You may be asking yourself how to clean data? First you have to understand where bad data comes from. Bad data gets added to CRM by sales reps who do not have time to check for duplicates, input complete information or verify that data is not outdated. Marketers are dealing with multiple sources of data input and by not having proper data governance techniques ends up creating duplicates, not standardizing data prior to uploading and creating new forms without complete data. Your first step in cleaning data is to stop it from happening in the first place. Once you have identified and fixed the original problem it is now time to clean data.
Step 1: Identify Duplicates
Once you identify all the duplicates in your system, you should copy any data that you need to the surviving record. Your next step is to delete the duplicate records from your system. It is also a good idea, to back up data in case you needed the duplicate and have a record to go back to should you need to.
Step 2: Find any bogus records
There are many ways bogus records make it into your system. They could be fake form submits or even spam. As well as test records created by operations teams when they are developing their CRMs and Marketing Automation Tools. The bogus records can be identify by looking for emails with only one character before the domain name, any ‘test’ emails and company names, company name and address of your corporate organization and by finding consecutive characters for example: email@example.com, firstname.lastname@example.org, email@example.com, etc…
Step 3: Validating and Verifying Data
You may want to validate and verify data such as email address, address, phone #, revenue information or company’s website. Since data validation and verification is expensive it is only advised to do so if it is interfering with business’s day to day or if you are planning to run an expensive direct mail marketing campaign or have high bounce rate.
Step 4: Standardizing Data
You may want to normalize/standardize fields that you may need for segmentation, analysis and territory planning. Those fields can include country, state/province, standardizing phone numbers, titles, industry and other custom fields. An example of standardizing is Industries: Financial services, investment banking, investor bank, would all become “Financial Services”.
Step 5: Other Cleaning
Depending on your data, you may choose to do other data cleaning initiatives such as: data appending for missing records, excluding any unsubscribed and bounced records from your system in case you have a number of rows limitation in your system, establish parent/child relationships or you may choose to update certain fields with custom information as you are making changes.
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