Data matching techniques: Best practices & challenges

As organizations collect data from multiple systems and touchpoints, inconsistencies and duplication become inevitable. Without a way to connect related records across sources, teams are left with fragmented views of customers, products, and transactions.

Data matching helps solve this problem by linking and consolidating similar records to create a unified, accurate dataset. In this article, we’ll explore the core techniques behind data matching—such as identity resolution and record linkage—along with the common challenges teams face and the best practices for improving match quality at scale.

Main takeaways from this article:

• Data matching is essential for creating a unified view of entities across fragmented data sources.
• Various techniques exist, ranging from exact matching to sophisticated machine learning and graph-based approaches.
• Implementing effective data matching involves addressing challenges related to data quality, scalability, and governance.
• Hybrid matching strategies and robust data governance are key to achieving high match accuracy when matching records at scale.
• Platforms like RudderStack offer advanced capabilities for deterministic and probabilistic identity resolution, supporting custom entity modeling and real-time access.

What is data matching?

Data matching, also known as record linkage or entity resolution, is the process of identifying and linking data records that refer to the same real-world entity across different data sources. It involves comparing data elements or patterns to find matches or similarities, even when there are variations in formatting, spelling, or data quality.

Key benefits of data matching

Data matching plays a critical role in improving how organizations manage and use information across departments. Its impact spans several core functions:

• Streamline operations: Matching helps eliminate duplicate records and consolidate data from multiple sources, reducing manual effort and minimizing errors. This leads to more efficient processes in areas like marketing execution, customer support, and supply chain coordination.
• Improve data quality: By identifying inconsistencies and redundancies, data matching ensures that datasets are accurate, complete, and reliable. Higher data quality means teams can trust the information they use for analysis and decision-making.
• Unlock personalization: A unified view of customer interactions allows teams to tailor communications and experiences across channels. With clearer insight into customer behavior and preferences, businesses can deliver more relevant and timely engagement.
• Ensure accurate reporting and compliance: Reliable data is the foundation for effective reporting and audit readiness. Data matching supports regulatory compliance by maintaining data accuracy, enabling organizations to meet standards like GDPR and CCPA with confidence.

Key applications of data matching

Data matching is a foundational capability with broad relevance across industries. Below are three of the most impactful use cases:

Entity resolution

Entity resolution identifies and connects records that refer to the same real-world individual, organization, or object. In customer relationship management (CRM), it helps unify fragmented profiles into a single, consistent view. This same approach supports master data management (MDM), where businesses build a trusted source of truth for core entities. Entity resolution is also critical in fraud detection, where linking seemingly unrelated records can reveal hidden patterns and suspicious activity.

Record linkage

Record linkage connects information about the same entity across different datasets or systems. In healthcare, for example, a patient's records may be distributed across hospitals, clinics, pharmacies, and insurers. Linking those records provides a complete medical history for better care and analysis. This technique also supports public health surveillance, law enforcement investigations, and multi-source research studies.

De-duplication

De-duplication removes repeated entries within a single dataset. It ensures data accuracy, reduces noise, and improves analysis. Duplicate records often stem from manual input errors or mismatched formats during imports. For instance, marketing databases frequently contain multiple versions of the same contact, leading to inefficient outreach and skewed metrics.

Common data matching techniques

To understand how data matching works, let's examine some common techniques that are available:

Exact matching

Exact data matching is the simplest form of data matching, where records are considered a match only if all specified attributes are identical. While highly accurate when applicable, it is often too restrictive for real-world data, which frequently contains variations, errors, or missing values.

Rule-based or deterministic matching

This method uses a predefined set of rules to determine whether records match. These rules are based on expert knowledge and can include conditions such as matching on exact names, email addresses, or combinations of attributes. While this approach offers transparency and control, it can be labor-intensive to configure and maintain, especially in datasets with high variability or incomplete information.

Probabilistic and fuzzy matching

Probabilistic matching uses statistical models to calculate the probability that two records refer to the same entity.

Fuzzy matching, a subset of probabilistic matching, uses data matching algorithms like Levenshtein distance or Jaro-Winkler to compare and recognize similar but not identical instances, accounting for spelling variations and typos. For instance, fuzzy matching can identify "William Richardson" and "Bill Richardson" as the same person.

Machine learning–assisted matching

In the machine learning–assisted matching method, machine learning algorithms learn matching patterns from labeled data.

Supervised learning models can be trained on pairs of records that are known to be matches or non-matches. These models then predict the likelihood of a match for new record pairs. This approach can be highly effective but requires a significant amount of labeled training data.

Graph-based matching

The graph-based matching model uses a graph to represent entities as nodes and their relationships as edges. Matching is performed by analyzing the structure and properties of the graph to identify clusters of nodes that likely represent the same real-world entity. This method is effective for detecting indirect relationships and uncovering matches that other techniques may overlook.

Common challenges of data matching

Despite the availability of various techniques, data matching is a complex process that presents several challenges:

Dirty, incomplete, or complex data

Inconsistent data formats, missing values, typos, and variations in data representation can complicate the matching process. For example, a default email address like "NA@NA.COM" might appear in many records, requiring careful handling.

