4 Tips to Keep Data Clean

analysis-close-up-coffee-1179800+%281%29.jpg

If you feel like you’re struggling to keep your company data clean and updated, you aren’t alone. Bad data comes in many forms and includes incomplete, inaccurate, and empty fields. Ignoring dirty data can result in high costs and inefficiencies within a system. Common fields that house bad data include the wrong phone number, outdated physical address, wrong title or job function, no phone number or no email address, incorrect email address, and misspelled or incomplete company name.

To avoid letting bad data get out of control, consider the tips below and take proactive measures so your organization can keep data as clean as possible. 

Standardize Rules

To keep data clean, establish standardization rules that all users must follow when inputting data. Be sure to standardize internal systems and client facing ones to be consistent. Determine what fields should be standardized and how they should be formatted based on your intended use of the data. Common fields to standardized are monetary values, salutations, addresses, and titles. 

For example:

  • Monetary values  – 1,000.00 or 1000

  • Addresses   –  Ave. or Avenue

  • Titles  –  VP or Vice President

Add Constraints

Entering data can be daunting for users, especially if they have a lot to get through in a short amount of time. To ensure your data doesn’t have holes in it, consider adding constraints and required fields. For example, making fields like name, email, and company name mandatory helps keep robust contact information. Setting an acceptable format for data also helps minimize the risk of dirty data (i.e. making phone number xxx-xxx-xxx format and only accepting email fields with @xxxx.com).

Update Often

Think of data like a tomato on your counter, not the can of tomato sauce in your pantry. Just because your data is good when you enter it, doesn’t mean it will stay that way for long. People change roles within companies, leave for new jobs, and change phone numbers more often than we recognize. Every year 20% of all postal addresses change, 18% of telephone numbers change, and 25%-33% of email addresses become outdated. Now think about the email campaigns, cold calls, and lead generating efforts that are based on the data in your system and the resources that are spent in an effort to contact people with incorrect information.  

Automate Data Cleansing

It’s an inefficient use of time and resources to manually cleanse data. Data cleansing is crucial to avoiding dirty data but there are less laborious options you can choose. There are systems and algorithms you can invest in to clean, append, and dedupe data. These systems can screen through massive amounts of data to find human errors that result in inconsistencies. Cleansing systems can even detect duplicate records that often come about from companies only using one primary metric – like an email address – to discern contacts. In other words, work smarter not harder by implementing automated data cleansing tools.

Lauren Craton