Data matching services

Eliminate data silos with DoubleData's high-precision data matching services, a unified data foundation for large-scale operations and analytics.

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Capabilities

Data matching: the key to data clarity and actionable insights

Data matching, also known as record linkage or entity resolution, finds the records that belong to the same entity across one or more datasets, then links and merges them. Advanced matching techniques uncover these connections even when records share no common unique identifier or differ in spelling, formatting or completeness. Dedicated data matching services enable companies to:

Unify product data

Keep product data in sync across systems and partners, so every listing and stock level matches.

Merge customer data

Merge scattered customer data from different systems into one profile for better personalization and marketing.

Leads matching

Connect leads from different databases and platforms into one contact record for sharper targeting and faster sales.

Remove duplicates

Remove duplicate customer, product and transaction records for cleaner data.

Market reporting & benchmarking

Bring data from multiple sources together for market research, competitor analysis and benchmarking across platforms.

Enrich data with external sources

Enrich their data with third-party or partner datasets for a fuller view of each customer or product.

Map data between systems

Convert data formats between CRM, ERP and other tools, so data moves between them without manual fixes.

Enable personalization

Use matched data to personalize experiences, recommendations and offers at scale.

Use cases

Explore our capabilities across industries and teams

Filter success stories by vertical, use case, or department to find the scenarios that match your needs. Learn how enterprises leverage our custom scraping, matching, and infrastructure to solve their toughest data problems.

Airline route & network analysis

Flight paths, frequencies, and capacity data for airline route and network analysis, from our managed scraping and matching service. Book a demo.

Airlines

Pack size matrix & unit pricing

Compare FMCG pack sizes like for like, with per-unit prices for product variations and bundles across retailers. Book a demo today.

FMCG

Delivery fee tracking

Delivery fee, service charge, and small order fee data across food delivery platforms, to optimize pricing and protect margins. Book a demo.

Food delivery

Exclusive vs. shared content

Exclusive vs. Shared Content data for audiobooks: see which titles sit on one platform or many, from our managed data service. Book a demo today.

Audiobooks

Event gap analysis (missing content)

Find the events and artists your competitors list but your ticketing platform does not. Book a demo.

Ticketing

ETA benchmarking

Benchmark your food delivery operations against the delivery time estimates competitors show across platforms. Request a demo.

Food delivery

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Challenges

Unmatched data holds your enterprise back

For large organizations, data disparity is a strategic roadblock. You're likely grappling with:

Lack of standardization

Many companies don't provide standardized identifiers like EANs or SKUs. The same Coca-Cola product might be listed differently from store to store, with its own images and descriptions, which makes automatic product and price monitoring impossible.

Manual data cleaning

Different naming conventions and inconsistent formats leave analysts and data scientists reconciling and cleaning data by hand when they should be focused on analysis and strategic projects.

Duplicates in CRM

Without a consistent data entry process, users enter the same information in slightly different ways. That creates duplicate records and messy reports, and it confuses teams that rely on accurate data for their work.

High technical requirements

Effective data scraping and matching take specialized technical skills that your in-house team may not always have, and that limits how quickly you can scale data-driven initiatives.

99.93%

Data accuracy

We rigorously cross-check every dataset across multiple sources to ensure entity-level precision, so you get clean, usable data.

15B+

Data points extracted

Our infrastructure handles massive data volume, from granular app content to multi-layered e-commerce listings, at true enterprise-grade scale.

99.89%

System uptime

Your data should keep up when your market moves. Our pipelines are designed for high availability and constant monitoring: a reliable data flow, backed by a 99.9% uptime SLA.

4.2TB+

Processed monthly

We process and normalize terabytes of structured data every month, optimizing for schema consistency, transformation accuracy, and downstream usability.

Data audit

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Identify gaps and optimize your web & mobile data pipeline. It's completely free and without obligation.

Process

DoubleData's approach to enterprise data unification

Our methodology brings clarity and control to your enterprise data, step by step.

Strategic deep dive
We collaborate with your stakeholders to understand strategic goals, data sources, existing pain points, and define clear KPIs for the matching initiative.
Custom matching blueprint
We architect a bespoke matching strategy, defining entities, key identifiers, advanced rule-sets (including sophisticated fuzzy matching), and tailored validation protocols for your enterprise data.
Data cleansing
Your data is cleaned and prepared to ensure the highest quality input for the matching engine.
Matching execution
Our infrastructure (industry-dedicated ML algorithms) processes and matches your data with precision, ensuring security and compliance at every step.
Rigorous validation
We perform comprehensive quality assurance, including exception handling and iterative refinement, to meet the stringent accuracy demands of enterprise applications.
Integration & delivery
We deliver production-ready matched data for integration with your target systems (data warehouses, MDM hubs, analytics platforms) and ensure your team understands the solution.
Continuous governance & optimization
We offer ongoing support for match rule evolution, periodic data refresh, and performance optimization to ensure sustained data integrity as your business evolves.

Our edge

Data matching approaches compared

Dedicated data matching services stand out because they combine accuracy, automation, scalability, and speed. They save valuable analyst time, need no constant maintenance, and are purpose-built for efficient, enterprise-grade matching.

Feature comparison
Pros	Cons
Manual Matching
No upfront costs Easy to start with small datasets	Very time-consuming High risk of errors Not scalable Drains analyst time
In-house Tools
Customizable to specific needs Full control over logic and data flows	Requires technical expertise High maintenance effort Hard to scale
General ETL Platforms
Good for standard transformations Often already in use for other integrations	Complex configuration for matching May require custom coding Not optimized for matching logic
Dedicated Data Matching Services
Purpose-built for matching use cases High accuracy with fuzzy logic Scalable and automated Faster time-to-value	May involve licensing costs Requires integration effort (but typically minimal)

Feature comparison
Features	Pros	Cons
Manual Matching	No upfront costs Easy to start with small datasets	Very time-consuming High risk of errors Not scalable Drains analyst time
In-house Tools	Customizable to specific needs Full control over logic and data flows	Requires technical expertise High maintenance effort Hard to scale
General ETL Platforms	Good for standard transformations Often already in use for other integrations	Complex configuration for matching May require custom coding Not optimized for matching logic
Dedicated Data Matching Services	Purpose-built for matching use cases High accuracy with fuzzy logic Scalable and automated Faster time-to-value	May involve licensing costs Requires integration effort (but typically minimal)

Elevate your enterprise with a unified data strategy

Stop letting data complexity dictate your limits. Partner with DoubleData to build a resilient, accurate, and actionable data foundation. That means:

Improved ROI
Reduced costs
Smarter and faster decisions
Boosted operational efficiency
Enhanced customer experience
Ensured compliance and mitigated risk

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