Poor data quality has been estimated to cost organizations $12.9 million per year according to Gartner research, underscoring the massive operational and financial impact of unreliable data.

Duplicate records and conflicting attributes

These are inherent issues in fragmented data sets. Even within a single system, data entry errors or system limitations can lead to multiple records representing the same entity. This duplicate data may contain conflicting information for the same attributes, making it difficult to determine the correct value.

Overmatching or undermatching

Overmatching occurs when records that should not be matched are incorrectly linked, while undermatching happens when related records are missed. Finding the right balance between accuracy and flexibility is crucial.

Lack of governance over source data

Without clear data ownership, definitions, and quality standards, it becomes difficult to ensure consistency and reliability across different data sources. Poor governance can lead to ongoing data quality issues that hinder matching efforts.

Scalability and performance issues

Scalability and performance issues can arise when dealing with large volumes of data. For instance, matching billions of records can be computationally intensive and require significant processing power and efficient algorithms.

Difficulty evaluating match accuracy

Another challenge is evaluating match accuracy. Determining the true positive and false positive rates of matching algorithms can be challenging, especially in the absence of a ground-truth dataset. Evaluating the quality of matches often requires manual review or sophisticated validation techniques.

Best practices to match data at scale

To overcome these challenges and achieve effective data matching at scale, the following best practices can help:

Standardize and clean input data early

A matching system is only as good as the data it processes. Start by standardizing formats, correcting errors, filling in missing values, and filtering out noise. Early-stage data cleaning sets the foundation for accurate matches and prevents inconsistencies from propagating downstream. This step also reduces the computational overhead required for later stages of the matching process.

Use hybrid matching strategies

Relying on a single method limits flexibility. A hybrid strategy allows you to combine deterministic matching (exact values like customer IDs or emails) with probabilistic or machine learning techniques that can detect fuzzier, less obvious connections. For example, RudderStack’s Profiles feature blends deterministic, probabilistic, and graph-based approaches to achieve accurate identity resolution in real time. This kind of layered method balances precision and completeness, even as data grows more complex.

Tune thresholds based on business context

Matching accuracy depends on context. There’s no universal rule for when two records should be considered a match. Some use cases, like fraud prevention or compliance, may demand strict precision to avoid false positives. Others, like lead enrichment or marketing outreach, may allow for looser thresholds to capture more potential matches. Calibrating your thresholds based on the intended outcome ensures the system delivers meaningful results.

Incorporate human intervention when needed

Automation is critical for scale, but not every match can or should be decided by an algorithm. Incorporating manual review for edge cases—especially those flagged as uncertain—helps avoid costly errors and builds trust in the system. Creating workflows where data stewards review ambiguous matches helps maintain high standards in sensitive or high-impact areas.

Invest in schema governance

Inconsistent data structures are a common cause of matching failures. Enforcing schema governance across all data sources helps maintain alignment in naming conventions, data types, and formatting rules. Use data dictionaries, automated validation checks, and clear documentation to ensure that data arrives in a predictable structure. This minimizes ambiguity and supports more consistent, scalable matching.

Leverage real-time matching where it matters

In many applications, timing is everything. Use real-time matching to power fraud detection, personalized recommendations, or in-session customer support. When your systems can identify and act on relevant data immediately, you unlock faster decisions, better user experiences, and more timely insights.

How RudderStack solves the identity matching problem

RudderStack is purpose-built to address the challenges of identity resolution across fragmented systems. It offers a modular framework that helps data teams unify customer records, apply advanced matching logic, and create reliable customer 360 views without relying on black-box solutions.

With RudderStack’s Profiles feature, teams can:

• Unify customer data across sources and channels: Profiles brings together behavioral and attribute data from web, mobile, backend systems, and third-party tools to create a consistent, unified view of each customer. It connects activity across sessions, devices, and platforms for a complete profile.
• Configure deterministic and probabilistic matching logic: The platform supports a flexible approach to identity resolution. Teams can define deterministic rules for exact matches while layering in probabilistic algorithms to catch less obvious relationships, adapting the logic to fit their specific data and accuracy goals.
• Model custom business entities: Beyond individual users, RudderStack lets you define and build identities for entities like accounts, households, or organizations. This enables more complex use cases, such as B2B account mapping or multi-user purchase attribution.
• Access identity data in real time or via your warehouse: Profiles offers real-time API access for operational use cases and maintains warehouse tables for analytics. This allows teams to activate identities in customer-facing applications or analyze them within their existing BI tools.
• Track identity evolution with versioned graphs: Every change to the identity graph is tracked and versioned. This auditability helps teams maintain transparency, troubleshoot edge cases, and meet internal or regulatory compliance requirements.

Close the gaps in your customer data with RudderStack

When customer data is scattered across tools and systems, building a complete picture becomes difficult. RudderStack addresses this by giving your team the tools to resolve identities with precision, model custom entities, and create trusted customer profiles at scale.

With support for advanced matching logic and real-time access to unified identities, RudderStack makes it easier to activate clean, reliable data across your stack.

Start with a free trial or get a personalized demo to see how RudderStack can help you solve identity resolution at the infrastructure level.