Duplicate records are a costly problem in any system that stores people’s names. “Jon Smith” vs. “John Smith.” “Liz” vs. “Elizabeth.” “Renée” vs. “Renee.” To a human, these are obviously the same person. To an exact string comparison, they’re four different records. The fix is to stop asking “do these names match?” and start asking “how confident am I that they refer to the same person?”

This article walks through a practical C# pipeline to do exactly that: normalize input, resolve nicknames, narrow candidates with blocking and phonetics, score similarity with Jaro-Winkler, and apply thresholds to decide what to accept, review, or reject.

A brief introduction

If you’ve worked with real-world data for any length of time, you’ve probably run into this problem: the same person shows up multiple times in your system… but with slightly different names.

Maybe it’s “Jon Smith” vs. “John Smith.” Maybe it’s “Liz” vs. “Elizabeth.” Or maybe it’s something more subtle, like punctuation, casing, or a missing accent mark.

These differences, albeit minor, do add up over time – leading to duplicate records, missed matches, and a growing amount of cleanup work. It’s all normal data, but it’s entered inconsistently.

Names, for example – as mentioned, they naturally vary depending on how they’re entered, stored or translated across systems. Typos, inconsistent formatting, etc. Unfortunately, exact string comparisons just aren’t flexible enough to deal with these issues.

The trick is to change your thinking from being all about exact matches, to how confident you are that, for example, two records do indeed refer to the same person. In this article, I’ll explore that shift from exact-matching to confidence-based matching in more detail.

To do so, I’ll demonstrate a practical, multi-stage approach you can implement in your own systems. The idea is to combine a few simple techniques into a pipeline that’s both effective and scalable:

By the end, you’ll have a clear framework you can adapt for your own use case. Hopefully, you’ll also learn a thing or two along the way. Let’s start with some basic cleanup, and the Normalize function.

The Normalize function

To get started, remove noise words such as punctuation, honorifics, and generation makers, and convert everything to a common case.

How you do this is up to you – consistency is the main key – but in practice you should use culture-invariant case-folding (for example, ToUpperInvariant())), so that comparisons behave the same regardless of server locale.

You may also have diacritic marks to consider. Chloë / Zoë, Renée / René, André, José, etc. Any of these names could easily have their diacritic marks removed in one source but not another and still represent the same name. To remove these marks, we can use a function like this:

The magic happens when we initialize the normalized variable. FormD will split a composed Unicode character into two parts: the base letter, and the accent mark. The loop then rebuilds the string explicitly skipping the accent marks.  The last thing we do is convert back to FormC. 

While builder.ToString() and the return value may look alike, they are not the same at the binary level. Converting back to FormC is critical.

We can add this to a complete Normalize function:

If you feel like you might see some churn in the noise words, feel free to store the list in the database so that you can update that logic without having to recompile and deploy code.

Database considerations

Normalization solves a lot of problems for our name comparisons, but we still have some problems to work through.  Normalization will not help us with tracking nicknames, so we’ll need a lookup table here. The relationship between nicknames is intuitively a many-to-many relationship, but they are also often transitive. 

For this example, we’ll use two tables to follow a Name Group Model. The tables can be rather simple:

We can get a catalog of configured names with queries like this:

And you can search for variations on a target name and easily find the canonical name with a query like this:

The NameGroup and NameGroupMapping tables only need to be populated with known nicknames that you want to track. As you discover new nicknames (or similar variations), you can easily update these tables. Maintenance becomes a simple matter of inserting new records with no code changes needed.

If the NameGroupMapping table is properly configured, it should return CHARLES. We can populate it with seed data such as this:

Then, we can tie it all together with EF access like this:

Lookup data

Next, we’ll need lookup data to compare data against. This table has a few subtle points that make it deceptively more complicated than you might initially suspect.

Here’s the DDL (Data Definition Language) for defining this table:

We’ll talk more about the Phonetic columns and the BlockingKey shortly.

We want to include the extra demographic data of ZipCode, DateOfBirth, and Gender, to add more context and weight for our confidence score. In your specific scenario, though, you might want different demographic data.

UserGuid can be useful as a stable external identifier for APIs (so you don’t expose sequential IDs). You may also want to include other foreign keys to identify and track the original Person, or maybe even add a Source column to track where the original Person comes from.

In addition to this DDL, we will have key indexes that are critical to the performance we need. Without these, you would still see full table scans or excessive disk reads:

Before we can use this as a lookup table, though, we need to make sure that the initial records are populated correctly. We will use a PersonDto like this:

Assumption: This retrieval step assumes you have already normalized/canonicalized the incoming name (UPPERCASE) and precomputed its BlockingKey, PhoneticPrimary, and PhoneticSecondary before calling FindCandidatesAsync.

We can query the Person table, and load candidate rows into a PersonDto, using the indexes and filters we built above. Tune minCandidates and maxCandidates to match your data volume and performance needs.

Note: the implementation below replaces the candidate list as it broadens (BlockingKey → PhoneticPrimary → PhoneticSecondary). An alternative approach is to union candidates across tiers (and de-duplicate), so you keep earlier matches while expanding the pool.

Phonetics and double metaphone, explained

Phonetics is the study of sound (specifically speech sounds.) Phonetic algorithms convert a string into standardized phonetic codes. Soundex is an older phonetic algorithm which is well implemented, even in SQL Server, but it has some problems. Safe to say, it’s rather dumb compared to modern algorithms!

