Data cleansing refers to the process where a company reviews, corrects and updates its data to ensure it is accurate, consistent and usable. This often involves removing duplicates, fixing errors, standardising formats and enriching data with missing information.
Purpose
The goal of data cleansing is to improve data quality so the business can make better decisions, improve customer engagement and reduce costs linked to incorrect information. Poor data can, for example, lead to wasted marketing, reporting errors or lost potential customers.
Examples of data cleansing
- Removing duplicates in a customer database
- Standardising addresses, phone numbers and emails
- Correcting spelling mistakes in names or cities
- Updating outdated information, e.g. contacts or CVR numbers
- Adding missing data from external sources
Use
Data cleansing is used in many contexts, especially within CRM systems, marketing, financial management and reporting. For companies with large volumes of customer data, it’s a key discipline to ensure campaigns reach the right people and sales teams work from valid information.
Methods
There are both manual and automated methods for data cleansing. Smaller businesses can often handle the process in spreadsheets or via simple scripts, while larger businesses use dedicated tools and integrations that can validate data against public registers (e.g. CVR in Denmark).