Intelligent Matching. Connected Data.

Match, link and unify records across multiple datasets—even when data doesn’t look the same.

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Connect Data. Discover Relationships. Create Better Insights.

Connect records across sources

Different systems.
Hidden connections.

Your data is spread across platforms, files and partner datasets. Related records may use different names, formats or structures.

CRM ERP Databases Spreadsheets Partner Data Research Data Cloud Platforms
An illustrative matching example
CRM · Record A ABC Technologies Pvt. Ltd. Website: abc.example · City: New Delhi
Partner data · Record B ABC Tech Private Limited Domain: abc.example · Location: Delhi
Potential relationship identified

Compare names, domains and locations to assess whether the records refer to the same organization.

Fictional example. Shared attributes support comparison; matching rules determine the outcome.

Paxcel helps identify relationships across fragmented datasets, making related records easier to connect and evaluate.

What Is Data Matching?

Data matching is the process of comparing records from one or more datasets to determine whether they correspond to the same or related entity.

One organization, different spellings?
  • Dataset A

    ABC Technologies Pvt. Ltd.

  • Dataset B

    ABC Technologies Private Limited

  • Dataset C

    ABC Tech Pvt Ltd

Highlighted text shows naming variations.
Look beyond exact text

Different names.
A possible connection.

Abbreviations and naming conventions can hide relationships between records.

Exact name comparison

These strings differ, so an exact comparison returns no match.

Intelligent matching

Evaluate name similarities alongside available identifiers, addresses and other attributes.

Compare attributes Evaluate potential matches Confirm the relationship

Why Data Matching Matters

Disconnected data makes it difficult to build a complete view of customers, organizations, products or other entities.

Connect Disparate Data

Match related records across databases, files, systems and external datasets.

Improve Data Quality

Identify relationships between records and reduce inconsistencies across datasets.

Create a Unified View

Bring fragmented information together to create a more complete entity profile.

Improve Analytics

Give analytics teams connected and consistent datasets for better reporting and insights.

Reduce Manual Reconciliation

Automate repetitive record-comparison and data-mapping activities.

Enable Better AI & ML

Create connected datasets that provide a stronger foundation for analytics, machine learning and AI initiatives.

Go Beyond Exact Matching

Real-world data contains variations. Paxcel supports multiple approaches to help identify meaningful matches.

Compare identical values

Exact Matching

Match records where selected attributes are identical. Useful when reliable unique identifiers are available.

ABC123 ABC123
Same identifier across both records.
Apply business logic

Rule-Based Matching

Combine selected attributes using predefined business rules to determine which records qualify as matches.

Same email AND Same date of birth
Both conditions must be satisfied under this rule.
Compare similar text

Fuzzy Matching

Find similar values despite spelling differences, typographical errors and minor variations.

Jon Smith John Smith
Similar spelling can surface a potential match.
Compare pronunciation

Phonetic Matching

Compare names by how they sound, helping identify potential matches with different spellings.

Smith Smyth
Similar pronunciation provides another matching signal.

Key Data Matching Capabilities

Match records across sources using exact, fuzzy, semantic and rule-based approaches to uncover meaningful connections.

Connect fragmented information

Multi-Source Data Matching

Match records across databases, spreadsheets, applications and partner datasets.

Databases Spreadsheets Applications Partner data
Connected records
Compare multiple signals

Intelligent Record Matching

Evaluate multiple attributes to determine whether records are related.

Name
Address
Identifier
Look beyond exact text

Fuzzy & Similarity Matching

Find potential matches despite spelling, formatting and other variations.

Jon Smith John Smith
Understand context

Semantic Data Matching

Identify relationships through contextual meaning, beyond simple text similarity.

Customer support Client assistance
Recognize the same entity

Entity Matching

Connect records that represent the same person, organization or other real-world entity.

Multiple records → One entity
Find shared connections

Cross-Dataset Matching

Compare datasets to find common entities and relationships across them.

Dataset A Dataset B
Accessible to business teams

No-Code Data Processing

Enable analysts and business users to prepare data without writing complex SQL or Python.

Select data Configure Process

Built for Complex Data Environments

Data matching becomes challenging when information comes from different systems.

Different Data Formats

Files, databases and applications can structure the same information differently.

