Which approach is used to handle inconsistencies during data consolidation in SAP MDG?

In the context of data consolidation within SAP Master Data Governance (MDG), the match and merge approach is crucial for identifying and resolving inconsistencies that may arise from having multiple records representing the same entity. This method involves comparing data records against each other to identify duplicates and then merging them into a single, unified record.

Utilizing match and merge facilitates the maintenance of data quality by ensuring that all relevant information is combined, avoiding data fragmentation. This process can improve accuracy and reliability when analyzing master data, as it allows organizations to present a single version of the truth. By effectively combining duplicates, organizations can enhance operational efficiency, reduce redundancy, and promote clearer decision-making based on consistent data.

In contrast, the other methods listed do not directly address data inconsistency during consolidation. Data warehousing involves storing large volumes of data but does not inherently resolve inconsistencies within that data. Duplicate data check might identify duplicates but does not necessarily merge the records to create a single authoritative source. Static data allocation refers to assigning fixed data values and does not pertain to the dynamic process of managing inconsistencies during data consolidation.