Entity resolution is the process of identifying when different records refer to the same real-world entity. It compares attributes like names, addresses, and identifiers using similarity algorithms to find matches. For businesses, this unifies fragmented customer, vendor, or product data into accurate profiles. Without it, duplicate records inflate counts and fragment important relationship history.
The same customer appears three times in your CRM with slightly different names.
Your finance team spends hours manually matching invoices to the right accounts.
Your reports show 15,000 customers, but you really have 9,000 with duplicate entries.
The same entity can have many faces. Your systems need to recognize them as one.
DATA INFRASTRUCTURE LAYER - Unifies fragmented records into coherent entities.
Entity resolution identifies when two or more records refer to the same real-world entity despite differences in how that entity is represented. "John Smith" at "123 Main St" and "J. Smith" at "123 Main Street" are likely the same person, but your systems do not know that without explicit logic.
The process compares attributes like names, addresses, emails, and phone numbers using similarity algorithms, then applies rules or machine learning to decide: same entity or different? Get it wrong and you merge records that should be separate. Get it right and fragmented data becomes unified profiles.
Entity resolution is not about cleaning data. It is about discovering hidden relationships between records that appear independent but represent the same underlying reality.
Entity resolution solves a universal problem: how do you recognize the same thing when it appears in different forms? The pattern applies anywhere identity must be established across fragmented sources.
Start with records from different sources. Compare key attributes using similarity measures. Apply matching rules to identify likely matches. Merge or link records that represent the same entity.
Your CRM has 6 customer records. Some are duplicates of the same person. Adjust matching strictness to see how entity resolution identifies them.
Exact rules, exact matches
Define explicit rules based on key fields. If email matches exactly, same entity. If name fuzzy-matches AND zip code matches, same entity. Rules are transparent and predictable but miss variations they were not programmed to handle.
Weighted similarity scores
Calculate similarity scores across multiple fields. Weight each field by discriminative power (email is more unique than first name). Combine scores into a match probability. Records above a threshold are considered the same entity.
Learn patterns from examples
Train a model on labeled pairs of matching and non-matching records. The model learns which attribute combinations indicate matches. Works well when you have complex data and training examples to learn from.
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How consistent is your data quality?
The ops director asks this question. The CRM shows 15,000 contacts, but many look like duplicates. Entity resolution compares records, identifies matches, and reveals the true count of 9,000 unique customers with consolidated profiles.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Your rules are too lenient. Two people named "John Smith" in the same city get merged into one record. Now customer A sees customer B orders, and support gives the wrong information to both.
Instead: Require multiple attribute matches, not just name. Add secondary identifiers like phone, email, or account number.
Your rules are too strict. "Robert Johnson" and "Bob Johnson" at the same address stay as separate records. Your customer count is inflated and marketing sends duplicate communications.
Instead: Implement nickname matching, fuzzy string matching, and probabilistic scoring. Accept that some false positives are better than massive duplication.
You match primarily on phone number or email. People change these frequently. Past records stop matching to current ones, and you lose relationship history.
Instead: Use stable identifiers like SSN or internal IDs when available. Fall back to composite matching on name + address + date of birth for stability.
Entity resolution identifies when different database records refer to the same real-world entity despite variations in how that entity is represented. It compares attributes like names, addresses, emails, and phone numbers using similarity algorithms, then applies matching rules to determine if records should be linked. The result is unified profiles instead of fragmented duplicates.
Implement entity resolution when you consolidate data from multiple sources, notice duplicate records affecting report accuracy, or need a single view of customers or vendors. Common triggers include post-acquisition data merges, CRM cleanups, and reporting discrepancies where counts seem inflated. If your team manually matches records, automation through entity resolution saves significant time.
Deterministic matching uses explicit rules based on exact field matches. If email matches, same entity. Probabilistic matching calculates similarity scores across multiple fields and uses thresholds to decide. Deterministic is fully explainable but misses variations. Probabilistic handles fuzzy matches better but requires tuning. Most production systems combine both approaches.
Over-merging happens when matching rules are too lenient, combining records of different entities who happen to share names. Under-merging happens when rules are too strict, missing legitimate duplicates with slight variations. Another mistake is matching on unstable attributes like phone numbers that change frequently, losing historical connections when contact info updates.
Entity resolution creates accurate counts by eliminating duplicate inflation. It builds complete profiles by combining partial information from multiple records. It reveals hidden relationships, like discovering two contacts work for the same company. Clean entity data flows into downstream systems like analytics, marketing, and customer service with consistent, trustworthy information.
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Choose the path that matches your current situation
You have duplicate records but no matching system
You match on exact identifiers but miss fuzzy duplicates
Matching works but you want better accuracy
You have learned how to identify when different records refer to the same entity. The natural next step is understanding how to merge those matched records into unified profiles.