The Data Engineer's Guide to Entity Resolution

Build your Entity Resolution pipeline the right way, the first time.

Sam Bortol
05/16/25
20 min

1. Introduction

1.1 What is Entity Resolution?

Entity Resolution (ER) is the process of identifying and linking records across datasets that refer to the same real-world entity - whether it's a person, business, address, or other object.

In practice, this means answering questions like:

• Are “Alice Johnson” and “A. Johnson” in different datasets the same person?
• Do “Tech Solutions LLC” and “Tech Solutions Limited” refer to the same company?
• Are two differently formatted addresses actually the same physical location?

Done well, entity resolution creates cleaner, more trustworthy data, which forms the necessary backbone for better analytics, signal extraction and customer experiences.

1.2 Why it matters

Messy, non-deduplicated data is often a significant blocker for downstream decision-making and automation. Entity resolution helps you:

• Eliminate duplicates that inflate company headcount or lead to redundant outreach
• Join records across sources where there’s no shared unique identifier
• Enrich data with external information while avoiding mismatches
• Build accurate customer or company profiles that support sales, marketing, investing and recruiting.

Whether you’re resolving records from multiple sources, enriching internal data or standardizing business information across systems, entity resolution is a critical (and often underestimated) process.

1.3 Who this guide is for

This guide is designed for data practitioners and technical teams ingesting and/or joining large-scale datasets (and oftentimes more than one). Whether you’re just starting to explore entity resolution or are looking for ways to scale your approach, this guide is for you.

The intended audience includes:

• Data engineers designing ETL pipelines or matching logic
• Analysts validating deduplication and other data quality metrics
• Technical onboarding teams supporting customer integrations
• Product and operations teams responsible for profile accuracy and scale

In this guide, we’ll walk through both foundational techniques as well as more advanced strategies with clear examples and implementation notes throughout.

2. Building an Entity Resolution Pipeline

Entity resolution isn’t a one-off task - it’s an ongoing process. An ER pipeline helps you operationalize everything from cleaning and matching to outputting usable, deduplicated data.

2.1 Pipeline Overview

A strong entity resolution pipeline typically includes the following stages:

1. Ingestion: Bring in raw data from various sources (i.e. CRM, external data providers, APIs, etc)
2. Cleaning & Preprocessing: Deduplicate, fix typos and initial processing to ensure field consistency
3. Standardization and Parsing: Apply common formats to fields like phone, addresses, names, etc and decompose compound fields into subcomponents for better matching (e.g. splitting full names)
4. Matching Logic: Use a mix of rule-based strategies, machine learning and external references to decide which records are match candidates
5. Resolution Decision: Decide which matching records to merge, flag or hold for manual review (including exact matches, probable matches, uncertain/outlier cases)
6. Output & Integration: Resolve matches and load into your downstream systems (e.g. data warehouse, CRM, etc)

2.2 Tips for Scalability and Maintenance

• Modularize each pipeline stage so it can be reused across datasets or projects
• Log your match decisions (especially for fuzzy/ML-based matches). This can make auditing and future improvements much easier to implement
• Version your rules and models, and monitor metrics like match rate, false positives, and unresolved records
• Include manual review paths for low-confidence matches, especially early in development, but even at scale.

Over the remainder of this document, we’ll explore each of the core pipeline stages in detail - so let’s dive in!

3. Foundation: Preparing Your Data

Before any sophisticated matching can happen, your data needs to be in shape. Preparing for entity resolution starts with cleaning, standardizing and parsing your records so that downstream processes work on reliable inputs.

3.1 Data Cleaning and Preprocessing

Removing Duplicates

Duplicate records are one of the most common data quality issues. Even exact copies can sneak into a dataset due to repeated imports, form resubmissions or batch errors.

