Background of SAP & MDM
Master Data Management (MDM) is a vital component of modern Enterprise Resource Planning (ERP) systems. SAP has become a leading platform for organizations aiming to efficiently manage their master data.
This article offers an overview of the master data architecture within SAP systems, highlights key SAP modules, and examines the functionalities of SAP MDM and SAP MDG.
We will also explore the limitations of these solutions and the challenges organizations encounter when transitioning to S/4HANA.
Understanding SAP Architecture
Before diving into SAP MDM and SAP MDG, it's important to understand the overall architecture of SAP without getting too technical, as well as the primary purpose of maintaining an ERP system.
ERP systems like SAP, Oracle JD Edwards, Infor, and others exist to document, automate, systematize, analyze, and standardize business processes across an organization.
Simply put, companies aim to organize and standardize most business processes to implement efficient workflows and to evaluate the performance and results of past operations.
This involves creating “templates” for various business processes, primarily categorized by business functions and then by industry.
ERP platforms like SAP provide numerous such “templates” tailored for different business functions, with variations specific to each industry. In SAP terminology, these collections of templates are called modules.
Modules in SAP
Essentially, modules are ready-made software solutions designed to handle repetitive business processes within the same function across different companies, or for specific industry use cases.
In SAP, there are generally two main types of modules: functional and technical.
- Technical Modules
Technical modules concentrate on the customization, integration, and development of SAP systems. They play a crucial role in ensuring the proper implementation and optimization of functional modules. SAP MDM is considered a technical module that operates across functional modules to effectively manage master data.
Key technical modules include:
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SAP Basis – Provides the foundational layer for SAP system administration and performance management.
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SAP ABAP (Advanced Business Application Programming) – Used to customize SAP applications.
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SAP NetWeaver – Facilitates integration across SAP and non-SAP systems.
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SAP PI/PO (Process Integration/Orchestration) – Enables seamless data exchange across enterprise applications.
- Functional Modules
These modules concentrate on specific business processes, offering tools and configurations tailored to meet organizational needs.
Functional modules manage the operational facets of a business, covering areas such as finance, production, logistics, sales and marketing, and human resources.
Below are some examples of key functional modules within SAP:
(Financial Accounting)
Handles financial transactions, reporting, and analysis.
Submodules: Accounts Payable, Accounts Receivable, General Ledger.
(Material Management)
Manages procurement, inventory, and warehouse functions.
(Controlling)
Focuses on planning, tracking, and monitoring costs.
Submodules: Cost Center Accounting, Profitability Analysis.
(Sales and Distribution)
Handles sales, shipping, billing, and customer relationship management.
(Production Planning)
Covers manufacturing processes like planning, execution, and quality checks.
(Human Resources)
Focuses on employee management, payroll, and recruitment.
(Warehouse Management)
Manages warehouse operations and stock movements.
(Quality Management)
Ensures product quality and compliance.
(Plant Maintenance)
Handles maintenance activities for equipment and machinery.
Master Data & SAP Modules
Understanding the role of Master Data in SAP becomes straightforward when considering its connection to various modules. Certain SAP modules require maintaining a "master database" to streamline operations within their specific business functions.
For instance, the material master database is primarily linked to SAP MM (Material Management), but it also plays a crucial role in SAP PP (Production Planning) and SAP PM (Plant Maintenance).
Common types of master data include:
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Material Master Data – Utilized in SAP MM, SAP PP, and SAP PM for inventory tracking, procurement, and production planning.
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Customer Master Data – Essential for SAP SD and SAP FI, supporting accurate sales transactions and invoicing.
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Vendor Master Data – Used in SAP MM and SAP FI to manage supplier relationships and purchase orders.
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Employee Master Data – Integral to SAP HR, covering payroll processing, tax deductions, and benefits administration.
Maintaining high-quality master data is vital to ensure smooth business operations and to avoid issues such as duplicate records, incorrect pricing, and compliance risks.
Changes to SAP MDM and MDG with S4/HANA
The transition to SAP S/4HANA, SAP’s advanced next-generation ERP platform, has brought significant changes to both SAP MDM (Master Data Management) and SAP MDG (Master Data Governance). With new features, architectural improvements, and a strategic shift, SAP S/4HANA fundamentally alters how master data is managed and governed.
