Why Do Name Matching Problems Occur?
Name Matching occurs for various reasons, the most important of which is the user’s conduct or intention. Some persons may choose to provide their nickname (a typical issue with online businesses that ask consumers to fill out forms).
Some people may opt to submit only their initials, while others may enter a random name. Regardless of your company’s size, kind, or sector, the cost of misleading or erroneous data is always considerable.
However, if your firm works in law enforcement, national security, financial compliance, or other data-sensitive areas, you cannot afford to take the risk of Name Matching.
4 General Ways to Resolve Name-Matching Issues
String matching has plagued businesses and organizations for decades. Enterprises such as Google and Amazon employ various strategies to tackle this difficulty, while less financed businesses continue to need help with the costs of managing a huge database.
1. The Common Key Method
Phonetics, a typical name difficulty, may be overcome using the typical Key Method. In this system, names are represented by a key or code corresponding to their English pronunciation.
A phonetic algorithm. Soundex indexes names based on their sound. For example, SMITH and SCHMIDT use S530 as their key. This is a simple method for resolving name conflicts. However, it is limiting.
It only works with Latin-based languages. Double Megaphones, another phonetic method, employs a main and secondary code for each name, allowing it to consider various languages such as Slavic, Germanic, Spanish, French, Greek, Italian, and Chinese!
Pros: Simple, quick, and great recall value.
Cons: It doesn’t function as well with non-Latin names. Accuracy may be compromised.
2. Method for Looking Up a List or a Dictionary
The procedure is straightforward: list all potential name variations and compare them to the main source.
This approach is most suited for multicultural data since names might have several derivations, possibly due to cultural preferences, individualism, or an uncorrected human error.
Although the list technique is straightforward, it is resource-intensive and fails when faced with additional variables such as initials, nicknames, surnames, etc.
Pros: Simple to use.
Cons: It is resource-intensive, has recall issues since new variations may not be caught, and is sluggish because it checks a huge database to find a match.
3. Edit Distance Method
The edit distance approach divides spellings into characters and assigns them weights. “Carl” and “Karl” will have an edit distance of one because the C transforms into a K.
The C is “transposed” to represent the K in this situation. The term “edit” in this procedure refers to the insert, remove, and transpose operations necessary to match the strings.
Pros: Easy to accomplish.
Cons: Does not operate well with non-Latin languages.
4. Rule-Based Method
This is an interesting approach based on human understanding. It is labor-intensive, but it combines real-world knowledge about names from many cultures and races.
This approach has the advantage that there is no translation from a foreign language to English, and the cultural subtleties of a language are preserved.
Pros: caters to foreign language names.
Cons: Relying on human knowledge.