........................................................................................................75
15.4 MDG consolidation operations..............................................................................................75
15.5 Change management ...........................................................................................................75
15.6 Success factors ....................................................................................................................76
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16 Implementation of Fiori in the context of migration and consolidation with SAP MDG .......77
16.1 Objective...............................................................................................................................77
16.2 Fiori as a bridge between user-friendliness and governance.........................................77
16.3 ³⬛ – Fiori in consolidation & migration.........................................................................77
16.4 z‘’\ Technical requirements............................................................................................78
16.5 ú,J◆ Migration tools with Fiori connection..............................................................................78
16.6 ●◎́ Advantages of Fiori integration....................................................................................78
16.7 Role of Fiori in the MDG context ..........................................................................................78
16.8 Integration into the migration process...................................................................................79
16.8.1 During migration:..............................................................................................................79
16.8.2 After migration..................................................................................................................79
16.9 Technical aspects.................................................................................................................79
16.10 Success factors ....................................................................................................................79
16.11 Relevant SAP Fiori apps in the context of SAP MDG & Migration ........................80
16.12 ⬛ Notes on use ..................................................................................................................81
17 ‘’z Technical tools & infrastructure ....................................................................................82
17.1 ‘‘z Overview of migration tools for SAP MDG & MDG Consolidation..........................82
17.2 ⬛ Application scenarios – rough classification by phase ...................................................83
18 ‘’ z Design of validation rules, derivation rules, and change requests ...........................................83
18.1 Objective...............................................................................................................................8318.2 Validation rules .....................................................................................................................84
18.2.1 Typical validations in the context of migration:.................................................................84
18.2.2 Technical implementation: ...............................................................................................84
18.3 Derivation rules.....................................................................................................................84
18.3.1 Examples of derivations...................................................................................................84
18.3.2 Technical implementation: ...............................................................................................84
18.4 Change requests in the context of migration ........................................................................85
18.4.1 Types of change requests:...............................................................................................85
18.4.2 Process logic....................................................................................................................85
18.5 Success factors ....................................................................................................................85
19 Use of CDQ and DQM in the context of migration and consolidation ..................................86
19.1 Objective...............................................................................................................................86
19.2 What is CDQ? ......................................................................................................................86
19.3 What is SAP DQM?..............................................................................................................87
19.4 Integration into the migration process...................................................................................87
19.5 Advantages ............................................................................................................................8
19.6 Success factors ....................................................................................................................88
20 Appendix – Definitions and glossary (migration & MDG consolidation)................................89
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21 Bibliography & Sources...................................................................................................9
21.1 Official SAP documentation & resources .....................................................................91
21.2 Technical literature & methodology ..............................................................................91
21.3 H_] Reference books & manuals.................................................................................92
21.4 Online resources & blogs ...............................................................................................92
21.5 C*̨ Content that is particularly helpful ..................................................................................92
21.6 Tool-specific sources ............................................................................................................92
21.7 Internal project sources.........................................................................................................92
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1 Foreword
The quality of master data is a key success factor for end-to-end business processes,
regulatory compliance, and data-driven decision-making. In the wake of digitalization and
system consolidation, master data governance is becoming increasingly important—
especially in complex system landscapes with multiple source and target systems.
This concept describes the structured development of a central, consistent, and quality-
assured master data platform based on SAP Master Data Governance (MDG). The focus is
on material and business partner data objects (customers, suppliers, addresses), which
are transferred from various SAP and non-SAP source systems, consolidated, and will be
maintained centrally via SAP MDG in the future.
A key component of the project is the use of SAP MDG Consolidation to clean up and
harmonize inventory data. A coordinated approach to duplicate checking, mapping,
validation, and governance processes lays the foundation for sustainable master data quality.
This document serves as a guideline for all stakeholders involved—from project management
to IT and specialist departments to data stewards—and describes the entire process from
analysis to go-live. The aim is not only to successfully implement a one-time data migration,
but also to raise the long-term maintenance and control of master data to a new level of
quality.