Below is an in-depth overview of the impacts and transformations affecting SAP MDM and SAP MDG in the era of S/4HANA:
SAP MDM and S/4HANA Migration
End of SAP MDM as a Standalone Solution
Phase-Out of SAP MDM
- SAP has officially positioned SAP MDG as the future-focused solution for master data management.
- SAP MDM is no longer actively developed and has limited support in S/4HANA landscapes.
Impact on Organizations
- Businesses using SAP MDM must transition to SAP MDG or alternative solutions as part of their S/4HANA migration strategy.
Challenges with SAP MDM in S/4HANA
- Built on SAP NetWeaver, SAP MDM was primarily designed for on-premise deployments.
- S/4HANA’s focus on real-time data processing, in-memory computing, and cloud readiness exposes SAP MDM’s architectural limitations.
- Lack of native integration with the HANA database negatively impacts SAP MDM’s performance in S/4HANA environments.
SAP MDG and S/4HANA Migration
Enhanced Integration with S/4HANA
- SAP MDG is deeply integrated with S/4HANA, utilizing its in-memory computing capabilities and simplified data model.
- Real-time data validation and governance are enabled through direct connectivity with the HANA database.
Native Support for HANA Database
- SAP MDG on S/4HANA takes advantage of HANA’s high-speed data processing capabilities.
- Features such as real-time duplicate detection, advanced analytics, and enhanced data validation have been significantly improved.
Simplified Data Models
- S/4HANA introduces a streamlined data model that removes redundancies and outdated tables.
- SAP MDG is aligned with this new model, which enhances the efficiency of master data governance.
- For example, the elimination of aggregate tables simplifies master data management processes.
Improved Governance Capabilities
- SAP MDG provides pre-configured governance processes for key master data domains such as Material, Customer, and Vendor.
- Workflows and change request management are more user-friendly and efficient, complementing the usability enhancements in S/4HANA.
Data Quality and Analytics
- Integration with SAP Analytics Cloud (SAC) and S/4HANA enables advanced reporting on master data quality metrics.
- HANA-powered predictive analytics improve data enrichment and help detect errors more effectively.
Functional Enhancements in SAP MDG for S/4HANA
Key New Features in SAP MDG on S/4HANA:
Data Consolidation:
- SAP MDG now supports consolidation scenarios directly within S/4HANA.
- Enables automatic matching, merging, and real-time de-duplication of master data.
Machine Learning Integration:
- Integration with SAP Leonardo and other machine learning frameworks enhances data matching and quality checks.
Improved User Interfaces (UIs):
- MDG uses SAP Fiori apps to provide an intuitive user experience.
- Data stewards and business users can more easily perform governance tasks.
Central Governance in Multi-System Landscapes:
- MDG serves as the central hub for master data governance across S/4HANA, legacy systems, and third-party applications.
Flexibility for Custom Domains:
- Offers enhanced support for defining and managing custom master data domains.
Strategic Implications for Organizations
For Existing SAP MDM Users
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Migration Required: With SAP MDM being phased out, organizations need to include migration to SAP MDG in their S/4HANA transition plans.
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Assessment Needed: Companies should review their existing master data processes to determine how to best leverage MDG’s advanced capabilities.
For New S/4HANA Adopters
- Native MDG Adoption: SAP recommends using MDG as the primary tool for master data governance within S/4HANA environments.
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Comprehensive Governance: MDG offers a centralized framework to maintain master data quality throughout the streamlined S/4HANA architecture.
Key Benefits of SAP MDG on S/4HANA
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Real-Time Governance: Real-time data validation and processing reduce errors and increase efficiency.
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Cost Savings: A simplified architecture and pre-configured content reduce implementation and maintenance costs.
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Scalability: The in-memory HANA database supports large data volumes, making MDG suitable for growing enterprises.
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Improved Analytics: Advanced reporting capabilities support better decision-making.
Considerations for Transition
Migration Pathway:
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For organizations migrating from SAP ECC to S/4HANA: Implement SAP MDG alongside or after the S/4HANA migration.
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For organizations using non-SAP MDM tools: Assess MDG’s integration capabilities and benefits compared to third-party solutions.
