Sap mdm Master Data Management

Sap mdm (Master daTa ManagerMaster Data Management) is mainly used for cleaning and comparison of master data. It integrates the inconsistency between master data of different systems to ensure the smooth operation of transactions (Data Exchange) based on master data.

Concept of primary data

 

First, we will introduce what is primary data. Here we use a data classification model from other websites. We can see that metadata (metadata) References
DaTa), Master data (Master daTa), Enterprise structure data (Enterprise
Structure daTa), Transaction activity data (transaction
Activity daTa), Transaction audit data (transaction audit daTa.

 

A brief explanation of these six categories of data, the definition of these types of data can be easily found on the Internet.

 

Metadata: Data. When designing a table, most attribute fields are metadata. For example, gender, nationality, and province of birth. This is the data closest to the natural meaning.

 

Reference data: the possible value range of metadata. When designing a table, the data dictionary often refers to reference data. For example, a gender can only be male and female, while a male and female can reference data. The cited data of countries is over 100 countries and regions in the world;

 

Primary data: the most important entities in our database design are the collection of metadata and reference data instances. Dmreview columnist Jane
Griffin defines primary data as "... used to create and maintain a full enterprise 'record system' for core business entities to record business transactions and assess the information required for the performance of these entities ." The customer information and product information we often encounter usually belong to the primary data. The introduction of primary data will be detailed later.

 

Enterprise structured data: the data entity required by an enterprise's business. It may be a collection of multiple primary data. Structured Data in different industries is very different.

 

Transaction activity data: data generated by activity between primary data. For example, the transaction record of the purchased product is the transaction activity data, the factory production product, and the production record is also the transaction activity data.

 

Transaction audit data: we record all activities of the data through transaction audit data. For example, we modify customer information and add or delete transactions. These activities need to be recorded in many key systems (such as banks, comply with the requirements of relevant regulations (such as Basel II and Sarbanes-Oxley Act ).

 

The deeper the blue in the data model, the stronger the semantic correlation and the more important the data quality, the deeper the yellow color, the more data the data is, the faster the update frequency, the faster the Real-Time captured data, and the shorter the data life. As you can see, metadata has the strongest semantics, almost no updates, the least data volume, and the longest life cycle.

(The above is from the http://blog.csdn.net/woohooli/archive/2009/01/07/3726040.aspx)

From the above introduction, we can conclude that the primary data remains relatively stable within the system, while the transaction data is based on the primary data. In fact, imagine two systems for exchange. If the most basic data (such as product variety, color, size, and specifications) is not uniform, then the business data based on this (such as sales information) it cannot be exchanged in any way. That is why primary data management is proposed. Only when primary data is unified can the exchange proceed.

Sap mdm provides a complete set of primary data solutions, including data integration, data cleansing, and comparison, combined with SAP's XI (PI, data transmission and exchange tool ), data can be imported, exported, or published ). Overall understanding of SAP
The MDM data structure is very different from the current relational database, but its main function is to establish a set of mappings between data in multiple structures, retain and merge consistent data, and discard erroneous and conflicting data, create a merging rule for the master data of multiple systems. This process is called cleaning. Finally, the cleaned data is published to various application systems through pi.