Increase Your Customers
Would you like more new customers? Do you want to increase your sales? If so your campaign can only be effective with the right data, and that’s where Data Bubble helps. We provide clean, GDPR compliant data that will help you to generate more leads and achieve more sales.
Quick Overview: What Is Data Cleaning?
Before we break down the steps of data cleaning, it’s important to first understand what it is.
Data cleaning is the process of combing through data, detecting corrupt, inaccurate, and incorrectly formatted records and either correcting or replacing them. So, for example, you may remove emails from your mailing list that are no longer active, or you might correct entries in your database that are incorrectly formatted.
Data cleaning is absolutely essential. If you don’t regularly clean your data, it won’t produce results for your business.
Now we’ve covered that, let’s have a look at how we actually clean data.
Data Cleaning Steps: How Do We Clean Data?
Here at Data Bubble, we’re experts when it comes to data cleaning. We follow a tried and tested process to ensure the data we offer is accurate.
Data Cleaning Steps #1: Audit
First and foremost, we must perform an audit to identify problem areas with our data. It’s important to take a good look at your data and determine which data entries need to be edited or removed altogether.
Auditing is a very important step that must not be missed. And as part of the audit process, you should determine some outcomes ahead of performing the rest of the process.
Data Cleaning Steps #2: Remove Duplicate and Irrelevant Data
Before correcting structural errors, you must remove duplicate and irrelevant data. It’s illogical to correct the formatting and structure of data entries that you’re going to remove afterwards, so that’s why this step comes first.
Once you have removed duplicate and irrelevant entries, the task of fixing structural errors won’t look so insurmountable anymore.
Data Cleaning Steps #3: Fix Structural Errors
Once you’ve removed duplicate and irrelevant data records, you can take the time to fix structural errors. Here’s what we mean by structural errors:
– Unique naming conventions that don’t match up with the rest of the data entries
– Obvious typing mistakes that need to be corrected, such as incorrectly spelt names
– Numerous categories that mean the same thing. For example, N/A and Not Applicable, which obviously have the same meaning.
Once you have fixed the structural errors, it should be easy to decipher your database and find data entries should you need to. Consistency is very important, to ensure that all entries are formatted identically at this stage.
Data Cleaning Steps #4: Assess Your Efforts
Once we’ve done all of the above, it’s time to assess our efforts and ensure all of the following is correct:
– The data makes sense
– The data is all formatted identically
– You only have data entries that you require
It’s important to assess your efforts at the end of data cleaning to make sure your efforts have the result that you’re wanting. And that’s the 4 steps, put simply. Data cleaning is a long, intensive process that can be very complicated, but we’re here to help you clean your data. Please contact us today if you would like to find out more about our data cleaning services.