Data Quality Checklist

March 20, 2015 RingLead Inc

Joseph Morgan, President and CEO of Standard Register, once said, “If you have the right data at the right time, you can absolutely accelerate your business.” Real time, clean, constantly-changing data will get you ahead of the curve so your business can focus on success.

However, clean customer data is hard to find and maintain, and it call comes down to data quality. Stemming from RingLead’s new ebook, the Complete Guide to Data Quality, here are the steps to ensure total data quality, in a quick and easy cheat sheet. Marking the boxes on this data quality checklist will help you cover all of the aspects of bad data, from missing data, to invalid data, to duplicates.

Print this data quality checklist, post it on your wall, and start marking off those boxes.



Analyzing Data Quality Checklist

Analyzing is about understanding the overall state of your data. In other words, how dirty is your database? Analyzing the state of your database requires you to ask yourself the following questions: How bad is our duplicate situation? Where are the duplicates coming from? Here are the key steps to analyzing your data.

  • Find the duplicate data
  • Find the missing data
  • Find the unstructured data


Without performing a comprehensive data cleanse, you’re off to a poor start. For example, if a record is imported from a trade show list, it is created manually via a sales user, and created again via a web form submission. Now this user exists in the CRM three times. Cleaning your data ensures a standardized approach to your data entry process. It supports unique, duplicate-free records.

Clean your data at the duplicate and accuracy levels with these steps:

Duplicate Data

Inaccurate Data

  • Validate contact data (email, phone, mailing address, etc.)
  • Research and update invalid data (wrong phone numbers, emails, etc.) to make it accurate and fresh


Now that you’ve removed the duplicates, populated the missing data, and standardized your database, you must make sure these issues don’t return. Without a protection strategy, your data will continually decay. Not only do phone numbers, emails and titles change, but as your employees are entering data into your CRM, they are creating duplicates and entering data inconsistently.

Protect your database from duplicates, unstandardized data, missing data, and invalid data.

Duplicate Data Protection

Data Standardization

  • Create a schema/naming convention for your database to ensure all data follows the same formatting and structure, i.e. “Director of Marketing” vs “Dir. Marketing” and “N.Y.” vs “New York”

Continue to Complete Missing Data

Data Validation


Without enhancing your existing data, you limit your data potential. Additional information on the record will help to complete the contact’s information, giving you a 360-degree view of the contact. It ensures the data is accurate by verifying the email addresses and saving your salespeople time from sending bad emails. When sales teams, customer success teams and other employees do not have access to a complete record, they waste time looking in external systems and on the internet in search of that contact information.

Here’s how to achieve that sense of completeness.


How many items did you mark off your data quality checklist? Keep coming back to this page (or print it out) to get all the boxes checked. Good luck in your data quality journey.

Get on the path to total data quality with this comprehensive ebook below.

The post Data Quality Checklist appeared first on RingLead.

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