Skill Requirements:
S/4HANA and MDG introduce new technical and functional paradigms, necessitating:
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Training for IT and functional teams.
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Expertise in HANA, SAP Fiori, and MDG workflows.
Steps to Configure Master Data Models in SAP
Configuring MDM in SAP typically involves establishing data structures, defining attributes, and setting up governance workflows. Usually, these configurations are handled by IT teams during the initial setup of the master data system.
However, we have outlined the steps for creating various data models that come pre-configured in SAP. If your enterprise already has these configurations in place, you may proceed directly to the next section.
Setting Up Data Models in SAP
Why "Manage" a Master Data
Over the past few decades, the field of Master Data Management (MDM) has evolved as it became clear that simply configuring data entities and models is only part of the solution.
To fully leverage the key advantages of a master data system , it is essential to carefully manage how data entries are created, configured, approved, edited, enriched, corrected, and governed. This requires a deep understanding of various industry-, account-, and discipline-specific use cases.
Like any new technology, a master data system is only as effective as the human inputs and processes that drive it. Consequently, challenges often arise.
While technology can support and greatly improve these processes, having a thorough understanding of the human-driven workflows is a critical prerequisite before implementing any master data management strategy.
Master Data Challenges in SAP
We have previously discussed the challenges of maintaining master data in a separate article. Below is a brief summary of common errors that can occur in a poorly managed master database.
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Lack of Naming Standards – When clear guidelines for creating records are missing, data entries can become inconsistent and disorganized, making it difficult to access and process information efficiently.
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Record Duplication – The goal of a master data system is to maintain a single, authoritative “golden record.” Poor management often results in duplicate data, which significantly undermines its effectiveness.
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Missing Critical Information – Complete and accurate data is essential for optimal operations and informed decision-making. Human errors often cause important data fields to be left blank or incomplete.
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Unorganized Data: For automations and workflows to function properly, data must be structured consistently within defined fields. Organizations without a well-defined master data management strategy often struggle with this.
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Conflicting or Incorrect Data: Multiple versions of the same record containing different information create confusion and make data unreliable for decision-making.
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Missing Data Governance Policies: Without formalized processes, approval workflows, and dedicated data stewards supported by technology, data management initiatives tend to fall short.
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Undefined Approval Workflows: Establishing clear access controls and approval processes for creating and editing master data is crucial to prevent issues such as inconsistent naming, duplication, missing information, and unstructured data.
What are the Components of SAP MDM
As previously mentioned, SAP MDM is a technical module designed to be implemented on various types of master databases to help maintain data accuracy.
Alongside SAP Master Data Governance (SAP MDG), which focuses on data governance, the key components of SAP MDM include:
Data Consolidation
Goal: Consolidate data from various systems (such as legacy platforms and third-party applications) into a single repository.
Techniques:
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Utilize ETL tools to extract data from diverse systems.
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Standardize field names, formats, and coding conventions.
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Eliminate duplicates by applying matching algorithms, such as fuzzy logic.
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Data Cleansing
Goal: Enhance data quality by eliminating errors, redundancies, and inconsistencies.
Techniques:
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Enforce standardized naming conventions (e.g., "IBM Inc." vs. "IBM").
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Validate data fields to ensure compliance with specific formats (e.g., phone numbers).
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Identify and correct anomalies such as invalid email addresses or inconsistent currency codes.
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Data Harmonization
Goal: Ensure data consistency across systems.
Techniques:
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Normalize data entries (e.g., treating "UAE" and "United Arab Emirates" as equivalent).
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Map local codes to global standards (e.g., converting regional product codes to universal identifiers).
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Analytics & Monitoring
Goal: Continuously monitor data quality and performance.
Techniques:
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Implement dashboards to track KPIs such as completeness, accuracy, and timeliness.
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Utilize SAP BW or SAP Analytics Cloud for data reporting and analytics.
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Conduct periodic audits to detect and resolve data quality issues.
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Data Governance
Goal: Define and enforce policies for data quality, security, and compliance.
Techniques:
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Implement workflows for approving data creation and modifications.
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Use role-based access controls to prevent unauthorized changes.
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Monitor compliance with relevant regulations, such as GDPR and SOX.
