address data

WinPure just debuted a new data cleansing feature with its recent update. In addition to its data matching and cleaning capabilities, you can now take advantage of global address parsing.

What does it do? This new feature scans address entries, looks for any local conventions, abbreviations and context, and intelligently breaks these data chunks into nice, clean and standardized address entries, and it works for all countries!

The new Address Parser uses innovative parsing technology, based on computational linguistics, natural language processing, parsing technology, semantic techology and with its built-in country detector will automatically detect the country of each address.

address parsing

This time-saving feature takes the guesswork out of address-related data, helping you keep your address book current, accurate, and ready for business.

This new feature splits global addresses into the following component parts.

  • house: venue name e.g. “Brooklyn Academy of Music”, and building names e.g. “Empire State Building”
  • category: for category queries like “restaurants”, etc.
  • near: phrases like “in”, “near”, etc. used after a category phrase to help with parsing queries like “restaurants in Brooklyn”
  • house_number: usually refers to the external (street-facing) building number. In some countries this may be a compount, hyphenated number which also includes an apartment number, or a block number (a la Japan), but libpostal will just call it the house_number for simplicity.
  • road: street name(s)
  • unit: an apartment, unit, office, lot, or other secondary unit designator
  • po_box: post office box: typically found in non-physical (mail-only) addresses
  • postcode: postal codes used for mail sorting
  • suburb: usually an unofficial neighborhood name like “Harlem”, “South Bronx”, or “Crown Heights”
  • city_district: these are usually boroughs or districts within a city that serve some official purpose e.g. “Brooklyn” or “Hackney” or “Bratislava IV”
  • city: any human settlement including cities, towns, villages, hamlets, localities, etc.
  • state_district: usually a second-level administrative division or county.
  • state: a first-level administrative division. Scotland, Northern Ireland, Wales, and England in the UK are mapped to “state” as well (convention used in OSM, GeoPlanet, etc.)
  • country: sovereign nations and their dependent territories, anything with an ISO-3166 code.

What is address parsing, and why does it matter? 

Here’s everything you need to know.

What is address parsing?

Address parsing involves breaking down a raw address string into its individual components, such as street name, city, state/province, postal code, and country. 

Let’s illustrate this process with a simple example:

Consider the following raw address string:

1600 Amphitheatre Parkway Street, Mountain View, California 94043, United States of America

The above example consists of these components:

–> Street Address: The first step in address parsing is to identify the street address. In this example, the street address is “1600 Amphitheatre Parkway Street”.

–> City: Next, we need to determine the city associated with the address. In this case, the city is “Mountain View”.

–> State/Province: After identifying the city, we look for the state or province. In this example, California.

–> Postal Code: The postal code provides further specificity to the address. Here, the postal code is “94043”.

–> Country: Lastly, we determine the country associated with the address. In this case, “United States of America” indicates that the address is in the USA.

An address parsing process (be it by a no-code data cleaning tool or a data analyst), scans through the raw address entries to accurately extract and classify these components. Once parsed, the address data can be standardized, validated, and utilized for various applications, such as geocoding, data matching, and address verification.

address parsing winpure

Parsing is just the first step towards getting runaway address data under control. 

After parsing an address into its components, the next crucial step is verification. Address verification involves validating the parsed address against reference data to ensure its accuracy and correctness. Now that the above address is parsed and standardized, the next step would be to verify it against an official government database to ensure the validity of the address. 

before and after address cleaning

Just like that, your poor address data is now clean, transformed, and ready to be used for business purposes such as direct mailing campaigns and targeted personalizations – which leads us to talk about the benefits of address parsing.

Now you could use traditional methods like fixing poor data on an Excel sheet or you could even outsource the cleaning to third-party consultants, however, these solutions are time-consuming and offer limited control. With dedicated data quality software though, you get full in-house control, while saving on time and effort.

table address parser customizations

Benefits of address parsing and verification

The need for an address parsing feature in a data cleaning/matching tool becomes glaringly evident when dealing with a database that contains a mixture of international and local addresses. 

Here are some data challenges that can be effectively resolved:

Easy Standardization of Address Data

International addresses come in a myriad of formats, varying greatly based on country-specific conventions and languages. Address parsing can standardize these diverse formats into a unified structure, ensuring consistency and facilitating seamless data integration and analysis.

Effective Data Quality Implementation 

Inaccurate or poorly formatted addresses can lead to undelivered shipments, misrouted communications, and erroneous customer records. Address parsing helps validate and cleanse address data, reducing errors and enhancing overall data quality.

Precise Geocoding and Mapping

Precise geolocation is crucial for businesses operating across regions. Address parsing enables accurate geocoding by extracting essential components such as street names, cities, and postal codes, thereby facilitating mapping applications and location-based services.

Meet Compliance and Regulatory Requirements

Different countries impose unique address formatting and validation standards. By parsing addresses according to these regulations, organizations can ensure compliance with local laws and regulations, mitigating the risk of penalties and legal repercussions.

Efficient Data Matching and Linkage

Matching records across databases or integrating data from disparate sources relies heavily on accurate address information. Address parsing enhances the efficacy of data matching algorithms by breaking down addresses into their constituent elements, enabling more precise comparisons and linkage.

Enhanced User Experience

For customer-facing applications like e-commerce platforms or service portals, a smooth and intuitive address entry process is essential. Address parsing can streamline address input by automatically detecting and correcting errors, reducing friction in user interactions and improving overall satisfaction.

Cost Savings

Inefficient handling of address data can incur significant costs, whether through wasted resources on undeliverable mail or lost opportunities due to inaccurate customer targeting. Address parsing minimizes these expenses by optimizing address validation, geocoding, and data cleansing processes.

To Conclude – Your Business Needs Address Parsing Capabilities

The traditional approaches to getting address data under control can be time-intensive and frustrating to deal with manually. The global address parsing feature that’s ships with WinPure helps to get runaway address entries neat, clean and most importantly verified so that you concentrate on getting the best business outcomes with your data. 

No matter which country you and your customers belong to, parsing and normalizing street addresses doesn’t get easier than this. Save your data analysts a ton of time instead of combing through unnecessary data-prepping tasks.

Try out our free trial today.

Written by Samir Yawar

Samir writes about data quality challenges faced by businesses and how it impacts their day-to-day operations. His end goal - help businesses make sense of their data with WinPure's no-code platform.

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