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2 Management summary
The introduction of SAP Master Data Governance (MDG) in conjunction with MDG
Consolidation represents a strategic step toward ensuring and controlling master data
quality in the company in the long term. This migration and consolidation concept forms
the basis for the successful transfer of historically grown master data structures to a
central, harmonized platform for material and business partner data.
The project approach is based on a phase-oriented procedure that ranges from data
analysis, mapping, consolidation, and validation to productive transfer and governance
support. The focus is on using MDG Consolidation for duplicate checking and data
harmonization, as well as the use of change requests for the structured release of the
cleaned data into the MDG governance model.
The technical architecture is supplemented by tools such as SAP Data Services, SAP
DQM, CDQ, and a role-based Fiori user interface for efficient and user-friendly master
data maintenance. All validation and derivation rules as well as governance processes
are designed to ensure both migration and regular operations in the long term. This
concept not only ensures the successful migration of legacy data, but also lays the
foundation for a future-proof, rule-based master data organization. The establishment of
clear responsibilities, standardized processes, and automated quality controls is the
key to ensuring consistent, complete, and usable master data in the long term – in line
with a true master data excellence model.
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3 Introduction
This book describes the procedure for migrating and consolidating material and business
partner master data into a central SAP MDG platform. It serves as a blueprint for merging
inventory data and introducing master data governance processes.
3.1 � A migration and consolidation concept in SAP MDG
(Master Data Governance)
A migration and consolidation concept in SAP MDG (Master Data Governance) is
crucial for standardizing master data from different sources and transferring it to a central
system. Here are the key points that such a concept typically includes:
3.1.1 � Consolidation with SAP MDG
• SAP MDG enables the consolidation of decentralized master data sources
using defined process steps.
• The sequence and behavior of these steps can be customized to meet
individual requirements.
• The goal is to enable duplicate detection, data cleansing, and consolidation
.
3.1.2 � Migration to SAP MDG
• Migration involves loading cleaned data into the central MDG system.
• SAP offers various tools for data migration:
o File upload
o Data Migration Cockpit (only for MDG on S/4HANA)
o SAP Data Services
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o Smart Data Integrator / Quality
o Agile Data Preparation
o SOA Services and LSMW for backend functions.
3.1.3 🛠� Customizing & technical requirements
• Configuration is performed using transaction MDCIMG.
• Relevant business functions must be activated (e.g., for customer, vendor, or
material data).
• Authorization objects and roles must be assigned correctly, e.g., for the
workflow system user.
3.2 Recommendations for implementation
• SAP recommends operating MDG as a separate master data hub rather than as a
co-deployment.
• A clear data quality strategy ("Clean & Keep Clean") should be part of the
concept.
• Consolidation and central governance can be carried out in parallel if the
corresponding functions are activated.
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4 � Objective
4.1 Establishment of a harmonized, quality-assured master data base
The objective of this step is to establish a central, uniform master data base for materials and
business partners that is complete, consistent, and free of duplicates. This forms the basis
for efficient master data governance and high data quality in operational business.
C ̨* Establishing a harmonized master data base is a strategic
project that enables companies to use consistent, reliable, and usable data across all
business areas. Here are the most important building blocks for a successful concept:
Establishing a central, consistent master data base is a strategic success factor for data-
driven companies. SAP Master Data Governance (MDG) offers a powerful framework that
takes both technical and organizational aspects into account. Here are the most important
building blocks for setting it up:
4.1.1 � 1. Data consolidation & mass processing
• Consolidation of master data from different source systems
• Cleaning up redundant and inconsistent data
• Use of matching and merging algorithms to identify duplicates
4.1.2 � 2. Central governance
• Definition of roles, responsibilities, and approval processes
• Standardized workflows for creating, changing, and approving master data
• Validation rules to ensure data quality
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4.1.3 � 3. Data quality management
• Definition of quality rules and KPIs for monitoring
• Automated check mechanisms to ensure data integrity
• Dashboards for visualizing quality metrics
4.1.4 � 4. Integration & Harmonization
• Uniform data standards across all business areas
• Synchronization of master data with connected target systems
• Harmonization of customer, supplier, material, and financial master data
4.1.5 � 5. Organizational anchoring
• Involvement of all relevant departments from the outset
• Training and awareness-raising among employees regarding data quality
• Establishment of a continuous improvement process
4.2 � 1. Data harmonization
• Objective: Standardization of master data from different sources and
systems.