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Data Enrichment
Goal: Enhance the value of master data by adding supplementary details.
Techniques:
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Integrate with external data sources (e.g., D&B for supplier data).
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Use AI/ML algorithms to infer missing information or detect anomalies.
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Standardize taxonomy for products, categories, and descriptions.
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Data Integration
Goal: Share master data seamlessly with all relevant systems.
Techniques:
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Use SAP PI/PO or middleware for integration with SAP and non-SAP systems.
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Distribute data via real-time APIs or scheduled batch processes.
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Implement change pointers to ensure only updated records are transmitted.
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Tooling & Features within SAP MDM
SAP MDM Console: Administrative tool used to manage repositories, schemas, and system configurations.
MDM Import Manager: Enables importing data from external sources into SAP MDM.
MDM Data Manager: User interface for managing, editing, and enhancing master data records.
MDM Syndicator: Responsible for publishing and distributing master data to connected applications.
MDM Workflow Engine: Facilitates automated workflows for data governance and approval procedures.
Data Quality Engines: Connects with third-party solutions (such as Informatica and Trillium) to perform advanced data cleansing.
Limitations of SAP MDM
SAP MDM is a highly robust software that effectively addresses many master data management challenges.
Among leading ERP systems, SAP offers arguably the most comprehensive features and capabilities for master data management via SAP MDM.
However, it is not a fully standalone solution that enables companies to completely manage their master data independently.
Drawing on over 15 years of experience tackling master data challenges for Fortune 500 companies, we have collaborated with many organizations that require our bolt-on software solutions for Master Data Management and data governance—even while using SAP MDM and MDG.
Through client feedback and further investigation, we identified key challenges enterprises continue to face despite deploying this solution:
High Implementation Complexity
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Challenge: Implementing SAP MDM demands extensive planning, time, and resources. The process involves integrating multiple systems, configuring workflows, and designing data models, which can be complex and resource-intensive.
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Impact: Organizations may experience delays or need specialized consultants to ensure a successful deployment.
Lack of Advanced Governance Features
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Challenge: Although SAP MDM offers basic data governance features, it lacks the advanced, built-in governance workflows and tools found in more modern solutions like SAP MDG (Master Data Governance).
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Impact: Organizations requiring comprehensive governance capabilities may have to depend on custom development or supplementary tools.
Limited User Interface (UI) Flexibility
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Challenge: The SAP MDM user interface is less modern and intuitive compared to newer SAP platforms such as SAP Fiori or SAP MDG.
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Impact: This can result in a steeper learning curve and reduced adoption among business users due to its less user-friendly design.
Industry-Specific Use Cases
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Challenge: The effectiveness of master data management can depend heavily on industry-specific modules and knowledge bases, which are especially important for integrating AI capabilities.
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Impact: Without these specialized tools, organizations may struggle to address or scale solutions tailored to their industry-specific needs.
Not Scalable for Enterprise Requirements
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Challenge: SAP MDM can encounter performance limitations when handling very large datasets or complex master data spanning multiple domains.
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Impact: To support increasing data volumes, organizations may need to invest in additional resources or make technical enhancements.
Dependency on Other SAP Tools
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Challenge: SAP MDM depends extensively on integration with other SAP solutions and third-party tools, such as SAP NetWeaver, SAP PI/PO, or ETL tools, to operate effectively.
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Impact: Without these integrations, the capabilities of MDM may be limited, resulting in increased costs and added complexity.
Limited Support for Cloud-Native Environments
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Challenge: SAP MDM was primarily built for on-premises use, which means it is not fully optimized for modern cloud-native environments.
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Impact: Organizations pursuing cloud-first strategies may encounter difficulties in utilizing MDM effectively without making further modifications.
Conclusion
As organizations migrate to modern ERP platforms like SAP S/4HANA, maintaining clean, consistent, and well-governed master data is essential. Poor data quality can negatively affect system performance, decision-making accuracy, and compliance with regulations. Moresco helps enterprises tackle these challenges with AI-powered solutions designed specifically for complex ERP migrations. From data discovery and cleansing to enrichment and governance, Moresco transforms scattered master data into a unified, dependable foundation. Collaborate with Moresco to future-proof your data environment and maximize the value of your ERP investments.
