Background of SAP & MDM
Master Data Management (MDM) plays a critical role in modern Enterprise Resource Planning (ERP) systems. SAP has emerged as a leading platform for organizations seeking to manage their master data effectively.
This article provides an overview of the architecture of master data within SAP systems, explores key SAP modules, and delves into the functionalities of SAP MDM and SAP MDG.
Additionally, we will discuss their limitations and the challenges organizations face when migrating to S/4HANA.
Understanding SAP Architecture
Before understanding SAP MDM and SAP MDG, it is important to understand the overall architecture of SAP without getting too technical and what exactly is the objective of maintaining an ERP system in the first place
An ERP system, like SAP, Oracle JD edwards, Infor etc exist to document, automate, systematize, analyze and standardize business processes across an organization.
Put simply, corporations wish to organize and standardize most business processes to be able to introduce efficient business processes and also to analyze the performance and outcomes of past business processes
This means “templatizing” several business processes, based primarily on business functions; followed by industries.
ERPs like SAP introduce several such “templates” for different business functions, with variations for each industry-type. In SAP speak, these collection of templates are referred to as “modules”
Modules in SAP
In essence, modules are out of the box software solutions for repetitive business processes across the same function within different companies OR for industry specific use-cases.
There are typically two main types of modules in SAP; functional and technical.
- Technical Modules
Technical modules focus on the customization, integration, and development of SAP systems. These modules ensure the proper implementation and optimization of functional modules. SAP MDM itself is considered a technical module applied across functional modules to manage master data effectively.
Some 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 for customizing SAP applications.
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SAP NetWeaver – Supports integration across SAP and non-SAP systems.
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SAP PI/PO (Process Integration/Orchestration) – Ensures smooth data exchange across enterprise applications.
- Functional Modules
These modules focus on specific business processes and provide tools and configurations to meet business requirements.
Functional modules are designed to handle the operational aspects of an organization, such as finance, production, logistics, sales and marketing and human resources.
Here are a few examples of different functional modules within SAP
(Financial Accounting)
Manages 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
It now becomes fairly straightforward to see how Master Data fits into this equation. Some modules within SAP will require maintaining a “master database” for streamlining operations within said business function
For example; a material master database is primarily associated with SAP MM (Material Management) but is also heavily used in SAP PP (Production Planning) and SAP PM (Plant Maintenance)
Some common types of master data include:
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Material Master Data – Used 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, ensuring 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 ensures seamless business operations and prevents issues such as duplicate records, incorrect pricing, and compliance risks.
Changes to SAP MDM and MDG with S4/HANA
The migration to SAP S/4HANA, SAP’s next-generation ERP system, has significantly impacted both SAP MDM (Master Data Management) and SAP MDG (Master Data Governance). SAP S/4HANA brings new capabilities, architecture enhancements, and a shift in focus that directly influence how master data is managed and governed.
Here’s a detailed look at the changes and implications for SAP MDM and MDG with the advent of S/4HANA:
SAP MDM and S/4HANA Migration
End of SAP MDM as a Standalone Solution
Phase-Out of SAP MDM
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SAP has officially positioned SAP MDG as the future-focused solution for master data management.
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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
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Built on SAP NetWeaver, SAP MDM was designed primarily for on-premise deployments.
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S/4HANA’s focus on real-time data processing, in-memory computing, and cloud readiness exposes SAP MDM’s architectural limitations.
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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
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SAP MDG is tightly integrated with S/4HANA, leveraging its in-memory computing capabilities and simplified data model.
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Real-time data validation and governance are now possible through direct integration with the HANA database.
Native Support for HANA Database
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SAP MDG on S/4HANA benefits from HANA’s high-speed data processing.
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Features like real-time duplicate detection, advanced analytics, and data validation are significantly enhanced.
Simplified Data Models
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S/4HANA introduces a streamlined data model, eliminating redundancies and outdated tables.
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SAP MDG aligns with this new model, improving master data governance efficiency.
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Example: The removal of aggregate tables simplifies master data management processes.
Improved Governance Capabilities
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SAP MDG offers pre-configured governance processes for core master data domains (Material, Customer, Vendor).
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Workflows and change request management are more efficient and user-friendly, aligning with S/4HANA’s usability improvements.
Data Quality and Analytics
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Integration with SAP Analytics Cloud (SAC) and S/4HANA enables advanced reporting on master data quality metrics.
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HANA-powered predictive analytics enhance data enrichment and error detection.
