SAP NetWeaver MDM was a stand-alone system built on C/C++, which started its journey in 2005, when SAP acquired of a company named A2A. It has reached its End of Life in 2020. In 2022 there are many organizations who are still using SAP NetWeaver MDM as the single version of truth for their SAP Master data. These organizations are either in the mode of migrating the huge volume of master data to modern applications like SAP MDG and S/4HANA, or probably they are planning to do so. It is a no brainer to realize “Why” these organizations are trying to move master data to modern SAP applications. In this blog post we will understand the “What” and “How” to complete this path for data journey from SAP MDM to SAP MDG in very simple terms covered under the 4 Pillars.
Pillar 1 – Data Integration
There are 2 approaches to achieve the movement of master data outside of SAP NetWeaver MDM (SAP MDM in short).
- SAP Process Integration (SAP PI)
- SAP Business Object Data Services (SAP DS or BODS)
SAP PI – Since these systems are running over decades, we can use the existing system setups i.e., using the already existing integration between SAP MDM and SAP PI.
Approach 1 – SAP PI: Leveraging Existing Integration with SAP MDM
Data Syndication – Outbound Scenario from MDM, Syndicate Files in XML
Data Movement – Inbound Scenario to SAP ECC or SAP S/4HANA
Approach 2 – SAP BODS: Data Movement from SAP MDM to ECC or S/4HANA
Outbound Scenario from SAP MDM
Using DB Views, we can directly Connect SAP NetWeaver MDM with SAP BusinessObjects Data Services (BODS). It generates a read-only database view of an MDM repository’s underlying database schema by using join operations. There is no data replication hence it saves space and resources.
Pillar 2 – Data Quality
There are three types of data in SAP system:
SAP Business Objects Data Quality Management helps to analyze, cleanse, and match all type of data customer, supplier, product, or material data, structured or unstructured – to ensure highly accurate and complete information anywhere in the enterprise.
Data quality refers to the set of transforms that work together to improve the quality of your data by cleansing, enhancing, matching, and consolidating data elements.
Data quality is primarily accomplished in the software using 4 transforms:
- Address Cleanse: Parses, standardizes, corrects, and enhances address data.
- Data Cleanse: Parses, standardizes, corrects, and enhances customer and operational data.
- Geocoding: Uses geographic coordinates, addresses, and point-of-interest (POI) data to append address, latitude and longitude, census, and other information to your records.
- Match: Identifies duplicate records at multiple levels within a single pass for individuals, households, or corporations within multiple tables or databases and consolidates them into a single source
Pillar 3 – Data Migration
- Webservices REST
- Webservices SOAP
Effective Data Migration Process
SAP Data Migration Solutions
|Solution||Usage Scenario||Migration Complexity||Features and Capabilities|
|SAP S/4HANA Migration Cockpit||
-One legacy SAP system to S/4HANA.
-Small to large data volumes.
-Minimal transformation required.
-No data cleansing required.
-A sufficient solution for a migration with low complexity for data being migrated from a single legacy SAP system to a new SAP S/4HANA environment with minimal data transformation requirements.
-Handles small to large volumes of data and includes pre-defined migration objects.
-Pre-defined migration objects (content and mapping).
-Migration Object Modeler for creating custom or adapt existing migration objects for S/4HANA.
-Transfer Data using Files (XML templates).
-Transfer Data using Staging Tables (using SAP Data Services).
-Transfer Data directly from SAP Systems (from SAP S/4HANA 1909).
|SAP Data Services with SAP Information Steward||
-Multiple legacy SAP/ non-SAP systems to S/4HANA.
-Consolidation of multiple SAP and non-SAP systems for migration.
-Complex data preparation, transformation and cleansing required.
-Reusable for on-going governance after migration.
-SAP Data Services in conjunction with SAP Information Steward are recommended for migrations that range medium level complexity.
-SAP Data Services enables to integrate, transform, improve, and deliver trusted data and complements the SAP S/4HANA migration cockpit for transferring Data using Staging Tables.
-Data profiling/ assessment prior to migration.
-Connectivity to a wide range of data sources for strong data extraction and preparation.
-Reusable sophisticated transformation and data cleansing supported with no coding.
-Monitoring and remediation of post-migration data quality issues.
|SAP Advanced Data Migration by Syniti||
-Large number of legacy SAP/ non-SAP systems to S/4HANA.
-Complements Data Services and Information Steward along with the SAP S/4HANA migration cockpit for the last step of data load into SAP S/4HANA.
-Migration project management, metrics, and communication.
-Reusable across project phases and multiple data migration projects.
-SAP Advanced Data Migration by Syniti is recommended for highly complex migrations, involving the data migration of multiple legacy SAP and/or non-SAP systems to SAP S/4HANA and for migration projects that will potentially span more than one phase or wave.
-A project management framework that include intelligent automation and prebuilt content with native integration with SAP Data Services.
-Project-based view, control, and orchestration of migration process, steps, and team.
-Orchestrate the underlying technologies used e.g., SAP Data Services, SAP Landscape Transformation.
-Collaboration platform for teams of different personas- project managers, analysts, developers, and LOB stakeholders.
Pillar 4 – Data Reconciliation
When the actual migration is complete, reconciliations can be started. Validation and Reconciliation are 2 important steps which should not be ignored in the process.
- Preload reports
- Error reports
- Post load reports
- Post load error reports
Please read my other blog posts on SAP Master Data Governance on S/4HANA.