Record Linkage: Linking Data from Different Sources Without Using Common Keys

Modern organisations rarely keep data in one place. Customer details may sit in a CRM, transactions in a billing system, support issues in a ticketing tool, and marketing responses in a separate platform. When these systems do not share a consistent ID, analytics becomes fragmented. Record linkage solves this by identifying which records across sources refer to the same real-world entity, even when no common key exists. For learners exploring real data integration problems in a data scientist course in Nagpur, record linkage is a practical skill that shows up in customer analytics, fraud detection, healthcare data, and government registries.

What Record Linkage Really Means

Record linkage (also called entity resolution or deduplication) is the process of matching records that represent the same person, company, address, or product across datasets. The difficulty is that fields may be incomplete, inconsistent, or noisy. For example, “R. Sharma” in one dataset might be “Rahul Sharma” in another, with slightly different phone formats, spelling variations, or old addresses.

Good record linkage typically handles:

• Variation: nicknames, typos, abbreviations, transliteration
• Missingness: blank phone numbers or partial addresses
• Conflicts: two sources disagree on date of birth or pincode
• Duplicates: multiple entries for the same entity inside one system

Core Approaches to Linking Records Without Keys

Deterministic (Rule-Based) Matching

This is the simplest approach: create rules such as “match if email is equal” or “match if phone number and last name are equal”. Deterministic matching is fast and easy to explain to stakeholders, but it breaks when data quality is poor. It also tends to miss true matches (low recall) if fields are missing or inconsistent.

Use deterministic rules when:

• You have a few strong identifiers (email, GSTIN, PAN—when legally appropriate)
• Data is clean and standardised
• You need a transparent baseline quickly

Probabilistic Matching (Statistical Record Linkage)

Probabilistic linkage assigns a match score based on how likely two records refer to the same entity. Instead of strict equality, it uses similarity signals: name similarity, address similarity, distance between dates, and frequency-aware weighting (rare surnames carry more signal than common ones).

A common design is:

• Compare fields using similarity functions (e.g., edit distance for names)
• Weight comparisons based on field reliability and uniqueness
• Combine signals into a final probability or score
• Set thresholds for “match”, “non-match”, and “needs review”

This approach is widely used because it handles noise better and gives measurable confidence.

Machine Learning-Based Entity Resolution

ML methods learn how to match from labelled examples (pairs marked “same entity” vs “different entity”). You typically engineer pairwise features such as:

• Jaro-Winkler similarity for names
• Token overlap for addresses
• Exact match flags (email/phone)
• Numeric differences (age, invoice amount bands)

Then you train a classifier (logistic regression, gradient boosting, etc.). ML can outperform rules, but it needs representative training data and careful monitoring for bias and drift. In many capstone-style projects within a data scientist course in Nagpur, teams combine rules (for strong identifiers) with ML (for messy records).

A Practical Record Linkage Pipeline That Works

A reliable pipeline usually includes these steps:

1. Standardise and clean
2. Normalise casing, remove extra spaces, standardise phone formats, split full names, parse addresses, and handle common abbreviations (Rd/Road). This step often delivers the biggest quality improvement.
3. Blocking to reduce comparisons
4. Comparing every record with every other record is expensive. Blocking narrows candidates using coarse keys like pincode, city, first letter of surname, or phone prefix. The goal is to reduce computation while keeping true matches inside the same blocks.
5. Field comparison and scoring
6. Compute similarity for candidate pairs. Use appropriate similarity functions for each field type (strings, dates, categories).
7. Decision thresholds + human review loop
8. Use three zones:

• High score → auto-match
• Low score → auto-reject
• Middle zone → manual review (or delayed resolution)
• This design protects quality while controlling operational effort.

1. Create a “golden record”
2. Once matched, merge records into a single profile with survivorship rules (e.g., latest address wins, verified phone wins). This is crucial for downstream analytics.

Measuring Quality and Avoiding Common Mistakes

Record linkage must be evaluated like any other model or decision system. Track:

• Precision: how many predicted matches are correct
• Recall: how many true matches you successfully found
• F1 score: balance of precision and recall
• Clerical review rate: percent requiring manual validation

Common mistakes include over-matching (merging different people with similar names) and under-matching (missing real duplicates due to conservative thresholds). Another frequent issue is ignoring feedback loops. When reviewers correct matches, that data should improve rules or retrain models.

Conclusion

Record linkage is the backbone of trustworthy analytics when datasets do not share a common identifier. It combines careful data cleaning, smart candidate generation, robust matching logic, and disciplined evaluation. Whether you use deterministic rules, probabilistic scoring, or machine learning, the goal remains the same: create accurate, auditable links that reduce duplicates and strengthen decision-making. For practitioners building real-world data products—especially those practising integration-heavy use cases in a data scientist course in Nagpur—record linkage is a skill that quickly turns messy, siloed data into a usable foundation for reporting, modelling, and automation.