What is Soundex?

Soundex was designed primarily for English-language use and never considered the wider context of global data. As a result, it often performs poorly for many non-English surnames and naming conventions.

For starters, it only encodes the first letter and, even after that, only considers one character at a time – ignoring how letter combinations change sounds in wider contexts. The GH combination is a common example: the sound in “night” is completely different from the sound in “tough”, but Soundex treats them the same.

Soundex also suffers from truncation issues. No matter how long the string is, the Soundex value is always one letter followed by three numbers. If the original string is long, the end of the string will generally be ignored.

What is Double Metaphone?

Double Metaphone is different in several key ways. While Soundex returns exactly one four-character code, Double Metaphone returns a Primary and Secondary code. 

The Primary will most likely support an English pronunciation, while the Secondary supports an alternate foreign pronunciation. Plus, while Soundex ignores all vowels except the first character, Double Metaphone can consider all vowels as required.

Double Metaphone will also give more accurate matches and fewer false positives. This is why our table and DTO has the two columns/properties: PhoneticPrimary and PhoneticSecondary.

We can use a library such as Lucene.Net to populate the phonetic columns. If you want Double Metaphone specifically, you’ll typically need the analysis package as well.

What are distance algorithms?

Distance algorithms are used to measure how similar or different two pieces of data are. We intuitively understand physical distance, and time differences make sense, but generalized distance algorithms allow us to compare anything that can be represented as data.

This includes anything from simple strings (like we’re interested in here), to user preferences, and even DNA. Let’s take a look at some of the specific algorithms in detail.

The Levenshtein Distance algorithm

Levenshtein Distance measures the minimum number of single-character edits (insertions, deletions, or substitutions) needed to turn one string into another, regardless of length. The possible values range from 0, all the way to the length of the longest string being compared. 

Additionally, it counts and weighs all edits the same. This is potentially problematic for our purposes, however, because it treats a typo at the beginning of the string the same as a typo at the end.

The Jaro-Winkler algorithm

Jaro-Winkler is a similar algorithm but, instead of distance, it measures similarity. It gives values in the range 0 to 1.  In this case, 1 would mean that the two strings are identical, while 0 means that they have nothing in common.  Simply put, bigger numbers are better.

Jaro-Winkler is optimized for comparing short strings. It has optimizations to penalize differences at the beginning of the strings more than differences at the end. This is ideal for comparing names since we’re more likely to get the start of a person’s name right; spelling differences/errors are more likely to show up towards the end.

When you read about string comparisons, you’ll hear about both algorithms and others. It can be difficult to understand the differences. However, since the optimizations found in Jaro-Winkler are especially tuned to our needs for comparing names, so we’ll use this for our example. It’ll give us a similarity metric to factor into our confidence score that two names refer to the same individual.

In our example, we’ll use the StringSimilarity nuget package to provide an implementation of the Jaro-Winkler algorithm. In addition to Jaro-Winkler, it gives us access to Levenshtein and many other useful algorithms.

Finally, it’s worth noting that Jaro-Winkler is surprisingly simple to implement and well understood. If you want, you can easily write your own implementation in about 40 lines of code or so.

How to build a confidence score

In our example, it’s now time to pull all the pieces together and decide how confident we are that the matches from our various filters from the database match to the targeted individual.

We’ll use a weighted scoring strategy where matching different parts of an individual’s name might carry different weights, and not matching demographic details might carry differing penalties.

For example, we might pay more attention to matches on the last name than matches on the first name. Or, we may switch that for women who may use their maiden name or a hyphenated name. We may want to penalize (lower the confidence) if the names match but the birth year and/or gender is wrong.

Now we can build out the logic for a MatchEvaluator class. We’ll define some constants for configuration and define a coordinating method (EvaluateMatch), and some helper functions (CalculateBaseNameScore and CalculateDemographicPenalties.) 

We’ll call the FindCandidatesAsync method and loop through the results to repeatedly call EvaluateMatch:

Summary

Exact string-matching breaks down quickly in real-world data. Names are messy: spelling variations, punctuation, diacritics, nicknames, and cultural conventions all create legitimate ways to refer to the same person. That’s where a confidence-based approach comes in. It replaces the brittle “match / no match” binary with a repeatable process that can be tuned to your risk tolerance and data quality.

The key is to combine multiple weak signals into one strong decision. We start by normalizing inputs (case-folding, punctuation, diacritics, and noise words) and canonicalizing known nicknames. We then use a blocking key (and, when needed, phonetic keys) to pull a small candidate set efficiently and finally compute a weighted similarity score with targeted demographic penalties.

Finally, the last step applies thresholds to route outcomes: automatic acceptance for high-confidence matches, a review queue for ambiguous cases, and rejection when confidence is too low.

How the solution outlined in this article helps

This layered design improves data integrity while keeping performance predictable at scale. Most records are eliminated by cheap, indexed filters before any expensive scoring occurs. It also makes the system auditable, since your weights, penalties, and thresholds explain why a match was accepted or rejected. Furthermore, it reduces manual work by focusing human review on the narrow band of uncertain cases.

In practice, the biggest wins come from treating configuration as a ‘living’ asset. Keep the nickname tables current, tune thresholds based on observed false positives/negatives, and periodically re-score samples as your data and business rules evolve.

Note: AI was used to generate the feature image for this article.