The main goal of data cleaning is to remove or alter data that is incomplete, erroneous, or misformatted, thus making data processing faster and more accurate. To do that, data is “fixed” through correcting syntax errors and spelling mistakes, filing out empty fields, and removing duplicates, among other processes. These fixes make it easier for tools to find the right data for every query easier.
That said, data cleaning is a crucial part of data management. Here are some of the best ways to use data cleaning to your advantage:
1. Enter data correctly
Reputable data management companies in the UK and other countries believe in cleaning data as it is being entered into the database as an excellent practice. Standardize data at the point of entry and correct errors and inaccuracies before it enters the database. Doing this will increase the quality of your data and decrease the need to clean inaccuracies later on, which will improve efficiency by a tenfold.
2. Set quality standards
What are the quality standards for your data? What are the processes needed to meet these standards? How will you monitor the quality of your data? By setting the minimum standards of data quality and determining the steps needed to achieve these standards, you can easily maintain data hygiene while having a monitoring system in place to minimize errors and inaccuracies.
3. Focus on non-contacted data
Deprioritize newly contacted data segments for now. Instead, focus on data that has not been responded or contacted recently to catch any errors that you may have missed before.
4. Validate data accuracy
Use data cleaning tools that can ensure the quality of your data while giving you e-mail notifications as needed. There are many useful tools that you can use, albeit not all of them are free. Alternatively, you can also validate data accuracy through manual means if you have the resources–and bandwidth–to do so.
5. Do a CTPS check
Another excellent data cleaning practice is to check your B2B data with the CTPS register, which contains the contact numbers of companies that don’t want to receive sales calls. When you do this check, you’re no longer wasting time, money, or effort in calling companies that do not want to be called.
6. Look at failed deliveries
When an email is not getting through or a letter is returned to the sender, chances are that the contact has changed their information. Isolate these records and update them before putting them back into the database so resources are not wasted. You can do this via an email campaign or through telemarketing. But if you don’t have the resources for both, find tools that can help you validate data and make sure they are updated.
Data cleaning is a crucial step to improve the efficiency and accuracy of a database, which, in turn, provides the business with increased productivity and a better decision-making process. If you want to improve your data cleaning practices, following these tips is the best way to get started.