• Measures
o Definition of uniform data models and standards.
o Use of classifications such as eCl@ss or Global Product
Classification (GPC).
o Consolidation of redundant data sets and elimination of duplicates.
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4.2.1 � Data harmonization in SAP MDG – overview and key
aspects
Data harmonization within SAP Master Data Governance (MDG) aims to standardize master
data from different sources and make it available consistently across all systems. This is
particularly relevant in complex IT landscapes with decentralized data storage, e.g., after
mergers or in global business processes.
4.2.2 � What does data harmonization mean in SAP MDG?
• Standardization of master data attributes: Uniform definitions for fields such
as name, address, tax data, etc.
• Reconciliation and consolidation: Identification and consolidation of duplicates
using matching algorithms
• Survival rules: Selection of the "best" data record from multiple sources based
on defined rules
• Distribution of harmonized data: Provision of consolidated master data for all
connected systems
4.2.3 🛠� Technical implementation
• Use of MDG consolidation for mass processing and duplicate
detection
• Combination of consolidation and central governance to control data quality and
distribution
• Integration with SAP ERP or as a separate hub system possible
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4.2.4 📈 Advantages of data harmonization
• Higher data quality and consistency
• More efficient business processes thanks to reliable master data
• Better decision-making thanks to a uniform database
• Reduction of redundant data maintenance efforts
4.3 � 2. Data quality management
• Objective: To ensure the accuracy, completeness, and timeliness of
master data.
• Measures
o Introduction of quality metrics (e.g., duplicate rate,
completeness).
o Regular data checks and cleansing.
o Use of tools such as SAP MDG, zetVisions, or data quality
dashboards.
o Avoid "garbage in, garbage out" through clear input rules and training.
4.3.1 ���� quality management with SAP MDG – overview and key features
Effective data quality management (DQM) is the backbone of every successful master data
strategy. SAP Master Data Governance (MDG) offers comprehensive functions for
systematically securing, monitoring, and continuously improving the quality of business-
critical master data.
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4.3.2 🛠� Core functions in data quality management with SAP MDG
• Definition of quality rules: Business rules are defined centrally to ensure that
master data is consistent, complete, and valid.
• Validation during data entry: Entries are checked directly when master data is
created or changed – e.g., for mandatory fields, format specifications, or
duplicates.
• Automated checking mechanisms: Rule-based checks identify incorrect or
incomplete data records and initiate correction processes.
• Dashboards & KPIs: Visualization of data quality metrics for monitoring
and controlling master data quality.
• Audit trail & change tracking: Every change is documented in a
traceable manner, which increases compliance and transparency.
4.3.3 � Benefits for businesses
• Greater data reliability for operational and strategic decisions
• Reduction of manual rework through automated checks
• More efficient processes thanks to consistent master data
• Faster integration of new data sources during mergers or
system changes
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4.4 🏗� 3. Governance & Organization
• Goal: Establish clear responsibilities and processes.
• Measures:
o Establishment of a master data board or data stewardship.
o Define roles such as data owner, data steward, and data architect.
o Define approval and change processes.
4.4.1 🏛� Governance & organization in the context of SAP
MDG – the foundation for sustainable master data quality
In the SAP Master Data Governance (MDG) environment, a clearly defined governance
structure and a coordinated organization are crucial for controlling master data processes
efficiently, transparently, and in compliance with regulations. Here are the key aspects:
4.4.2 � Governance structure – Who decides what?
