✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Bar and pub directories collect duplicates when the same establishment is listed under its legal name, its trading name, and its colloquial neighborhood name. A craft cocktail bar might appear as 'The Copper Still LLC', 'Copper Still Bar & Lounge', and simply 'Copper Still' across different data sources. dataclean.to matches bar listings by normalizing business names, comparing addresses and phone numbers, and clustering records that share overlapping attributes to produce a single definitive entry per venue.
The Problem
Duplicate bar listings distort market analysis, inflate directory counts, and confuse patrons searching for a specific venue. When a bar appears multiple times in a city guide or review aggregator, ratings and reviews are split across entries, giving an incomplete picture of customer sentiment. For bar owners managing their online presence, duplicates mean claiming and updating multiple listings on every platform. Beverage distributors working from dirty venue lists waste sales rep time visiting what they think are two separate accounts but is actually the same bar. Schema.org BarOrPub structured data type
How to Fix It
1
Collect bar listings from all sources
Gather venue data from your directory, POS system, review platforms, and distributor lists. Include business name, address, phone number, hours of operation, and any category tags like 'cocktail bar' or 'sports bar'.
2
Upload the combined dataset to dataclean.to
Upload the CSV. The tool strips common words like 'Bar', 'Pub', 'Lounge', 'LLC', and 'Inc' from names during comparison, then matches on address proximity and shared phone numbers.
3
Review duplicate venue clusters
Examine groups of listings flagged as the same bar. The tool shows name variations, address differences like suite numbers or floor levels, and any conflicting details such as different phone numbers or hours.
4
Merge into single venue records
Consolidate confirmed duplicates into one listing per bar. Keep the most complete set of attributes including the best description, all known phone numbers, and the most accurate operating hours.
5
Export the clean directory
Download the deduplicated CSV for use in your directory, mapping application, or distributor database. Each bar now has one authoritative entry with consistent information.
Frequently Asked Questions
How does the tool handle bars that share the same address as a restaurant?
If a bar and restaurant occupy the same address but have different names and phone numbers, they are treated as separate businesses. If they share both address and phone number, they are flagged as potential duplicates for you to review, since some venues operate as both.
What about bars with seasonal name changes or pop-up concepts?
Seasonal concepts that temporarily rebrand are flagged based on address and phone number matching. You can review these clusters and decide whether to merge them under the primary bar name or keep the seasonal listing as a separate entry.
Can it handle bar data from multiple cities or countries?
Yes. The tool processes all records together and uses full address comparison including city and postal code. Two bars with the same name in different cities are correctly identified as separate venues.
Example: Input → Output
name
cuisine
address
phone
hours
La Bella Italia
Italian
456 Oak Ave Chicago
(312) 555-0100
Mon-Sun 11am-10pm
la bella italia
italian
456 oak ave
chicago il
(312)555-0100
daily 11-22
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.