Functional Enhancements in SAP MDG for S/4HANA
Key New Features in SAP MDG on S/4HANA:
Data Consolidation:
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SAP MDG now supports consolidation scenarios directly on S/4HANA.
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Enables the automatic matching, merging, and de-duplication of master data in real-time.
Machine Learning Integration:
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SAP Leonardo and other ML frameworks are integrated to enhance data matching and quality checks.
Improved UIs:
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MDG leverages SAP Fiori apps for an intuitive user experience.
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Data stewards and business users can perform governance tasks more easily.
Central Governance in Multi-System Landscapes:
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MDG acts as the central hub for master data governance across S/4HANA, legacy systems, and third-party applications.
Flexibility for Custom Domains:
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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: As SAP MDM is being phased out, organizations must plan for a migration to SAP MDG as part of their S/4HANA roadmap.
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Assessment Needed: Businesses should evaluate their current master data processes and decide how to best utilize MDG’s advanced features.
For New S/4HANA Adopters
- Native MDG Usage: SAP recommends adopting MDG for master data governance in S/4HANA environments.
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Holistic Governance: MDG provides a centralized framework for ensuring master data quality across the simplified 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.
Key Considerations Before Migrating to S/4HANA: Key Data Lessons from Chevron’s SAP S/4HANA migration with Verdantis’s AI-powered solution
Steps to Configure Master Data Models in SAP
Configuring MDM in SAP involves setting up structures, defining data attributes, and implementing governance workflows. In most cases, these are already configured by IT teams that setup a master data system in the first place.
We have, nevertheless, detailed the steps involved for creating different data models that are configured out of the box in SAP.
In the likely scenario that this is already configured for your enterprise, you can navigate directly to the next section.
Setting Up Data Models in SAP
Why "Manage" a Master Data
The discipline of MDM has emerged over the last couple of decades as people began to realize that configuring the data entities and the data models therein is only a fraction of the job done.
To harness the most noteworthy benefits of a master data system, the way in which the data entries are created, configured, approved, edited, enriched. corrected and governed are critical and require a robust understanding of several industry-specific, account-specific and discipline-specific use cases
Like any other piece of new technology, a master data is only as good as the human input(s) and human-driven processes that are configured, and, unsurprisingly, several challenges do arise from time to time
Yes, technology does support these processes and can certainly enhance them and make them far more efficient but having full context of the human-driven processes is a necessary pre-requisite before implementing any master data management process
Master Data Challenges in SAP
We have covered a separate article on difficulties faced with maintaining a master data earlier, Here is a quick rundown of some of the errors that can creep into any poorly managed master database
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Absence of a Naming Convention – An absence of clear processes in creating records can lead to haphazard entries of data records which can make it challenges to retrieve and extract and process the data
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Duplication of Records – The very idea of introducing a Master data system is to get a complete view of a single record, also referred to as the “golden record” in master data management; a poorly maintained master data will inevitably lead to duplication of information, thus drastically reducing the efficacy
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Absence of Key Information – To operate at the best possible efficiency AND to ensure best possible decision-making, “completeness” of information is a critical aspect in MDM and human-driven processes inevitably lead to such errors
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Unstructured Data: In order to introduce automations and workflows in any given business process, the data will need to be structured in a specific format across strictly defined fields. This is not common in organizations that don’t employ a concerted plan for managing master data
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Inconsistencies or Erroneous data: Multiple instances of the same record with conflicting information makes decision-making tricky and the master data useless
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Absence of Data Governance Rules: Without a clearly defined process, approval matrix and data stewardship, enforced through technology-first solutions; all efforts towards data management is likely to fail
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Absence of Approval Workflows: Clear access-controls and approval workflows need to be defined for creating and editing master data records to avoid issues mentioned in #1, #2, #3, and #4. Any master data that does not define this is bound to fail.
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What are the Components of SAP MDM
As mentioned earlier, SAP MDM is a technical module that can be applied on Master Databases of all types and can be configured for ensuring data accuracy.
Together with SAP Master Data Governance (SAP MDG), a sub-module for data governance, the core components of SAP MDM include :
Data Consolidation
Goal: Merge data from multiple systems (e.g., legacy systems, third-party applications) into one repository.
Techniques:
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Use ETL tools to extract data from disparate systems.
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Standardize field names, formats, and coding conventions.
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Remove duplicates using matching algorithms (e.g., fuzzy logic).
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Data Cleansing
Goal: Improve data quality by removing errors, redundancies, and inconsistencies.
