Remove Duplicates from Renovation Project and Contractor Data
✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Renovation platforms, permit databases, and home improvement marketplaces track projects, contractors, and material suppliers across systems that rarely share identifiers. The same renovation project appears when both the homeowner and the contractor submit records. A contractor who works under a business name and a personal name creates duplicate provider entries. Building permit databases accumulate duplicates when the same project receives multiple permits for different phases (electrical, plumbing, structural). dataclean.to matches renovation records by property address, contractor license number, and project details to produce clean, non-redundant datasets.
The Problem
Duplicate records in renovation data create confusion for homeowners, contractors, and platform operators. A homeowner searching for a contractor sees the same business listed twice with different review counts, making it unclear which profile is authoritative. Project management platforms with duplicate project entries show conflicting timelines and budgets for the same renovation. Permit databases with duplicates overcount construction activity in a jurisdiction, skewing housing market analyses. Insurance companies tracking renovation claims against properties with duplicate records may miss that multiple claims refer to the same project, potentially overpaying or flagging the wrong claim. ICC building permit and code compliance resources
How to Fix It
1
Export project and contractor records
Pull data from your renovation platform, permit database, or contractor directory into CSV format. Include property address, project type, contractor name, contractor license number, permit number, project start date, and estimated cost.
2
Upload to dataclean.to
Upload the CSV. The tool compares property addresses, contractor names, and license numbers to find projects entered multiple times and contractors listed under different business identities.
3
Review duplicate clusters
Examine flagged groups. Typical duplicates include the same renovation project entered by both homeowner and contractor, different permit phases for the same project treated as separate projects, and contractors listed under both a DBA and legal business name.
4
Merge into consolidated records
Consolidate confirmed duplicate projects into single entries with the combined permit history, accurate timeline, and correct total budget. Merge contractor duplicates into one profile with the verified license number and all business name variations.
5
Export the clean renovation database
Download the deduplicated data for import into your platform or permit system. Clean records give homeowners an accurate view of contractor profiles and ensure project tracking reflects reality.
Frequently Asked Questions
How does the tool handle multiple permits for the same renovation?
Different permit types (electrical, plumbing, structural) for the same address and date range are flagged as related entries. You can merge them into a single project record with all permit numbers listed, showing the full scope of work at that property.
Can it match contractors who operate under different business names?
When two contractor entries share the same license number but different business names, they are flagged as duplicates. This catches contractors who work under a DBA name and a legal entity name, or who rebranded without updating all platforms.
What about recurring renovation work at the same property?
Multiple renovation projects at the same address in different years are not duplicates. The tool checks project dates and types in addition to address, so a kitchen renovation in 2024 and a bathroom renovation in 2025 at the same property are kept as separate projects.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.