What is Address Standardization & How to Do It?

Address data consists of semi-structured phrases and numbers. It’s easy to look for errors and fix them with structured data because you can use exact match algorithms to detect duplicates or Excel functions to identify common errors. But address data are usually long strings of text that are entered manually by customers themselves. Very few companies have strong form validation rules that can break the address data into formattable fields. Most simply have an address box that requires users to type in data manually. This data becomes extremely difficult to parse, clean, and standardize. 

Data quality specialists or IT managers have to manually parse components of address data to standardize them. 

For example, here’s how an address data looks like:

When customers are filling in a form manually, they are highly likely to miss out locality or even town/city name. Some may not even remember their postal codes or may get it entirely wrong! Therefore, before you begin to standardize this data, you would need to develop an address data-cleaning strategy that involves parsing the data into components while verifying the accuracy of post codes. 

Your address data standardization strategy depends on the complexity and the state of your data quality. As a starting point, there are some best practices that you can borrow from to create a custom address standardization strategy. 

Some key steps include: 

Identify the data sources: Determine the various sources of address data and identify the key fields that need to be standardized, such as street name, house number, postal code, city, and state. Never attempt to fix address data as a single field. You must always treat each field on its own, identifying and fixing errors one by one. 

Define the standard format: Determine the standard format for each address component which should be based on the postal authority of the country in question and should comply with industry standards. For example, if the official postal code format includes uppercase letters, then you must make sure all letters are in the same case. If there are spaces between codes, ensure it is maintained throughout. 

Use a data matching tool to cross-refer with government databases: You must always cross-refer to government address databases when standardizing data. Remember, standardization also involves eliminating duplicates, and so you’d need a powerful data matching engine that allows you to match within your own source to remove duplicates and to match against a government database to verify addresses. 

With WinPure for example, you can clean, deduplicate, standardize and validate your address data without requiring any code! See how in the video.

Use data cleansing tools: Utilize data cleansing tools such as data parsing and data matching to identify and correct errors in the address data. These tools can help to identify and correct inconsistencies in the way that data is entered, such as variations in abbreviations or misspellings.

Implement an automated process: Implement an automated process to standardize address data, such as utilizing regular expressions or scripting languages. This will ensure consistency and efficiency in the standardization process, especially for large volumes of data.

Continuously monitor and improve: Continuously monitor the quality of the standardized address data and identify areas for improvement. This could involve analyzing the results of data validation and enrichment processes, or monitoring data quality metrics, such as completeness and accuracy rates, to identify areas for improvement.

Your address data standardization strategy should not just be a standalone exercise, but should be an important part of your data cleaning and deduplication strategy. You don’t want to spend hours standardizing your address data only to find out you’ve got dozens of duplicates that need to be resolved. 

Address standardization involves a number of steps, including parsing, formatting, and validating address data. 

Parsing: The first step in address standardization is to parse the address data into its individual components. This typically involves breaking the address down into its constituent parts, such as street name, city, state, and zip code. 

Formatting: Once the address has been parsed, it is formatted according to a standardized format. This format may vary depending on the country or region in which the address is located. For example, addresses in the United States are typically formatted with a street address, city, state, and zip code, while addresses in the United Kingdom are typically formatted with a street address, city, postal code, county, and country.

Deduplication: When your address is parsed and formatted, you can easily use numbers like zipcodes, building numbers or house numbers to identify duplicates. It’s essential to treat duplicates so you don’t end up having to verify multiple versions of the same address. In some cases, you need human intervention to investigate duplicate entries. 

Validating: The final step in address standardization is to validate the address to ensure that it is correct and complete. This may involve comparing the address to a database of known addresses or using a third-party service to verify the address. 

Regular maintenance: Make sure to regularly update your address data with government databases at least once a year. The more frequent you keep your data clean, the better your business results. 

If you’re using WinPure, you could do all this by simply selecting rows and columns and letting it do all the work for you. If you’re manually implementing this process though, you’d need to script matching algorithms, maybe run multiple rounds of cleanup using Excel or other tools, only to end up missing hidden errors.

Address standardization is an important process for businesses that deal with a large volume of customer data. By ensuring that every address is correctly and consistently formatted, businesses can reduce the risk of errors in data entry, improve the accuracy of data analysis, and prepare their data for use in geographic information systems. With the help of address validation services, parsing software, and standardization software, businesses can streamline their address standardization process and ensure that their address data is accurate and up-to-date.

When businesses begin working on the quality of their address data, they aren’t sure what it is they need. There is often confusion over terms such as address standardization, address cleaning, address validation, and verification. All of these are distinctive processes, however, they are used together to provide accurate addresses. The WinPure solution, for example, allows for addresses to be cleansed, standardized, validated, and verified. 

Let’s use an example to portray this: 

Address: 123 Main St, Apt 2F, Anytown, NY 12345-6789

A detailed breakdown of the processes: 

Address Cleaning: Remove unnecessary characters or whitespace from the address. In the example, you would remove any extra spaces or punctuation to create a clean address string.

Address Standardization: Convert non-standard address components to standard formats. In the example, you would convert the “Apt” abbreviation to “APT” and capitalizing all letters to create a standardized address format.

Address Validation: Ensure address structure is valid and complete. In the example, you would check that the street name and number are valid, that the apartment number is correctly formatted, and that the postal code is valid for the state.

Address Verification: Confirm that the address actually exists and is deliverable. In the example, you would check the address against a database of valid addresses maintained by a postal service or third-party verification service to ensure that mail can be delivered to the address.

By going through these processes in order, we can ensure that the address is accurate, standardized, valid, and deliverable, reducing the risk of returned mail or delivery errors.

Whether you’re a small business or a multi-national corporation, address quality management is a necessary component of your data quality goals. The accuracy and consistency of address data can have a significant impact on business operations, customer satisfaction, and the bottom line.

As we’ve discussed in this guide, effective address quality management involves four key functions: address cleaning, address standardization, address validation, and address verification. 

Address cleaning helps to eliminate duplicates, typos, and other errors from address data, while address standardization ensures that address data is formatted consistently. Address validation verifies that an address exists and is deliverable, and address verification checks and corrects addresses in real-time.

Businesses that implement robust address quality management practices can improve efficiency, reduce unnecessary costs, and deliver customer satisfaction. Accurate and consistent address data helps to streamline operations, eliminate unnecessary costs associated with incorrect addresses, and ensure that customers receive their orders on time.

WinPure is a comprehensive data quality management tool that can help businesses with all four aspects of address quality management. With its powerful data cleaning engine, address standardization feature, address validation function, and address verification service, WinPure is a one-stop-shop for all of your address quality management needs. By using WinPure, businesses can ensure that their address data is accurate, consistent, and reliable, helping them to make informed decisions and stay ahead of the competition.