Algorithm Envy

The term “algorithm envy” was used by Aaron Zornes in his piece on MDM trends when talking about identity resolution.

In my experience there is surely a need for good data matching algorithms.

As I have a built a data matching tool myself I faced that need back in 2005. At that time my tool was merely based on some standardization and parsing, match codes, some probabilistic learning and a few light weight algorithms like the hamming distance (more descriptions of these techniques here).

My tool was pretty national (like many other matching tools) as it was tuned for handling Danish names and addresses as well as Swedish, Norwegian, Finish and German addresses which are very similar.

The task ahead was to expand the match tool so it could be used to match business-to-business records with the D&B worldbase. This database has business entities from all over the world. The names and addresses in there are only standardized to the extent that is provided by the public sector or other providers for each country.

The records to be matched came from Nordic companies operating globally. For such records you can’t assume that these are entered by people who know the name and address format for the country in question. So, all in all, standardization and parsing wasn’t the full solution. If you don’t trust me, there is more explanation here.

When dealing with international data match codes becomes either too complex or too bad. This is also due to lack of standardization in both the records to be compared.

For the probabilistic learning my problem was that all learned data until then was only gathered from Nordic data. They wouldn’t be any good for the rest of the world.

The solution was including an advanced data matching algorithm, in this case Omikron FACT.

Since then the Omikron FACT algorithm has been considerable improved and is now branded as WorldMatch®. Some of the new advantages is dealing with different character sets and script systems and having synonyms embedded directly into the matching logic, which is far superior to using synonyms in a prior standardization process.

For full disclosure I work for the vendor Omikron Data Quality today. But I am not praising the product because of that – I work for Omikron because of the product.

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