by Tim Sidor, Data Quality Analyst
the past we’ve discussed implementing different matching strategies based on how
you would like your records grouped. For example. By “Address”? or by “Name and
Address”. The former would match ‘John’ and ‘Mary Smith’ at the same household,
whereas the latter would identify them as unique entities.
Global processing, even after determining and selecting a general strategy,
‘Address’ for example, it might still require knowing the expected address
formats of the source data that needs to be compared and thus reevaluate the
first glance, a ‘Global Address’ matchcode might appear to be a safe accurate
knowing that some countries don’t have a reliable Postal Code, which is usually
the component MatchUp uses for efficient ‘neighborhooding’ (also known as
‘grouping’ or ‘clustering’), how can we accurately match these records? Simply
removing the Postal Code component would incorrectly match similar addresses
that were in different parts of the country.
& Canada users are so used to using the reliable Postal Code that we rarely
use City (Locality). But for processing countries without Postal Codes, or
databases with multiple countries, adding a Locality can bring back accuracy
and efficient clustering.
this matchcode to allow ‘blank matching’ on the Postal Code will accurately match
records for most worldwide addresses and is a default distributed matchcode.
many countries distinguish addresses by also using a different hierarchy
structure which may include a combination of Dependent Locality, Administrative
Area and or Sub Administrative area. Or the use a Dependent Thoroughfare to
distinguish the delivery address. So knowing the primary data types used in a
countries standard address can help you decide the proper matchcode components
to include in your matchcode.
do I know how to construct a good matchcode for specific region processing? Our
‘Global Address, Locality’ matchcode is a good basic strategy, but using
Melissa’s resources – such as Global Verification documentation and or
actual record processing and parsing can help you determine the necessary components
to construct a matchcode to produce accurate results.