• Roles & responsibilities: Clear assignment of tasks such as data
maintenance, approval, quality assurance, and escalation
• Rules & policies: Uniform guidelines for data modeling, validation, and
use
• Approval processes: Workflow-based approval steps for creating new master
data and making changes
4.4.3 �� Organizational anchoring – How is MDG implemented?
• Data owner& stewardship models: Business departments take
responsibility for "their" data objects
• Centralized vs. decentralized governance: Depending on the company
structure, MDG can be operated as a central hub or in a hybrid form
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• Change management & training: Raising employee awareness of data
quality and governance processes
4.4.4 � Interaction with IT & business
• Business-driven requirements: Governance must support the operational and
strategic goals of the departments
• Technical implementation in SAP MDG: Workflows, validation rules, and
role models are mapped in the system
• Continuous improvement: Governance is regularly reviewed and adapted to new
requirements
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4.5 MDG system landscape
The MDG system landscape is an essential architectural component of your migration and
consolidation concept in SAP MDG. It describes systems, roles, data flows, interfaces,
and the technical structure of the overall scenario.
4.5.1 Object
• Presentation of the technical infrastructure for the migration, consolidation, and
governance of master data with SAP MDG.
• Clear separation of system roles (source systems, consolidation platform,
target systems) and overview of interfaces and data flows.
4.5.2 Component overview
System type Role in the process
Source systems SAP ERP (ECC), SAP S/4HANA, non-SAP systems
MDG system Central consolidation and governance system
Target systems
(distributors)
SAP ERP/S/4HANA systems for replication of approved master data
4.5.3 System landscape roles
4.5.3.1 SAP MDG (central system):
o Includes MDG consolidation, MDG governance (MAT & BP), DRF (Data
Replication Framework).
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o Data staging, matching & merging, CR processes, approvals, and
replication.
4.5.3.2 Source systems:
o SAP ERP systems (MARA, KNA1, etc.), non-SAP sources (e.g., CRM,
MDM, Excel).
o Data is extracted via ETL, web services, or files.
4.5.3.3 ETL/middleware layer:
o e.g., SAP Data Services, SLT, or flat file import via MDG.
o Ensures conversion, mapping, and transfer to the staging area.
4.5.3.4 Target systems:
o Systems in which the master data released by MDG is used (e.g., SAP
ERP).
o Supply via DRF/IDoc/web services.
4.5.4 System connections & interfaces
Source→ MDG Technology
SAP ERP RFC, IDoc, SLT, Data Services
Non-SAP (e.g., MDM, CRM, Excel) SAP Data Services, file upload, web services
MDG Consolidation→ MDG Governance Internal transfer via consolidation framework
MDG→ Target system(s) DRF, IDoc, web service, ALE
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4.5.5 Client and environment structure
Environment Purpose
Development (DEV) Customizing, rule development, mapping logic
Quality (QAS) Test migration, UAT, consolidation runs
Production (PRD) Final data import, governance operations
4.5.6 Visualization (can be supplemented with graphics)
A typical landscape shows:
• Multiple source systems→ ETL→ MDG staging→ Consolidation →
Governance → Replication → Target system(s)
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4.6 MDG Hub or co-deployment –
architecture decision
The topic of "MDG Hub vs. Co-Deployment" is a key architectural decision in the
SAP MDG implementation and must be considered early on as part of a migration
and consolidation concept. It has a lasting impact on the system landscape,
interfaces, replication logic, and operations.
4.6.1 Objective
• Evaluate the system architecture for SAP MDG: central hub system
vs. co-deployment (MDG in the operational ERP/S/4 system).
• Definition of the chosen strategy as part of migration and
consolidation.
4.6.2 MDG hub implementation
Description:
MDG runs as a standalone, central system independent of the operational
ERP or S/4HANA system.
