Data decays by 20% per year. Correct, organise, and format your customer addresses with Address Cleansing
What is Dirty Address Data?
Dirty data refers to inaccurate, incomplete, duplicated or inconsistent information in your database. For businesses that rely on customer information—particularly names and addresses—clean, reliable data is essential. Poor-quality data can damage operations, increase costs and lead to poor customer experiences.
For example, an incorrect postcode can delay a parcel. A missing flat number might result in a failed delivery. An outdated address could mean sending communications to the wrong place. Over time, these issues compound and can erode customer trust, inflate operational costs, and create inefficiencies across teams.
Address data is especially vulnerable to becoming "dirty" over time due to manual entry errors, outdated records or integration issues. This makes regular validation and cleansing vital for maintaining accuracy.
Common Types of Dirty Data
Understanding the nature of dirty data can help pinpoint the right solutions. Here are the most common categories:
Incorrect data
This includes any address that contains wrong or invalid information. Mistakes might be introduced by a customer entering their information online or by a team member updating your CRM. If you’re an ecommerce business delivering to customers, storing the wrong addresses will lead to failed deliveries and unhappy customers.
Inconsistent data
Inconsistencies occur when the same address is represented differently across your systems or databases. This often happens when businesses store information in separate platforms—such as CRM systems, order management software or customer service tools—without standardising the format. Data redundancy can lead to inconsistent operations and will cost you time and resources to manually check multiple databases for information.
Incomplete data
Sometimes, essential address components are missing altogether. This might include missing building names or numbers, omitted postcodes, or partially filled-out address fields.
Incomplete data is often the result of poorly designed forms or rushed user input. Without full and valid address information, delivery services may be unable to fulfil orders, and your internal teams may lack the details needed for verification or reporting.
Duplicate data
Duplicate address records can be introduced in several ways, including multiple form submissions by the same customer, data imports from different sources without proper deduplication, or errors during manual data entry.
Duplicates lead to inefficiencies, such as sending multiple communications to the same customer or allocating resources to service the same address more than once. Over time, this clutters your database and skews reporting.
What Causes Dirty Data?
Dirty address data doesn’t happen by accident. It’s usually the result of preventable issues at different stages of your data collection and processing workflow. It can create confusion, slow down efficiency, accumulate additional costs and lead to missed opportunities. Here are the most common causes:
Data Entry Errors
Human error remains one of the most frequent causes of dirty data. Even a single typo or misplaced character can cause a record to become invalid.
For example:
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Swapping two letters in a street name
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Entering the wrong house number
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Misselecting a postcode from a dropdown list
Poor Form Design
Forms are often the first point of data capture, and their structure directly impacts data quality. A poorly designed form can confuse users, leading to incomplete or inaccurate entries.
Missing or Unavailable Data
Sometimes, users simply don’t have the correct details to hand, or they may choose to enter placeholder data just to complete a form. For example:
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A user might enter “123 Fake Street” if they’re unsure of their full address
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Temporary staff might leave fields blank when updating records under pressure
This can result in address data that looks plausible but is not functional or traceable.
System Integration Issues
Businesses often rely on a range of systems to manage operations such as CRMs, eCommerce platforms, fulfilment software and third-party APIs. If these systems are not well-integrated or do not share data accurately, inconsistencies and errors can easily arise.
This can lead to:
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Records being overwritten with outdated information
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Conflicting versions of the same address in different platforms
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Synchronisation delays that result in outdated data being used
How Address Cleansing Helps
Address Cleansingis the process of reviewing, correcting and formatting address records to ensure they are accurate, complete and consistent. It helps identify errors, fill in missing details, and bring all records in line with recognised postal standards.
At Ideal Postcodes, our Address Cleansing solution uses Royal Mail’s Postcode Address File (PAF®)—the UK’s most authoritative source of address data. By cleansing your address records, you can:
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Correct invalid or incomplete addresses in bulk
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Standardise records across systems and departments
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Minimise failed deliveries and improve customer experience
Whether you're updating legacy records or onboarding new data, address cleansing is a practical and cost-effective way to improve operational efficiency and data quality. Get in touch to find out how.