Case Study: SAP MDG Implementation at a Large Diversified Chemical Company

Topic: Selection of Master Data Management Software

This is the first article in a series of articles about SAP MDG case studies. This article focuses on the early phase of software selection and proof of concept.
 

Project Background

This customer currently has an extensive SAP environment that consists of several SAP ECC instances, SAP BW, SAP APO, SAP SRM, SAP BOBJ BI, as well as various other SAP and non-SAP software tools. This customer also has a central SAP ECC instance for master data management and integrated Lotus Notes work flow and collaboration system in support of its data governance processes. Customer is in the process of integrating several of its SAP instances into one SAP ECC instance.

 

Customer wanted to consider replacing its ECC based master data instance and its Lotus Notes based governance system with an off-the-shelf master data management tool. Customer was confused, however, about the various master data management and EIM offerings from SAP.

Solution

The following explanation of the different SAP tools helped this customer to focus on SAP MDG as the proper solution.

 

There are some overlapping capabilities between SAP MDM and MDG. By all accounts, MDG is the tool that SAP is planning to develop and support into the future and MDM is being sunset. However, MDM still has an advantage in match-and-merge area, and MDG is stronger as a central master data governance system – especially, when there is a need to govern SAP ERP master data.  

 

Match-and-merge is functionality needed to match master data maintained in multiple systems, harmonize it, and then syndicate the master data back to those systems. Therefore, typically MDM is better used when there is no one system is the source of master data -- there are multiple systems that own the same master data and the main functionality of MDM system is to help synchronize this data.  

 

Please note that SAP MDG EhP 7.0 offers an enhanced data matching and cleansing capabilities using SAP Hana. This functionality seems to enhance fuzzy match, but it remains to be seen if it is cable to execute an effective match-and-merge function as described here.

 

MDG's strength is in its ability to govern master data by being the central system to do this. Data governance has two important components -- managing work flow and managing the quality of data. MDG can distribute the data to other systems as needed (and it has excellent integration capabilities through DRM), but it should be the central source for data creation or at least for enforcing data and business standards and quality standards.

Data Services is an ETL tool. I call it ETL+, because SAP Data Services has excellent capabilities not only to move data from one source to another, but also to improve data quality in the process and to clean data. Data Services is not an alternate tool to MDG and MDM, it is a complementary tool. Data Services can be used effectively in many scenarios to integrate data between MDG and external system (with exception of SAP ERP), for data validation (e.g., address validation) in real-time integrated mode with MDG, and for data cleansing (real-time or batch).

 

Information Steward is from the same package as Data Services and uses some of the same routines (e.g., profiling). The main focus of Information Steward is on monitoring data quality. It is also a complementary tool to MDG and MDM. It is possible to setup data quality dashboards and monitor whether actual data quality conforms to standards. Information Steward can also manage meta-data in an enterprise. This functionality is less applicable to ERP data (since meta-data is well defined within ERP data dictionary), but is very important to help manage meta-data in different business intelligence and data warehouses.

 

Based on these definitions, this customer selected to implement SAP MDG EhP 6.1 (EhP 7.0 was not available at the time) integrated with SAP Data Services 4.1 and Information Steward 4.1.
 

SAP MDG Proof-of-Concept (POC) Phase

Prior to finalizing their decision, the customer wanted to do a proof-of-concept (POC) project to make sure that SAP MDG has the capabilities to address main business requirements. I recommend this approach for customers that have complex business processes and complex IT environments. For smaller customers with simpler business processes, I often recommend to go straight into pilot implementation, as a pilot MDG project may not be much more expansive than a POC.

 

The scope of this POC project included the following:

• Standard Vendor Master data model (out-of-the-box SAP MDG data model) with minor modification of adding one new SAP data element that was not in the standard data model and one custom data element;
• Standard Vendor Master BRF+ workflow with minor modification – one parallel approval process was added to demonstrate parallel work flow process;
• No integration with SAP ERP system was implemented, but output data from MDG was exported for review after activation;
• Standard MDG data and process monitoring analytics and KPIs were used to demonstrate MDG master data governance monitoring capabilities.

After successful completion of this POC, the customer was satisfied that SAP MDG was able to address its business requirements and replace the central ECC based master data system as well as Lotus Notes based governance. 

 

In the next article I will describe some of the MDG EhP 6.1 architectural decisions that were made during the initial phases of MDG implementation.