Advantages:
• Central master data maintenance for multiple target systems possible
• Clear system boundaries (staging, governance, replication)
• Decoupling of release cycles
• Ideal for heterogeneous system landscapes with multiple ERP systems
Challenges
• Replication effort via DRF required
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• Expansion projects require interface management
• More complex authorization and monitoring structure
4.6.3 Co-deployment (embedded in ERP/S/4HANA)
Description:
SAP MDG is installed and operated directly in the leading operational ERP or
S/4HANA system.
Advantages
• No need to replicate data maintenance in the same system
• Technically simple setup
• Faster time-to-value for smaller scenarios
• Direct data availability in operational use
Challenges
• No client or system separation
• Technical risks during release changes (ERP vs. MDG)
• Less suitable for multiple ERP systems
4.6.4 Decision criteria for the migration project
Criterion Recommended
architecture
Multiple ERP source systems MDG Hub
Central consolidation required MDG Hub
Only one leading S/4 system Co-deployment possible
High demand for in-house
developments
MDG Hub preferred
Low interface complexity Co-deployment sufficient
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2.6.4 Project decision made
In many projects, the architecture decision was made in favor of an MDG Hub
implementation because the migration and consolidation involves master data from multiple
SAP and non-SAP systems. Central control via a separate MDG system ensures scalability,
transparency, and independence from operational releases.
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4.7 🖥� Technological infrastructure in the context of SAP
MDG – the foundation for efficient
master data management
The technological infrastructure is the backbone of every successful SAP Master Data
Governance (MDG) implementation. It ensures that master data processes run efficiently,
scalably, and securely – both in on-premise and cloud scenarios.
• Goal: Support through powerful systems and interfaces.
• Measures:
o Use of a central master data hub (e.g., SAP MDG, Informatica MDM).
o Integration via APIs, SOA services, or GDSN for data
exchange.
o Use of cloud platforms for scalability and flexibility.
4.7.1 � 1 . System architecture & deployment models
• SAP MDG on S/4HANA: Integrated into the digital core platform, ideal for
centralized governance and real-time processes.
• SAP MDG on ERP 6.0: For companies with a classic SAP landscape.
• Cloud Edition: SaaS model for business partner governance based on the SAP
One Domain Model.
• Hub vs. co-deployment: MDG can be operated as a separate master data
hub or integrated into operational systems.
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4.7.2 � 2. Integration & Interfaces
• SOA services & APIs: For connecting external systems and real-time data
distribution.
• SAP Business Technology Platform (BTP): Enables hybrid scenarios and
expandability.
• Third-party integration: e.g., with Informatica, Talend, or cloud platforms via
standardized connectors.
4.7.3 🛠� 3. Extensibility & customization
• Preconfigured data models: For customers, suppliers, materials, etc.
• Custom data models: You can define your own domains using the MDG
framework.
• Workflow engine & validation rules: Adaptable to
company-specific requirements.
4.7.4 � 4. Security & compliance
• Audit trails & change tracking: For transparency and
traceability.
• Access control via roles & permissions: Protectionof sensitive
master data.
• Cloud security: SAP Trust Center provides information on data protection and
certifications.
4.7.5 📈 5. Monitoring & Performance
• Dashboards for data quality & process metrics
• Mass data processing: Optimized for large amounts of data with
consolidation functions
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• Scalability: Infrastructure adapts to growing requirements – locally or in the
cloud.
4.8 � Continuous improvement with SAP MDG – More than just
master data maintenance
In the context of SAP Master Data Governance (MDG), continuous improvement means not
only optimizing master data processes, but also strategically developing data quality,
governance structures, and system integration. Here are some key ways in which SAP MDG
supports this approach:
• Goal: Sustainable securing and further development of the master data base.
• Measures
o Establishment of a continuous improvement process (CIP).
o Monitoring data quality with KPIs.
o Feedback loops from specialist departments and IT.
4.8.1 � 1. Data quality management as a driver
• Automated checking mechanisms: SAP MDG offers functions for
duplicate checking, validation, and standardization of data.
