SAP Master Data Governance (SAP MDG) is an essential tool for businesses looking to consolidate and manage their master data effectively. Master data is crucial to businesses for successful operations and meaningful transactions. The identification of master data can vary between industries and differ across various business processes. Master data also includes reference data with less frequent definition changes.
SAP MDG supports several key master data objects, including material master, business partner, customer master, supplier master, FI-CA, internal orders, and finance master data objects like general ledger accounts, cost center, profit center, and more. The tool provides reusable templates and frameworks to extend the standard functionality and govern custom master data objects. SAP also offers partner-developed solution extensions for master data objects linked to enterprise asset management and retail article master.
In addition to the above, SAP MDG offers other data management capabilities like hierarchy processing, mass processing, data integration, data quality evaluation, data quality management, and process analytics.
2. Key Features and Benefits of SAP MDG
SAP Master Data Governance comprises various modules that facilitate enterprise-wide master data management (MDM) by providing a comprehensive set of tools for monitoring and controlling information. The following are the key modules of the SAP MDG:
- Data Quality Management (DQM): DQM allows users to establish quality control protocols, ensuring that all relevant data is captured in the database. This feature guarantees that the data is always up-to-date, precise, and consistent with industry standards.
- Master Data Management (MDM): MDM helps companies to streamline and standardize various business data types across multiple systems.
- Master Data Services (MDS): MDS automates the entire master data governance process, including creating new customer records and updating existing information when necessary.
In essence, the SAP MDG enables organizations to take charge of their master data and achieve optimal data quality and compliance.
Tight integration with the SAP data model for key master data domains, which helps to reduce the total cost of implementation by using data validation and mapping logic.
Frameworks to configure custom master data objects for data management and extension capabilities for standard master data objects.
Application programming interfaces (APIs) and integration capabilities to enrich data and provide change logs.
The business rules repository is used to manage data quality rules and analyze data quality for product and business partner master records. Correction of these data quality issues (data quality evaluation) occurs via worklists and embedded analytics.
3. Different Use cases
SAP MDG has multiple use cases, some of which include:
Central Governance: This feature allows for the creation and maintenance of master data in a central system that adheres to the data rules and standards. The maintained master data is replicated to satellite systems, which utilize the quality master data for downstream system transactions. With centralized governance and replication, data is consistent across the enterprise and facilitates error-prone manual master data maintenance processes in multiple satellite systems.
Consolidation for Analytics Purpose Only: This feature allows businesses to create master data in separate systems or transactional systems which are consolidated into a central system. The data is merged and mapped into a common data standard, so it can be used for analytical purposes.
Consolidation for Initial Load Before Central Governance: This is a one-time activity where data is extracted, cleansed, and consolidated using the consolidation scenario. This prepares the data for the central governance scenario where an enterprise decides to centrally govern the master data and replicate it to multiple systems.
Consolidation for Mergers or Acquisitions: This feature harmonizes and de-duplicates new data with existing data when new systems are introduced into the landscape.
Continuous Hybrid Approach: Here, both consolidation and central governance scenarios are implemented together to refine master data from the source systems. Enhanced data is then governed through the central governance process.
4. Real-Life Examples: Success Stories with SAP MDG
SAP Master Data Governance (MDG) has been implemented by many companies across various industries to improve their master data processes and drive better business outcomes. Here are some real-life examples of successful implementation of SAP MDG:
- Coca-Cola European Partners (CCEP)
Coca-Cola European Partners is one of the world’s largest independent bottlers of Coca-Cola products, operating in 13 countries across Europe. The company was facing challenges in managing its master data due to multiple legacy systems and manual processes. To address this issue, CCEP implemented SAP MDG to streamline its master data management processes and ensure data consistency across all systems.
With SAP MDG, CCEP was able to automate its governance workflows and improve data quality through standardization and validation rules. This resulted in faster processing times for new product launches and improved accuracy in financial reporting.
Read more about how CCEP leveraged SAP MDG for better master data management: https://www.sap.com/documents/2018/06/7c5d9b3e-5f7d-0010-87a3-c30de2ffd8ff.html
- Siemens AG
Siemens AG is a global technology company that specializes in electrification, automation, and digitalization solutions for various industries such as energy, healthcare, transportation etc.. The company had multiple ERP systems which led to inconsistent master data across different business units.
To address this issue Siemens AG implemented an enterprise-wide solution using SAP Master Data Governance (MDG). With the help of this solution they were able to establish a single source of truth for their critical business information which helped them make informed decisions based on accurate information.
Read more about how Siemens AG leveraged SAP MDG for better master data management: https://www.sap.com/documents/2016/06/7c5d9b3e-737c-0010-82c7-eda71af511fa.html
Nestle is a Swiss multinational food and beverage company that operates in more than 190 countries worldwide. The company was facing challenges in managing its product data due to multiple systems and manual processes.
To address this issue, Nestle implemented SAP Master Data Governance (MDG) to streamline its product data management processes and ensure data consistency across all systems. With the help of this solution, they were able to automate their governance workflows, improve data quality through standardization and validation rules which resulted in faster processing times for new product launches.
Read more about how Nestle leveraged SAP MDG for better master data management: https://www.sap.com/documents/2018/06/fd4f1a2e-5f7d-0010-87a3-c30de2ffd8ff.html
These success stories demonstrate how implementing an effective master data governance solution like SAP MDG can help businesses optimize their operations for better performance.
4. Comparison with Other Master Data Management Solutions
AP Master Data Governance (MDG) is one of the most popular master data management solutions available in the market. However, there are other MDM solutions that businesses can consider based on their specific needs and requirements. Here’s a comparison table between SAP MDG and some other popular MDM solutions:
|Solution Name||Key Features|
|SAP Master Data Governance (MDG)||– Centralized governance workflows – Standardization and validation rules – Integration with SAP ERP systems|
|Informatica MDM||– Multi-domain support – Advanced matching algorithms – Integration with non-SAP systems|
|IBM InfoSphere MDM||– Multi-domain support – Advanced matching algorithms – Integration with non-SAP systems|
While all three solutions offer similar features such as multi-domain support, advanced matching algorithms, and integration capabilities, there are some differences that businesses should consider when choosing an MDM solution.
SAP MDG offers centralized governance workflows which can help streamline master data processes within an organization. It also has strong integration capabilities with SAP ERP systems which can be beneficial for companies already using these systems.
Informatica MDM offers advanced matching algorithms which can help improve data quality by identifying duplicate records across different domains. It also has strong integration capabilities with non-SAP systems which can be beneficial for companies using multiple software platforms.
IBM InfoSphere MDM also offers multi-domain support along with advanced matching algorithms. It also has strong integration capabilities with non-SAP systems like Informatica.
Ultimately, the choice of an MDM solution will depend on various factors such as business needs, budget constraints, existing IT infrastructure etc.. Businesses should evaluate each solution carefully before making a decision.
In conclusion, SAP Master Data Governance is a vital tool for businesses looking to consolidate and manage their master data effectively. By supporting several key master data objects, SAP MDG provides reusable templates and frameworks to extend standard functionality and provides governance for custom master data objects. With the various data management capabilities and use cases, SAP MDG helps businesses enhance their data quality and streamline their operations.