Data Matching

Can you truly compare the same companies, products, or locations across different sources?

Many organisations have datasets about the same subject: product files from suppliers, customer records from several systems, or market data from public sources. In practice, small differences in names, codes, addresses, and structures prevent direct connections.

Scrape IT automatically identifies corresponding records and combines them into one reliable information picture.

Why data matching is becoming increasingly important

The number of data sources within organisations continues to grow. Without matching, records remain fragmented, duplicates persist, and analyses compare inconsistent information.

Reliable matching is therefore a prerequisite for automation, reporting, AI, and data-driven decision-making.

Expert insight

The greatest data-quality problem is often not missing data, but failing to recognise that two different records describe the same entity.

Common data-matching challenges

Products are difficult to compare

Suppliers and competitors use different names, codes, descriptions, units, and specifications for identical or comparable products.

Companies occur more than once

The same organisation may appear under a trading name, legal name, former address, or spelling variation.

Datasets do not align

Sources use different structures, identifiers, and quality standards, preventing straightforward joins.

Manual linking takes too much time

Checking records individually is slow, inconsistent, and impossible to scale.

From separate datasets to one reliable information picture

Scrape IT combines deterministic rules, identifiers, characteristics, and intelligent matching logic. Matches can be assigned confidence levels, validated, and supplied with an audit trail.

Applications include product matching, company matching, location matching, deduplication, master-data management, and data enrichment.

Which data can be matched?

Product matching

Connect identical and comparable products using codes, brands, names, specifications, categories, and custom rules—even when no shared EAN or SKU exists.

Company matching

Recognise organisations across CRM, ERP, supplier files, registers, and public datasets despite variations in name or address.

Location matching

Connect addresses, branches, properties, and geographic objects using structured location characteristics.

Deduplication

Identify duplicate records and consolidate them into one consistent customer, supplier, product, or location view.

Dashboard

Review matches, confidence levels, unresolved records, and data-quality developments from one central dashboard.

Practical example: from separate datasets to one customer or product view

Instead of asking Which dataset is correct?, teams can ask How do we combine all available information into one dependable record?

Automated matching preserves valuable attributes from every source while preventing duplicate or contradictory records.

What does data matching deliver?

Organisations improve data quality, reduce duplicate records, produce more reliable analysis, automate processes, and spend less time on manual verification.

Who is it for?

Pricing and category management compare products, sales and CRM teams create one customer view, procurement connects supplier data, BI teams build consistent models, and IT establishes a reliable source of truth.

Combine data matching with

MCP AI data integration

Let AI work with correctly connected datasets.

Dashboarding

Use consistent matched data as the basis for reporting.

Price Monitoring

Match products before comparing their prices.

Market Intelligence

Connect information from several market sources into one view.

From separate datasets to Data Intelligence

Organisations often already possess the data required for better decisions; it is simply distributed across systems. Matching creates consistent, complete information that analysis, automation, dashboards, and AI can trust.

Frequently asked questions

What is data matching?

Data matching automatically recognises and connects the same companies, products, locations, or other entities across different sources, creating one consistent dataset.

Why is data matching important?

Data from different systems rarely aligns exactly. Matching creates a more complete information picture, improves quality, and makes analysis more reliable.

Which data can be matched?

Products, companies, customers, suppliers, locations, addresses, objects, and other uniquely identifiable entities can be matched.

How does data matching work?

Names, codes, addresses, specifications, and other identifying characteristics are compared using rules and intelligent matching logic.

Is data matching only suitable for large datasets?

No. It benefits both small and large datasets, although its value generally increases with the number of records and sources.

Can data matching include external sources?

Yes. Internal records can be combined with public sources and market information.

Which organisations benefit from data matching?

Retailers, wholesalers, manufacturers, e-commerce companies, financial service providers, and organisations using several data sources benefit from matching.

How is matched data delivered?

Delivery is possible through APIs, dashboards, CSV, Excel, JSON, XML, Parquet, Power BI, MCP, EDI, SFTP, email alerts, or direct integrations.

Can data matching be combined with other Scrape IT solutions?

Yes. It is commonly combined with MCP AI data integration, dashboarding, price monitoring, and market intelligence.

What is the difference between data matching and CRM enrichment?

Data matching recognises and connects records across sources. CRM enrichment supplements and updates customer and prospect records. Combining both improves consistency and completeness.