How Does AWS Know Two Records Belong to the Same Person?
A simple look at how AWS Entity Resolution matches related records across different datasets and helps create a more consistent view of data.
How Does AWS Know Two Records Belong to the Same Person?
Imagine that a company has two databases containing customer information.
One database has:
John Smith
john@example.com
john@example.com
Another has:
John A. Smith
john@example.com
john@example.com
Are these two different people or the same person?
For a human, this might be fairly easy to figure out. For a computer system working with thousands or millions of records, it becomes a much more interesting problem.
This is where I came across AWS Entity Resolution.
What is AWS Entity Resolution?
AWS Entity Resolution is a service that helps match and link related records stored across different applications, data sources, and systems.
The goal is to identify records that represent the same underlying entity, even when the information isn't exactly the same.
For example, the same customer might appear in different systems with differences in their name, address, phone number, or other information.
Instead of treating every record as completely separate, Entity Resolution can help connect related records.
Why Is This Problem Important?
One of the problems with working with real-world data is that it is rarely perfectly consistent.
A customer's name might be written differently in two systems.
An address could have a different format.
A phone number might contain a country code in one database but not another.
There could also be missing information.
A simple exact comparison may not be enough to identify these records as belonging to the same entity.
This becomes more difficult as the amount of data increases.
How Does AWS Entity Resolution Work?
AWS Entity Resolution uses something called a matching workflow.
A matching workflow takes data from different inputs, compares the records using a selected matching technique, and produces the matching results.
Before creating a workflow, we define how AWS should understand the input data using a schema mapping.
The schema mapping tells the service which fields are available and which fields should be used for matching.
For example, we might have fields such as:
- Name
- Phone number
- Address
AWS Entity Resolution can also normalize certain input data before matching, which can help handle formatting differences.
Rule-Based Matching
One way of matching records is through rules.
With rule-based matching, we define the conditions that should be used to determine whether records match.
For example, a rule could use specific combinations of fields to identify related records.
AWS provides configurable rules, and they can support exact or fuzzy matching depending on the rule type and configuration.
This approach is useful when we already understand the data and want more control over how matching decisions are made.
Machine Learning-Based Matching
AWS Entity Resolution also provides machine learning-based matching.
This is where the service becomes particularly interesting.
Instead of depending only on exact matching rules, the ML-based approach can identify similarities across records even when there are variations, errors, or missing fields.
The ML matching process can use information such as names, email addresses, phone numbers, addresses, and dates of birth.
It also produces match groups and confidence information that can help indicate how strong a match is.
This doesn't mean that every similar-looking record should automatically be considered the same person. The quality of the input data and the matching approach still matter.
What Is a Match ID?
One concept I found useful to understand is the Match ID.
When AWS Entity Resolution finds records that belong to the same matched group, it can assign a Match ID to that group.
This gives the related records a common identifier.
Instead of having several records that appear unrelated, downstream applications can use the Match ID to understand that they belong to the same entity.
This can be particularly useful when combining information from different systems.
Where Could This Be Useful?
There are quite a few situations where entity resolution could be useful.
Customer Data
A company might have customer information spread across a website, CRM system, mobile application, and other platforms.
Entity Resolution can help connect related records.
Product Data
The same product might have different identifiers or codes across different systems.
Matching can help identify records that refer to the same product.
Data Integration
When data from multiple sources is combined, duplicate or inconsistent records can become a problem.
Entity resolution can help create a more consistent dataset.
Analytics
Having related records connected can make it easier to analyze information across different customer interactions or systems.
What I Found Interesting
What I found interesting about AWS Entity Resolution is that the problem isn't really about finding identical records.
It is about figuring out whether different records represent the same underlying thing.
That sounds simple when looking at two records, but it becomes much harder when dealing with large datasets containing inconsistent or incomplete information.
This is one of those problems that is easy to overlook when working with small datasets but becomes very important when applications start dealing with real-world data.
Things to Keep in Mind
Entity resolution also needs to be used carefully.
A false match can be just as problematic as failing to find a match.
For example, incorrectly combining two different customers could result in incorrect analytics or other downstream problems.
This means that data quality, matching rules, and confidence information are important when designing an entity resolution workflow.
It is also important to think about how sensitive customer information is handled and to follow appropriate security and privacy practices.
Final Thoughts
Before exploring AWS Entity Resolution, I mostly thought of data matching as a simple comparison problem.
After looking into it, I realized that matching real-world data is much more complicated.
Two records don't always look identical even when they represent the same entity.
AWS Entity Resolution provides different approaches for dealing with this problem, including rule-based matching and machine learning-based matching.
For me, the interesting part isn't just the AWS service itself.
It is the problem behind it:
How do we teach a system that two different records can actually represent the same thing?
That is what makes AWS Entity Resolution an interesting service to explore.
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