• Consolidation & harmonization: Master data from different sources is
standardized and checked for consistency.
• Monitoring & KPIs: Dashboards for monitoring data quality and process
metrics help identify weaknesses early on.
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4.8.2 � 2. Governance processes with a learning curve
• Change request-based workflows: Each master data record goes through defined
approval processes that can be continuously adapted.
• Feedback loops: User interactions and error analyses are incorporated into
the optimization of workflows.
• Role-based permissions: Governance is strengthened through
clear responsibilities.
4.8.3 � 3. Agile project methodology & quick wins
• Best practice content: Predefined processes and data models enable quick wins
and iterative improvements.
• Sprints & sub-projects: Small, focused improvement cycles promote
acceptance and efficiency.
• Synchronization with S/4HANA transformation: MDG can be used as an
intermediate step or an integral part of the migration.
4.8.4 � 4. Extensibility & innovation
• Custom data models: Companies can define their own data objects and expand
them gradually.
• Integration with SAP BTP: Enables the use of AI, machine learning, and other
innovations for data analysis and improvement.
4.9 Approach
4.9.1 Harmonization:
Standardization of data structures and key fields (e.g., material types, units of measure).
Definition of naming and attribute standards and creation of mapping tables for source and
target systems.
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4.9.2 📦 Standardization of data structures and key fields in SAP
MDG – basis for consistent
master data
The harmonization of data structures is a central component of any successful SAP Master
Data Governance (MDG) strategy. It creates the basis for cross-system consistency, better
data quality, and smooth integrations.
4.9.3 � 1. Standardization of key fields
• Material types & units of measurement: Uniform definitions and codes
(e.g., MATNR, MEINS) prevent duplicates and misunderstandings.
• Cross-domain standards: Uniform fields for customers, suppliers,
materials, etc. simplify maintenance and integration.
• Reference data models: SAP One Domain Model as the basis for consistent
data structures in cloud and on-premise systems.
4.9.4 2. Name and attribute standards
• Naming conventions: Uniform naming of fields, objects, and attributes (e.g.,
CamelCase, prefixes such as MAT_, BP_).
• Attribute definitions: Clear rules for mandatory fields, data types, lengths, and
validation logic.
• Documentation & Governance: Standards are documented centrally and
reviewed regularly.
4.9.5 � 3 . Mapping tables for source and target systems
• Source-to-target mapping: Tables for assigning fields between systems –
including transformation rules.
• ETL processes (extract, transform, load): Automated data transfer with
conversion of formats and values.
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• Mapping specifications: Documents with field assignments, data types,
rules, and business context.
4.9.6 � 4. Tools & best practices
Area Tools & Methods
Data integration SAP Data Services, Informatica, Talend
Mapping documentation Excel, SAP Solution Manager, Data Catalogs
Validation & Quality SAP MDG DQM, Business Rules Framework (BRF+)
Governance SAP BTP, Fiori apps for maintenance & release
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4.9.7 Data backup
Implementation of validation rules (mandatory fields, value ranges), derivation logic, and
automatic duplicate detection with SAP MDG Consolidation.
4.9.8 Governance
Introduction of workflows for checking and releasing master data, role model with data
stewards and master data owners, and continuous monitoring.
4.10 � Technical implementation with SAP MDG
data models MAT & BP –
• Use of SAP MDG data models MAT (material) and BP (business partner).
• Use of MDG Consolidation for duplicate checking and harmonization across multiple
source systems.
• Iterative consolidation runs and validations before final go-live.
The MAT (material) and BP (business partner) data models are central components of
SAP Master Data Governance (MDG) and enable structured harmonization and
maintenance of master data.
4.10.1 � 1 . Use of predefined SAP data models
• MAT: Contains fields for material master data such as basic data, scheduling,
purchasing, sales, etc.
• BP: Includes master data for business partners, customers, and suppliers –
including roles, addresses, and bank details.
• Both models can be extended using MDG customizing (transaction
MDGIMG).