Techniques:
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Standardize naming conventions (e.g., “IBM Inc.” vs. “IBM”).
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Validate data fields (e.g., phone numbers must follow a specific format).
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Detect and correct anomalies like invalid email addresses or mismatched currencies.
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Data Harmonization
Goal: Align data across systems to ensure consistency.
Techniques:
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Normalize data (e.g., ensuring “US” and “United States” are treated as identical).
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Map local codes to a global standard (e.g., mapping regional product codes to global identifiers).
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Analytics & Monitoring
Goal: Continuously track data quality and performance.
Techniques:
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Implement dashboards to monitor KPIs like completeness, accuracy, and timeliness.
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Use SAP BW or SAP Analytics Cloud for data reporting and insights.
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Schedule periodic audits to identify and address 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 data creation and modification approvals.
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Use role-based access controls to restrict unauthorized changes.
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Monitor compliance with industry regulations (e.g., GDPR, 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: Admin tool for managing repositories, schemas, and configurations.
MDM Import Manager: Facilitates data import from external systems into SAP MDM.
MDM Data Manager: Provides a user interface for managing, editing, and enriching master data.
MDM Syndicator: Publishes and distributes master data to connected systems.
MDM Workflow Engine: Automates workflows for data governance and approval processes.
Data Quality Engines: Integrates with third-party tools (e.g., Informatica, Trillium) for advanced data cleansing.
Limitations of SAP MDM
SAP MDM is undoubtedly an extremely powerful software and solves for several MDM-specific use cases quite well.
Among most leading ERPs, SAP arguably offers the best possible set of features and capabilities for master data management through SAP MDM.
That being said, it’s certainly not a comprehensive solution that can empower companies to independently manage their master data.
In our 15 years+ of experience in addressing master data challenges for fortune 500 companies, we’ve worked with several enterprises who seek our bolt-on software solution for Master Data Management and data governance – despite using SAP MDM & MDG.
After asking our clients and investigating further, we were able to ascertain the glaring challenges that enterprises face despite using this solution :
High Implementation Complexity
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Challenge: Implementing SAP MDM requires significant planning, time, and resources. The process involves integrating with multiple systems, configuring workflows, and designing data models, which can be complex and labor-intensive.
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Impact: Organizations may face delays or require expert consultants for smooth deployment.
Lack of Advanced Governance Features
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Challenge: While SAP MDM provides basic data governance capabilities, it lacks the advanced, built-in governance workflows and tools that are more robust in newer solutions like SAP MDG (Master Data Governance)
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Impact: Organizations seeking extensive governance capabilities may need to rely on custom development or additional tools.
Limited User Interface (UI) Flexibility
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Challenge: The user interface in SAP MDM is less modern and intuitive compared to newer SAP tools like SAP Fiori or SAP MDG.
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Impact: Users may find it less user-friendly, which can lead to a steeper learning curve and lower adoption rates among business users.
Industry-Specific Use Cases
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Challenge: Depending on the type of master data, industry-specific moduales and knowledge-bases can help address several MDM-specific requirements, especially for embedding AI in these products
- Impact: Organizations aren’t able to address or scale solutions for industry-specific challenges
Not Scalable for Enterprise Requirements
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Challenge: SAP MDM may face performance issues when dealing with extremely large datasets or complex, multi-domain master data requirements
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Impact: Scaling the solution to accommodate growing data volumes might require additional resources or technical adjustments
Dependency on Other SAP Tools
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Challenge: SAP MDM relies heavily on integration with other SAP and non-SAP tools, such as SAP NetWeaver, SAP PI/PO, or third-party ETL tools for full functionality.
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Impact: Without these integrations, MDM may not deliver its full potential, leading to additional costs and complexity.
Limited Support for Cloud-Native Environments
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Challenge: SAP MDM was originally designed for on-premise deployments, making it less optimized for modern cloud-native environments.
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Impact: Organizations aiming for cloud-first strategies may face challenges in leveraging MDM efficiently without additional adjustments.
Conclusion
As organizations transition to modern ERP systems like SAP S/4HANA, ensuring clean, consistent, and governed master data becomes mission-critical. Poor data quality can severely impact system performance, decision-making, and regulatory compliance. Verdantis empowers enterprises to overcome these challenges with AI-driven solutions tailored for complex ERP migrations. From data discovery and cleansing to enrichment and governance, Verdantis ensures a seamless transition by transforming fragmented master data into a unified, reliable foundation. Partner with Verdantis to future-proof your data landscape and unlock the full value of your ERP investments.