ID    Name           Email
1     Alice Johnson  alice@example.com
2     Alice Johnson  alice@example.com

Solution

• Identify duplicates based on stable fields (e.g. email, phone number, or ID)
• Use SQL queries, data cleaning libraries (like pandas.drop_duplicates()) or deduplication tools to remove simple duplicates

Correcting Typos and Errors

Simple misspellings can derail matching logic, especially for names and email addresses.

Name: Bbo Smith
Email: bob.smith@example.com

Solution

• Apply spell-check tools or fuzzy matching to catch and correct errors
• In high-sensitivity cases, add a manual review step to ensure accuracy

3.2 Data Standardization and Normalization

Standardization ensures that data across records follows the same format, which is crucial for reliable comparisons.

Phone Numbers and Addresses

Phone numbers can appear in dozens of formats. Likewise, addresses may use different abbreviations, casing or country codes.

(123) 456-7890
123.456.7890
+1 123-456-7890

Solution

• Convert phone numbers to a standard format like E.164 ( +11234567890)
• Normalize addresses using an API (e.g. Google Maps, USPS, OpenStreetMap)

3.3 Parsing and Decomposition

Breaking complex fields into structured components gives you more to work with when matching.

Email Parsing

From an email address, you can often infer name and organization details

Email: john.doe@corporation.org
→ First Name: John
→ Last Name: Doe
→ Domain: corporation.org

Address Parsing

Addresses often need to be decomposed into street, city, state, and zip/postal code.

Address: 456 Elm St Apt 12B, Metropolis, NY 10001
→ Street: 456 Elm St
→ Apartment: Apt 12B
→ City: Metropolis
→ State: NY
→ ZIP: 10001

Implementation

• Use regular expressions or address parsing libraries (like useaddress, libpostal or commercial APIs) to split emails and addresses into their constituent parts
• Always test against real-world data to catch and handle edge cases

3.4 (Bonus) PDL Cleaner APIs

We at PDL have various data cleaning and standardization systems in place for our own entity resolution pipelines. We offer 3 Cleaner APIs specifically for cleaning:

• Locations
• Companies
• Schools

Each of these endpoints takes in a raw string and returns cleaned, structured and canonicalized JSON structures representing the resolved entity.

“Portland Oregon” →
{
  "name": "portland, oregon, united states",
  "locality": "portland",
  "region": "oregon",
  "subregion": "multnomah county",
  "country": "united states",
  "continent": "north america",
  "type": "locality",
  "geo": "45.52,-122.67",
}

By investing in these foundational steps, you will dramatically improve the effectiveness of the matching and entity resolution techniques we’ll cover in the following sections.

4. Rule-Based Matching Techniques

Once your data is clean and structured, the next step is to compare records using deterministic rules. Rule-based matching doesn’t rely on training data or machine learning - it’s transparent, tunable and often highly effective for known patterns and business logic.

4.1 Phonetic Algorithms

Phonetic algorithms help catch similar-sounding names that may be spelled differently.

"Steven Clark" vs. "Stephen Clarke"
→ Both convert to: STFN KLARK (Double Metaphone)

Common Techniques

• Double Metaphone: A relatively accurate phonetic-coding algorithm that works well across names and languages
• Soundex: A simpler phonetic-coding technique, but that is more limited than the Double Metaphone technique.

4.2 Fuzzy Matching

Fuzzy matching calculates similarity between strings to help identify values that are “close enough”.

"Micheal Johnson" vs. "Michael Jonson"
→ Levenshtein Distance: 2

Key Techniques

• Levenshtein Distance: A string similarity metric that scores using the edit distance between two strings (insertions, deletions, substitutions)
• Jaro-Winkler: Another string similarity metric that prioritizes the beginning-of-string similarity

4.3 Unique Identifiers

When present, unique IDs are the fastest and most accurate way to link records.

Dataset A: A1002 — Peter Griffin
Dataset B: A1002 — peter@example.com
→ Match on ID “A1002”

Best Practices

• Use stable identifiers like CRM IDs, PDL profile IDs or hashed emails
• For missing IDs, you can infer matches through consistent fields like email or phone (but take care as overly lax rules here can lead to incorrect matching errors)

4.4 Composite Keys and Cross-Field Matching Strategies

When no single field is a reliable identifier on its own, you can use a combination of fields instead to create composite keys.