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4.10.2 � 2. Extension & customization
• Customizing via Data Model Editor: Add fields, define validation rules.
• Use of replication models (DRF): Control data distribution to target systems.
• Key mapping & value mapping: Ensure consistent identities and values
across systems.
4.10.3 � 3. Integration & Governance
• SOA services for BP and MAT for real-time integration.
• Business Rules Framework (BRF+) for workflow validation and control.
• Change requests & workflows: Control of data maintenance processes with
approval logic.
4.10.4 � 4. Quality assurance & monitoring
• Use of data quality dashboards to monitor harmonization.
• Audit trails & version history for traceability.
• KPIs for evaluating data quality (e.g., duplicate rate, completeness).
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4.11 � MDG consolidation for duplicate checking & harmonization across multiple
source systems
The SAP MDG Consolidation component is specially designed to merge master data
from different source systems, identify duplicates, and generate a consolidated "golden
record." Here is a structured overview of the technical implementation:
4.12 Data import from multiple source systems
Data import from multiple source systems is a key issue in the migration and
consolidation concept with SAP MDG, especially when using MDG Consolidation.
4.12.1 Object
• Description of the technical procedure for transferring master data from various
source systems to the SAP MDG staging environment.
• Ensuring complete, consistent, and traceable data transfer as a basis
for consolidation.
4.12.2 Sourcesystem types
System type Examples Special
SAP ERP (ECC,
S/4)
MARA, KNA1, LFA1 Standard tables, structured data
Non-SAP systems
CRM, MDM, Excel, CSV,
Access
Unstructured formats, proprietary fields
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4.12.3 Import paths in MDG Consolidation
1. SAP Data Services (ETL):
o Transformation and harmonization before loading
o Option for validation & field mapping
2. Flat file upload (CSV, XML):
o Particularly suitable for pilot/test migrations
o Format checking and field mapping via upload templates
3. SLT (SAP Landscape Transformation):
o Real-time transfer from SAP ERP
o More suitable for regular operations than for mass migration
4. Web services/API:
o Ideal for integration with non-SAP sources
o Automated transfer and data verification possible
4.12.4 Technical requirements
• Data from all source systems must meet the following criteria:
o Unique source system identifier
o Mapping capability to the MDG data model (MAT/BP)
o Minimum number of mandatory fields
o Format conformity (e.g., country codes, date formats)
• Import into the staging area of MDG Consolidation, where:
o Check for duplicates
o Starting point for matching/merging
o Optional pre-validation
4.12.5 Success factors
• Clear responsibilities for data extraction for each source system
• Documentation of data structures and field assignments
• Early setup and testing of ETL routes
• Audit-proof logging of imports with technical source
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• Iterative test runs for each source system before productive consolidation
4.12.6 � 2 . Duplicate checking & matching strategies
• Use of fuzzy logic for cross-field similarity recognition (e.g., name, address, tax
number).
• Configuration of matching rules in Customizing – including weighting and
threshold values.
• Clearing monitor for manual evaluation and decision on merging.
• Option to integrate external tools such as SAP Data Quality Management
or Marlin MDC.
4.12.7 🏆 3. Golden Record & Harmonization
• Definition of survival rules: Which values are adopted in the event of
conflicts?
• Merging of views (e.g., company code, purchasing organization) to form a
consistent master data record.
• Audit trail & history for traceability of consolidation steps.
4.12.8 � 4. Replication & integration
• Use of the DRF framework to distribute consolidated data to target
systems.
• Support for BP and MAT data models during harmonization.
• Real-time integration via SOA services or batch replication.
4.12.9 � Iterative consolidation & validation before go-live – a success factor for
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SAP MDG projects
• Multiple consolidation cycles with data from different source systems (e.g., ERP,
CRM).
• Each run serves to refine the matching logic and improve duplicate detection.
• Document results: Which data records were merged, which remained unclear?
