Data cleansing is a business-critical part of data management. Over time businesses accumulate a lot of information about their customers and prospects. Information can become outdated quite quickly from the basics of contact names and address through to financial details and product portfolios.
Quite simply, data cleansing involves a review of all the data within a database to either remove or update information that is incomplete, incorrect, improperly formatted, duplicated or irrelevant. According to Forbes, about 27% of business leaders aren’t sure how much of their data is accurate, making data cleansing a worthwhile activity for most organisations.
The objectives of data cleansing is usually threefold: maintenance of information for existing customers to enable relevant communication, maintaining the information that supports business function like collecting payments and making deliveries and finally, data cleansing supports the compliance requirements of many industries including data protection legislation such as GDPR.
How to carry out data cleansing
The process of data cleansing includes a number of key stages.
1. Dealing with missing data
Plugging any missing values in a data set is an important element of quality data management. Missing postcodes could mean undelivered goods and missing forenames can lead to important communication being misdirected. Appending missing elements to create a complete dataset often relies on external parties.
2. Validating existing data
Reviewing existing data for consistency and accuracy will have far-reaching benefits from maintaining your communication channels to ensuring that your customers are able to pay and furthermore you will be able to fulfil any legal obligations. With Royal Mail reporting that they force address changes to over 40,000 UK addresses each year all organisations should be minded of the need to update the location details they hold for customers and prospects alike.
3. Removing duplicate data
The identification of duplicate records keeps your database ship-shape and accurate.
4. Handling structural errors
Redressing mistakes that arise from processing such as measurement and transfer of data illustrates the importance of data cleansing. Inconsistent punctuation, typos and mislabelled classes are the most common problems that need to be resolved.
How often should you carry out data cleansing?
The data cleansing process is usually done all at once and can take quite a while if information has been piling up for years. That’s why it’s important to regularly perform data cleansing.
How often organisations should cleanse depends on a variety of factors, not least the volume of data they hold. It’s also important not to cleanse too often or you may waste resources by performing unnecessary actions.
Why is data cleansing important?
Regular and structured data cleansing can have wide-reaching benefits across an organisation.
1. Avoid costly errors
Data cleansing is the best solution for avoiding costs that crop up when organisations are busy processing errors, correcting incorrect data or troubleshooting. For example, making sure deliveries are made to the correct address first time and therefore not requiring costly redeliveries.
2. Make data work across different channels
Data cleansing clears the path for the successful management of multichannel customer data. Accuracy across customer data including phone, postal and email channels allows your contact strategies to be successfully executed across channels.
3. Enhance customer acquisition
Organisations with well-maintained data are best placed to develop lists of prospects using accurate and updated data. As a result, they increase the efficiency of their acquisition and onboarding operations.
4. Ease the decision-making process
Nothing helps to support the straightforward decision-making process like clean data. Accurate data supports MI and other key analytics that in turn provide organisations with the insights they require to make well-informed decisions.
5. Increase productivity of internal teams
Data cleansing is also important because it improves data quality and therefore impacts on increased productivity. When incorrect data is removed or updated organisations are left with the highest quality information this means that their teams do not have to use time resources to wade through irrelevant and incorrect data.
Data cleansing for data quality
Quality data should be the glue that holds processes together to deliver a superior customer experience, gain a competitive advantage and move your business forward. Now, more than ever, in this time of flux and change the team at Loqate are here to help you build lasting customer relationships by correcting, suppressing or appending data.
How does your data measure up?
Take our free data health check and see how accurate your data is. You can decide after if you want to cleanse the data or not.
We'll compare your customer and prospect records against our own data sources to reveal how accurate, complete and compliant your data is.
We'll provide you with a visualised data quality audit, and a unique Data Quality Index Score, giving you insight into how you measure up against other companies who hold similar data.