Your Deduplication Processes May be Leaving You at Risk for GDPR Fines

Melissa Team | Article, Data Audit, Data Matching, Data Quality, Duplicate Elimination, Fuzzy Matching, GDPR, Global Business, Global Data Quality, Identity Resolution | , , , , , , , , ,

Once-trusted fuzzy matching algorithms may be leaving your organization vulnerable to hefty GDPR fines. The balancing act of false-positives and false-negatives in single customer view (SCV) systems used to favor the false-negative side, with near negligible error results. However, the standard of that balancing act has now been redefined by the GDPR regulations. Find out how GDPR has moved the “match” goalposts, how to test your SCV platform, and what you need to do to keep your organization GDPR compliant.

Tips & Tricks for Global MatchUp Matching Strategies

Blog Administrator | Address Verification, Matching, Tips & Solutions | , , , , ,

 

by Tim Sidor, Data Quality Analyst

In
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.

 

For
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
logic.

 

At
first glance, a ‘Global Address’ matchcode might appear to be a safe accurate
matching strategy…

But
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.

 

US
& 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.

Configuring
this matchcode to allow ‘blank matching’ on the Postal Code will accurately match
records for most worldwide addresses and is a default distributed matchcode.

 

However,
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.

 

How
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.

How to Do It All with Melissa

Blog Administrator | Address Check, Address Correction, Address Quality, Address Standardization, Address Validation, Address Verification, Analyzing Data Quality, Data Enhancement, Data Enrichment, Data Governance, Data Integration, Data Management, Data Matching, Data Profiling, Data Quality, Data Quality Components for SSIS, Full Contact Authentication, Geocoding, Global Address Verification | , , , ,

With
Melissa, you can do it all – see for yourself with the brand new Solutions
Catalog. This catalog showcases products to transform your people data (names,
addresses, emails, phone numbers) into accurate, actionable insight. Our
products are in the Cloud or available via easy plugins and APIs. We provide
solutions to power Know Your Customer initiatives, improve mail deliverability
and response, drive sales, clean and match data, and boost ROI.

 

Specific solutions include:

·
Cleaning,
matching & enriching data

·
Creating
a 360 degree profile of every customer

·
Finding
more customers like your best ones with lookalike profiling

·
Integrating
data from any source, at any time

Other
highlights include: global address autocompletion; mobile phone verification; real-time
email address ping; a new customer management platform; as well as info on a
wealth of data append and mailing list services.

 

Download the catalog now:

https://www.melissa.com/resources/catalogs/melissa-2021-solutions-catalog