4.12.10 � 2. Validation & Quality Assurance
• Use of BRF+ rules for automated checking of consistency, mandatory
fields, and format errors.
• Manual validation by data stewards in the clearing monitor.
• Define KPIs: e.g., duplicate rate, error rate, completeness.
4.12.11 � 3. Dry runs & simulations
• Conduct dry runs to simulate the go-live process.
• Test replication to target systems via DRF.
• Validate the golden records: Are the consolidated data records complete and
correct?
4.12.12 📋 4. Acceptance & release
• Technical approval by business owners and data governance.
• Technical approval by IT – including interfaces, performance, logging.
• Documentation of all approvals and lessons learned.
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4.12.13 � 5. Final go-live
• Execution of the final consolidation run with productive data.
• Activation of replication in real time or batch mode.
• Start of the hypercare phase for stabilization and troubleshooting.
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4.13 Success
• Early definition of harmonization requirements and mandatory fields.
• Close cooperation with specialist departments to define standards.
• Automated quality assurance through validation and duplicate checks.
• Regular monitoring and KPI reporting after migration.
4.14 Elimination of duplicates and inconsistencies
The elimination of duplicates and inconsistencies is a central component of the
migration and consolidation concept.
Cleaning up redundant and inconsistent data is a key step in data migration and consolidation
in SAP Master Data Governance (MDG). Here are some proven approaches and methods:
4.14.1 Objective
• Ensure that the master data base is free of redundant and
contradictory information.
• Avoid multiple entries (e.g., same materials in different systems).
• Establish consistency in all attributes for material and business partner data.
4.14.2 � Goals of the cleanup
• Improve data quality and decision-making basis
• Avoid redundant information and costs
• Uniform view of customers, suppliers, or products
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4.14.3 🔍 Typical causes of duplicates and inconsistencies
• Multiple entries by different systems or users
• Different spellings, formats, or fields (e.g., "Müller" vs.
"Mueller")
• Errors in data transfers during migrations
• Incorrect data transfers during migrations
4.14.4 🛠� Methods for duplicate detection
• Fuzzy matching: Recognition of similar data records despite minor differences
• Phonetic algorithms: e.g., Soundex or Cologne phonetics
• String comparisons: bi-/tri-string analysis, Levenshtein distance, Jaro-Winkler
• Heuristic methods: combination of rules and probabilities
4.14.5 � Consolidation strategies
• Record linkage: Merging of data records that represent the same object
• Golden Record: Creation of a consolidated, reliable master data
record
• Negative check: Comparison with block lists or blacklists
• MDG validation rules: Ensuring consistent data models
4.14.6 � Tools and technologies
• SAP MDG offers integrated functions for duplicate detection and merging
• External deduplication software or AI-powered self-service solutions such as
Kobold AI
• Data quality dashboards for monitoring and control
https://www.kobold.ai/daten-dubletten-entfernen/
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4.14.7 Approach
4.14.7.1 Analysis phase:
o Duplicate analysis per source system.
o Definition of matching criteria (e.g., material number+ , description, BP
name + address).
4.14.7.2 MDG consolidation:
o Use of the staging area to consolidate data from multiple systems.
o Use of matching rules to identify potential duplicates.
o Merge rules for prioritizing attributes.
4.14.7.3 Cleaning runs:
o Iterative consolidation runs with departmental approval.
o Manual resolution of conflicts (e.g., different addresses,
classifications).
4.14.7.4 Validations:
o Format and value range checks to avoid inconsistencies.
o Field combination rules for critical attributes (e.g., tax code+ , country).
4.14.8 Technical implementation
• MDG consolidation with match and merge rules.
• Use of SAP Data Services for pre-validations.
• Custom BAdIs for project-specific duplicate checks.
4.14.9 Success
• Clear definition of duplicate and consistency criteria.
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• Early coordination with specialist departments on merge logic.
• Iterative approach with multiple consolidation runs.
• Continuous duplicate checkingMais conteúdos dessa disciplina
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