Liliendahl on Data Quality

In master data management the two most prominent domains are:

• Parties and
• Products

In the quest for finding representations of parties actually being the same real world party and finding representations of products actually being the same real world product we typically execute fuzzy data matching of:

• Party names as person names and company names
• Product descriptions

However I have often seen party names being an integral part of matching products.

Some examples:

Manufacturer Names:

A product is most often being regarded as distinct not only based on the description but also based on the manufacturer. So besides being sharp on matching product descriptions for light bulbs you must also consider if for example the following manufacturer company names are the same or not:

• Koninklijke Philips Electronics N.V.
• Phillips
• Philips Electronic

Author Names:

A book is a product. The title of the book is the description. But also the author’s person name counts. So how do we collect the entire works made by the author:

• Hans Christian Andersen
• Andersen, Hans Christian
• H. C. Andersen

as all three representations are superb bad data?

Bear Names:

A certain kind of teddy bears has a product description like “Plush magenta teddy bear”. But each bear may have a pet name like “Lots-O’-Huggin’ Bear” or just short “Lotso” as seen in the film “Toy Story 3”. And seriously: In real business I have worked with building a bear data model and the related data matching.

PS: For those who have seen Toy Story 3: Is that Lotso one or two real world entities?