CSV rows JSON objects
Different structures to reconcile.

Different Naming Conventions

The same field or entity may appear under different names across systems.

customer_name clientName full_name
Different labels for related information.

Missing Identifiers

Without a shared customer or organization ID, linking records requires other attributes.

CRM customer ID C-1042
Partner customer ID Not available
No common key to join the records.

Spelling Variations

Typos, abbreviations and formatting differences can hide potential matches.

Jon Smith John Smith
Similar names, different text.

Large Data Volumes

Comparing records across millions of rows can demand significant processing resources.

More records. More potential comparisons.

Fragmented Data

Each department or organization may hold only part of the information you need.

Sales: purchases Support: tickets Finance: invoices
One customer’s context, spread across systems.

Data Matching Use Cases

Connect records across customer, finance, healthcare, education, research and marketing systems for a more complete view of your data.

Customer 360

Connect customer records across departments to bring identity, activity and interactions together.

CRM Sales Support Finance
A more complete view of each customer.

Financial Services

Match customer information across banking, lending and credit-card systems.

Banking Lending Credit cards
Connected identities to support analytics and risk workflows.

Healthcare

Connect patient, provider or organizational records held across different healthcare systems.

Patient records Provider directories Partner systems
More consistent connections across healthcare data.

Higher Education

Match student, institution, enrollment and research records across institutional datasets.

Admissions Enrollment Research
Connected records for reporting and cross-institution analysis.

Research & Consortiums

Identify common entities in information submitted by institutions, agencies and partners.

Institutions Agencies Partners
More consistent datasets for collaborative research.

Marketing & CRM

Match prospect and customer records across marketing platforms and CRM systems.

Lead forms Campaign platforms CRM
Cleaner audiences and more connected campaign insights.

Data Matching for Customer 360

A customer may exist across multiple systems:

Customer records across systems
  • CRM John Smith
    Candidate
  • Sales J. Smith
    Candidate
  • Support Jonathan Smith
    Review
  • Finance John A. Smith
    Candidate
  • Partner Data J Smith
    Candidate
Build a connected customer view

John Smith

Evaluate related records across sources

Compare available matching signals
Name Email Phone Address Customer ID
Sales Purchase history
Support Service interactions
Finance Billing information
Partners External relationships

Connect confirmed matches. Keep uncertain records separate until the relationship is resolved.

One customer. Multiple records. One connected view.

Paxcel helps evaluate which records belong together, bringing confirmed relationships into a more complete customer picture.

Why Choose Paxcel?

Intelligent Matching

Support multiple matching approaches—from exact and rule-based to fuzzy and semantic matching.

Enterprise Data Processing

Designed for complex, multi-source data environments.

No-Code Experience

Enable analysts and business users to participate in data preparation.

Secure Architecture

Maintain control over sensitive data during processing.

Data Sovereignty

Keep data within your controlled environment.

Integrated Data Quality

Connect Data Matching with Data Cleansing, Deduplication and Validation workflows.

FAQs

What is data matching?

Data matching is the process of comparing records from different datasets to identify whether they represent the same or related entities.

Data matching identifies relationships between records, while deduplication focuses on identifying and resolving duplicate records.

Yes. Deduplication workflows can use fuzzy, phonetic, similarity-based and other matching approaches to identify records that are similar but not identical.

Fuzzy data matching compares records based on similarity rather than requiring an exact match. It can help identify records containing minor spelling, formatting or textual differences.

Semantic data matching evaluates the meaning and context of information rather than relying only on exact or approximate text similarity.

Yes. Paxcel’s platform is designed to bring together disparate datasets and support data collection, cleansing, matching and consolidation workflows.

Paxcel’s Data Collaboration Suite emphasizes no-code data processing and provides tools that allow business analysts and researchers to prepare and transform data without writing SQL or Python.

Paxcel states that processing occurs within the customer’s environment and highlights encryption, access controls, audit trails and data-sovereignty capabilities.

Data matching can support financial services, healthcare, education, research, retail, e-commerce, government and other organizations working with fragmented or multi-source data.

Turn Raw Data Into Usable Data

Stop spending hours manually extracting and aligning information.

Use Paxcel to extract, standardize, and prepare your data for analytics and AI.

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