01
Products are difficult to compare
The same product can have a different name, item code, or description at different suppliers. This makes automatic comparison almost impossible.
Data Integration Connect datasets automatically and reliably
Data Matching
Automatically connect companies, products, and locations. Improve data quality with intelligent data matching and reliable dataset connections.
Can you truly compare the same companies, products, or locations across different data sources?
Many organisations have several datasets that essentially cover the same subject.
Consider product files from different suppliers.
Customer data from multiple systems.
Or market data from a variety of public sources.
In practice, however, this data often differs just enough to prevent it from being connected automatically.
A product has different item numbers.
A company name is written differently.
An address is incomplete.
Or the same organisation appears more than once.
This creates fragmented information and makes comparison, analysis, and automation difficult.
Scrape IT helps organisations automatically connect, validate, and enrich datasets using data matching.
This creates one reliable information picture that analyses, dashboards, and business processes can trust.
Why this matters
Why data matching is becoming increasingly important
Organisations collect more and more data.
But data from different sources rarely aligns one-to-one.
Examples include:
- different product codes
- variations in company names
- multiple ways of writing addresses
- missing attributes
- different data structures
- duplicate records.
Without data matching, these datasets remain separate information sources.
Automatically connecting data creates one consistent dataset that organisations can use for more reliable analysis and automation.
The quality of analyses is not determined by the amount of data. It is determined by how accurately data is connected. Reliable information only emerges when datasets recognise the same companies, products, or locations.
Common data-matching challenges
02
Companies appear more than once
Organisations are often registered in different ways. Abbreviations, typing errors, or variations in spelling cause the same organisation to appear multiple times.
03
Datasets do not align
Data from different systems often uses different structures, field names, or identifiers. As a result, data cannot be combined without additional processing.
04
Manual linking takes too much time
Many organisations check datasets manually before analysis can be performed. As datasets grow, this becomes increasingly time-consuming and prone to error.
Our approach
From separate datasets to one reliable information picture
Good data matching involves more than comparing two spreadsheets.
The challenge is to automatically recognise, connect, and enrich different data sources so that one consistent dataset is created.
Scrape IT combines intelligent matching techniques with validation and data enrichment to reliably connect companies, products, locations, and other entities.
The solution is used for applications including:
- product matching
- company matching
- customer matching
- location matching
- deduplication
- data enrichment
- master data.
Which data can be matched?
Product matching
Product information from different suppliers or data sources is connected automatically. This enables organisations to compare products objectively, regardless of differences in product names, item codes, or descriptions. Applications include:
- price comparisons
- competitor analysis
- assortment comparisons
- supplier comparisons.
Company matching
Companies are recognised automatically, even when names or details do not match exactly. This creates one complete company view across multiple data sources.
Location matching
Locations from different data sources are connected automatically. Examples include branches, points of sale, charging locations, and properties.
Deduplication
Duplicate records are recognised and merged automatically. This improves data quality and gives organisations one consistent source of information.
Direct insight
Dashboard
Automatically connect companies, products, and locations from different data sources and always work with one reliable dataset.
Practical example
Practical example: from separate datasets to one reliable customer or product view
Many organisations work with data from different sources every day.
Some of it comes from the CRM.
Another part comes from an ERP system.
External datasets are also used, or information is collected from public sources.
On paper, these datasets concern the same companies, products, or locations.
In practice, however, the data differs just enough to prevent it from being connected automatically.
Data matching changes this way of working.
Instead of:
Which dataset is correct?
the question becomes:
How do we combine all available information into one reliable dataset?
This gives employees one consistent dataset that analyses, dashboards, and business processes can always rely on.
The result
What does data matching deliver?
Automatically connecting datasets creates a more complete and reliable information picture.
Common applications include:
- product matching
- company matching
- location matching
- deduplication
- master data management
- data enrichment.
This enables organisations to achieve benefits including:
- higher data quality
- fewer duplicate records
- more reliable analyses
- more efficient business processes
- less manual verification.
Which organisations is it for?
Pricing & category management
Compare products from different suppliers or competitors using reliable product matching.
Sales & CRM
Prevent duplicate customer records and work with one complete customer view.
Procurement
Connect supplier data from different systems into one reliable dataset.
Business Intelligence
Work with consistent datasets for reports, analyses, and data models.
IT & data management
Improve the quality of business data and create one central source of truth.
From data to intelligence
From separate datasets to Data Intelligence
Organisations often already have all the information needed to make better decisions.
The challenge is that this information is distributed across different systems and data sources.
Automatically connecting companies, products, locations, and other entities creates one reliable information picture that analyses, dashboards, and business processes can trust.
This makes data matching more than a technical step within a data process.
It forms the foundation for data intelligence, enabling organisations to work with consistent, complete, and reliable data that can be used directly for analysis, automation, and decision-making.
Frequently asked questions
What is data matching?
Data matching is the automatic recognition and connection of the same companies, products, locations, or other entities across different data sources. This creates one consistent dataset that organisations can use for more reliable analysis and automation.
Why is data matching important?
Data from different systems often does not align one-to-one. Automatically connecting datasets creates a more complete information picture, improves data quality, and makes analyses more reliable.
Which data can be matched?
Depending on your application, data including products, companies, customers, suppliers, locations, addresses, objects, and other unique entities can be connected.
How does data matching work?
Data matching compares different characteristics such as names, product codes, addresses, or other identifying data. Based on these characteristics, records are automatically connected into one consistent dataset.
Is data matching only suitable for large datasets?
No. Data matching is valuable for both small and large datasets. Its added value generally increases as the number of data sources grows.
Can data matching be combined with external data sources?
Yes. Many organisations combine internal data with public data sources or market information to build a more complete and reliable information picture.
Which organisations is data matching suitable for?
This solution is used by organisations including retailers, wholesalers, manufacturers, e-commerce companies, financial service providers, and organisations with multiple data sources.
How is the matched data delivered?
Matched data can be delivered in almost any required format or integrated directly with existing systems. Options include APIs, dashboards, CSV, Excel, JSON, XML or Parquet, Power BI, MCP for direct integration with AI applications, EDI, (S)FTP, email alerts, and direct integration with proprietary systems. In short, almost any preferred form of data delivery or integration is possible.
Can data matching be combined with other Scrape IT solutions?
Yes. Many organisations combine data matching with MCP AI data integration for AI applications, dashboarding for real-time analysis, and intelligence solutions such as price monitoring and market intelligence and competitor analysis.
What is the difference between data matching and CRM enrichment?
Data matching focuses on recognising and connecting data from different sources to create one consistent dataset. CRM enrichment focuses on supplementing and updating customer and prospect data within a CRM system. Many organisations combine both solutions to improve both data quality and the completeness of their CRM.
Reliable data, ready to use
Would you like to get more value from your business data?
Discuss which internal and external data sources you want to connect and how current information should become available to dashboards, AI, and business processes.