[SOLVED] How to Match Incomplete Data Using Fuzzy Data Matching
When working with real-world datasets, incomplete information is more common than you’d think. Some records might contain a name and phone number, others a name and email, and a few might have just a name and address. Matching these records manually? Nearly impossible. But with the right tool—and the right strategy—it becomes surprisingly easy.
The Problem with Incomplete Data
In an ideal world, every record in your dataset would be complete, clean, and perfectly structured. Unfortunately, the reality is messy. Especially in CRM exports, lead lists, or legacy systems, it’s rare to find fully populated records. This makes flexible fuzzy data matching a necessity, not a luxury.
Let’s say you have a contact in three different rows:
One record has a name and phone number.
Another has the same name and email.
A third has the same name and an address.
Individually, none of these records give you the full picture. But together, they represent one real person or business. So how do you bring them together?
We will use the following records as an example for this article. Please note that each one of them is missing an important piece of information that is needed for matching.
The Solution: Multi-Definition Fuzzy Matching in MDP
At Match Data Pro, we’ve built a matching engine that solves this exact problem—online fuzzy matching with multi-layered logic. Instead of relying on a single rule, we use multiple definitions (OR statements) and multiple criteria within each definition (AND statements). This gives you unmatched flexibility.
Here’s how it works:
Definition 1 might say: Match records if Name and Phone Number are fuzzy matched.
Definition 2 might say: Match records if Name and Email are fuzzy matched.
Definition 3 could be: Match records if Name and Address match above a certain threshold.
As long as one full definition evaluates to true, a match is made. This logic ensures you capture those valuable links between records that seem disconnected at first glance.
Why This Works Better Than Traditional Matching
Traditional matching tools struggle with missing fields. If an email is blank or a phone number is missing, the match is discarded. That’s a missed opportunity.
By contrast, MDP’s fuzzy name matching doesn’t give up so easily. Even if a field is empty, the tool looks at the remaining criteria. If a name is close enough and another field supports the match, it goes through.
This approach is not only more accurate—it’s also aligned with data cleansing best practices and data profiling to find quality issues. You get better data quality without sacrificing precision.
Final Step: Grouping for Clarity
Once matches are identified across definitions, MDP performs an intelligent grouping process. This combines all linked records—even if they matched through different definitions—into unified groups.
That’s how one name spread across three records (with partial info in each) becomes one solid, enriched record group. You can then review, merge, or export them based on your needs.
Please note the following observations regarding the output from the MDP Fuzzy Matching Tool:
- You can see that all 3 records have the same group ID (1). This is how the matches are linked.
- You can see the criteria scores for the Name + Phone Definition.
- You can see the criteria scores for the Name + Email Definition
- You can see the criteria scores for the Name + Address Definition
Real Example from the Tool (With Screenshots)
As you can see in the above paragraphs, we are able to successfully group the similar records together with fuzzy data matching even though some important information is missing. This shows you the power and flexibility of the tool.
Why It Matters
Better matching means better decisions. Whether you’re cleaning up a mailing list, merging customer records, or building a new CRM from multiple sources, fuzzy matching that accounts for incomplete data is essential.
Match Data Pro makes it easy to implement these fuzzy data matching strategies—online, fast, and scalable.
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