What Is Data Matching?
A Simple Guide With
Real Examples

Data matching is the process of identifying and linking records that represent the same real-world entity across one or more datasets, even when those records are not identical.

Data matching in short

• Compares records to determine if they refer to the same entity

• Uses exact, fuzzy, or probabilistic techniques

• Requires data preparation to be reliable

• Powers deduplication and entity resolution

It is commonly used to detect duplicates, unify fragmented records, and support tasks such as deduplication, entity resolution, system migrations, and analytics.

If you have duplicate customers, vendors, patients, or contacts, data matching is how you find and connect them.

This guide explains what data matching is, how it works in practice, common methods, real-world examples, and the mistakes that cause bad matches.

What is data matching?

At its core, data matching compares records to determine whether they represent the same person, company, household, or object.

A match might be:

• Exact, where values are identical

• Fuzzy, where values are similar but not equal

• Probabilistic, where multiple attributes contribute to a match score

For example, these records likely describe the same company:

• Acme Corp

• ACME Corporation

• Acme Corp. LLC

Even though the text differs, a data matching process can recognize them as the same entity.

Why data matching matters

Poor matching creates real business problems:

• Duplicate customers inflate counts and waste marketing spend

• Inconsistent vendor records cause payment and compliance issues

• Fragmented customer data breaks analytics and personalization

• Mergers and system migrations fail without accurate matching

Data matching turns messy, fragmented data into a unified and reliable view.

How data matching actually works (step by step)

This is where most guides stay vague. Let’s be concrete.

In practice, data matching follows a repeatable workflow: data is profiled, cleaned, standardized, compared using defined rules or algorithms, scored for confidence, reviewed when necessary, and then merged or linked.

1. Data profiling

Before matching anything, you need to understand your data:

• How complete are the fields?

• How consistent are formats?

• Where are the outliers and anomalies?

Skipping this step guarantees poor results later.

2. Data cleansing and standardization

Matching works best when data follows consistent rules.

This includes:

• Trimming spaces and punctuation

• Normalizing case

• Standardizing addresses, phone numbers, and names

• Removing obvious junk values

Garbage in still means garbage out.

3. Blocking or candidate selection

Instead of comparing every record to every other record, matching systems narrow the search using blocking rules.

Examples:

• Same ZIP code

• Same email domain

• First letter of last name

Blocking improves performance and reduces false matches.

4. Matching and scoring

This is where comparison happens.

Records are evaluated using:

• Exact comparisons

• Fuzzy similarity scores

• Weighted combinations of multiple fields

Each comparison produces a score that reflects match confidence.

5. Review and validation

No matching process is perfect.

High-confidence matches can be automated.
Borderline matches often require review or additional rules.

This step protects data quality and trust.

6. Merge or link records

Finally, matched records are:

• Merged into a single golden record, or

• Linked together while remaining separate

The approach depends on business needs and governance rules.

Common data matching methods explained

Data matching methods differ in how strictly they compare values and how they handle imperfect data. The three most common approaches are exact matching, fuzzy matching, and probabilistic matching.

Exact matching

Exact matching compares values character by character.

Best for

• IDs

• Email addresses

• Account numbers

Limitations

• Fails when data is incomplete or inconsistent

Fuzzy matching

Fuzzy matching measures similarity rather than equality.

Best for

• Names

• Company names

• Addresses

• Free-text fields

Limitations

• Requires thresholds and tuning

• Can produce false positives if poorly configured

Probabilistic matching

Probabilistic matching evaluates multiple attributes together and assigns a likelihood score.

Best for

• Large datasets

• Incomplete or noisy data

• Entity resolution use cases

Limitations

• More complex to configure and explain

• Requires careful validation\

Data matching vs entity resolution vs deduplication

Data matching, deduplication, and entity resolution describe related but distinct concepts. Understanding the difference is important for choosing the right approach.

• •  

  • Data matching: the process of comparing records

  • Deduplication: removing duplicate records within a dataset

• Entity resolution: linking all records related to the same entity across systems

In practice, data matching is the engine that powers both deduplication and entity resolution.

Real-world data matching examples

Deduplicación de CRM

Sales and marketing teams often inherit CRMs filled with duplicates.

Data matching:

• Identifies duplicate contacts and accounts

• Merges engagement history

• Improves reporting accuracy

Customer householding

Retailers and insurers need to group individuals by household.

Matching combines:

• Names

• Addresses

• Relationship logic

This enables better targeting and analytics.

Vendor and supplier matching

Finance teams rely on clean vendor data.

Matching helps:

• Detect duplicate suppliers

• Prevent double payments

• Improve compliance and audits

System migrations and mergers

When organizations merge systems, matching is essential.

It ensures:

• Records are not duplicated

• History is preserved

• Analytics remain accurate post-migration

Common data matching mistakes

Even strong tools fail when the process is wrong.

Relying on a single field

No single attribute is reliable enough on its own in most datasets.

Skipping data preparation

Uncleansed data produces unreliable scores and wasted effort.

Using thresholds blindly

Match thresholds must reflect data quality and risk tolerance.

There is no universal “correct” number.

Treating matching as a one-time task

Data changes constantly. Matching must be repeatable and auditable.

How to choose the right data matching approach

Ask these questions:

• How clean is the data?

• How much risk can you tolerate?

• Do matches need to be explainable?

• Is performance or accuracy more critical?

The answers guide whether exact, fuzzy, probabilistic, or hybrid approaches make sense.

Final thoughts

Data matching is not just a technical exercise. It’s a foundational data quality discipline.

When done correctly, it:

• Reduces costs

• Improves trust in data

• Enables better analytics and decision-making

When done poorly, it silently undermines everything built on top of the data.

If your organization relies on accurate customer, vendor, or entity data, data matching is not optional.

If you’re already working with messy data, we can walk through your use case anytime—just ask or schedule a demo.

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