Data Quality, part of SAP’s Data Services, is a powerful tool for cleansing and matching customers, businesses, postal addresses, and much more. However, its wide range of options and capabilities can be intimidating for new users. In this series of posts, I will focus on key transforms and settings required to get your first cleanse up and running.
What is Data Cleansing?
Data Cleansing in the context of SAP Data Quality covers a range of activities including cleansing, standardization, and supplementation. Using dictionaries and reference files, it not only repairs and reformats, but in some cases also completes and fills missing data.
Cleansing broadly refers to Data Quality’s ability to identify and flag incorrect or suspicious data. This will catch invalid phone numbers, non-existent postal addresses, improperly formatted email addresses, and incorrect dates, as well as common misspellings of names and businesses.
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About Bruce Labbate
Bruce Labbate is a business intelligence consultant specializing in data warehousing, data quality, and ETL development. Bruce delivers customized SAP BusinessObjects Data Services solutions for customers across all industries. With Decision First Technologies, Brett utilizes Data Integrator, Data Quality, Information Design Tool, and a variety of database technologies, including SQL Server, DB2, Oracle, Netezza, and HANA.