Record A: Anne Hall, 1990-05-15
Record B: Annie Hall, 15/05/1990
→ Standardize DOB → Create composite key: Name + DOB

4.5 Handling Nicknames and Variations

Nicknames and common misspellings can cause mismatches in otherwise clear cases.

"Bill Thompson" vs. "William Thomson"
→ "Bill" is a nickname for "William"
→ "Thompson" vs. "Thomson" = 1-character difference

4.6 Company-Specific Rules

Company data brings its own matching challenges - legal suffixes, vanity domains, and contact details all play a role.

"Tech Solutions LLC" vs. "Tech Solutions Limited"
→ Same LinkedIn URL, matching website
→ Normalize suffixes and phone formats

4.7 Geocoding and Location-Based Matching

Addresses can vary dramatically in format - but geographic coordinates are consistent, making them a powerful way to match records more accurately.

Using Latitude/Longitude for Address Comparison

"789 Pine Road, Smallville, KS"
"789 Pine Rd, Smallville, Kansas"
→ Both geocode to (37.094, -95.712)

Steps

• Use a geocoding service (e.g. Google Maps or OpenStreetMap) to convert addresses to lat/lon coordinates
• Compare the resulting coordinates using distance thresholds to define an acceptable margin of error (e.g. 5 miles).

5. Machine Learning Approaches

Rule-based systems are powerful, but have limits - especially when dealing with ambiguous records or large datasets with subtle inconsistencies. This is where machine learning can help by improving precision and recall at scale.

5.1 Supervised Learning

There are a wide range of Machine Learning (ML) techniques that can be applied to an entity resolution pipeline. In this section, we’ll cover a traditional supervised learning approach for probabilistic matching, which provides an easy entry point into this class of techniques.

Probabilistic Matching

Instead of hard rules, machine learning lets you work with likelihoods. Any classifier trained with the approach above will take in a set of similarity features and learn to output a probabilistic matching score that you can use to set thresholds or triage results.

Example Workflow

Here’s an example of what an ML-based workflow could look like:

1. Label a set of record pairs as “Match” or “No Match”
2. Extract similarity features for each pair of records
3. Split your dataset into a training set and test set
4. Train a model using the training set data
5. Evaluate the model’s performance using the test dataset

Feature Engineering

The first step in ML-based entity resolution is transforming raw data into features that reflect similarity.

Some examples of such features could include:

• Name similarity score (e.g. Levenshtein Distance)
• Email exact match (binary 0 or 1)
• Address similarity score (e.g. Jaccard index or fuzzy match score)
• Phone number match (binary 0 or 1)
• Domain match from email or website

These collections of features become the input variables for the model, and should be generated in a consistent manner for each record in the training dataset as well as the test set.

Training Labels

For the training process, you will also need to create ground truth training labels by assigning pairs of records as “match” (1) or “no match” (0) based on prior knowledge.

Training Data vs Test Data

Next, you should divide your dataset up into a training set and a test set. Take care to ensure that the distribution of matches vs no matches is roughly similar across both datasets, and in particular ensure that no data from your test set is used in the training process.

Training a Classifier

Once the features are created, labels are assigned and the data is split, you can train a classifier model to learn what combinations of signals suggest a match. Model training should be done on only the training dataset. The test set should only be used for benchmarking the fully trained model after the training process has completed.

6. Conclusion

Entity resolution is a foundational process for any team working with large, messy, or multi-source datasets. By systematically cleaning, standardizing, and matching records, you unlock more accurate analytics, stronger automation, and better decision-making abilities.

We hope this guide has helped demystify the process and given you practical tools and knowledge to build and scale your own ER pipeline.

We believe that clean, connected data isn’t just a technical goal - it’s a strategic advantage.