Six steps for comprehensive and robust data cleansing

Register now

Data is the foundational building block for any marketing, advertising, or branding strategy. Whether it be contact information, the amount of time someone spends on a landing page, past buying preferences, or a list of social media interactions, tasks like marketing automation and tools like CRM can only operate with the usage of data . However, the quality is just as important as the quantity.

Any marketer or data analyst can attest to the challenge of keeping data clean and useable. Accounting for a mistyped word or name, emails that no longer work, and information that comes from a third-party can be a time-consuming and mistake-wrought process. However, while nothing is perfect, it is possible for marketers and data analysts to create a data cleanse process that ensures the accuracy of insights and strategies. Here are six steps in how to conduct a thorough data cleansing process.

Develop Data Entry Best Practices

Before you start the cleanse process or even put a plan in place to handle the review of data, you should develop best practices for those entering any data into CRM systems. An example of this would be to require anyone with access to the CRM database to adhere to an early error detection process where all newly entered information is rechecked within a few days of entry.

Another part of this is to define the types of errors that can occur and their likely impact on business processes. A keystroke, field, or application interpretation error can bring various kinds of problems in their own ways. So, you should ensure your team is watching out for these issues and has set a designated time to recheck the information.

Set Accuracy Goals

It would help if you encouraged your team always to strive to produce accurate data. While it does help to describe to them why it is crucial to adhere to data entry best practices or ensure that the information they are receiving from a third-party is correct, there should also be some motivation involved as well.

Work with your team to set accuracy goals, and begin to track their progress. At the end of the week when preliminary data entry checks are made, how much of the information is accurate and how much of it has to be corrected? When you have set goals for accuracy, you can see how your team is fairing and see if they are improving. Doing this also allows you to recognize problem areas in the overall data entry process and develop a plan to address them.

Identify High Priority Data

When beginning the cleanse, you should start with the data that matters most to your company and what you do. Are job titles significant, or are last names what you need for your email marketing strategy? Know exactly what you need to complement your marketing automation efforts. Starting with the most critical data first is wise as if you have to steer away from this project for any reason, you know that the most vital data has already been taken care of so you can still conduct business as usual until you have to come back and address everything else.

Standardize Your Data

Make sure that all the data entered is the same. For addresses, do you spell out “Road,” or use the abbreviation “Rd.” Do postal codes and state codes agree? Are you using titles for everyone or just some contacts in your CRM? Ensure that all the data used in the CRM matches as far as their form. You might even want to see if you can work with your IT department to see if some of this information can be pre-populated into the CRM to ensure greater accuracy.

For instance, if a college or university is entered into the database, can the corresponding state automatically populate in the database? Also, can a postal code automatically match with a state? Ensuring that all your information is standardized will go a long way in helping to solidify accuracy.

Check for Mistakes

This step is where you begin to see what is missing and the information that is incorrect. You can also see if specific information that has been missing —because it was initially not available—is now able to be located. The results of data gaps will likely guide your efforts. For instance, are many emails bouncing when you send your campaigns? Are social media tags incorrect or outdated? This step is the time when you work with your team to check each record for mistakes like these. You should have a reliable source to review the data against (this could be different depending on the platform). It also makes sense to have a best practice resource or guidelines for correcting data to which your team can refer.

Create a Plan for Acquiring Missing Data

For information like phone numbers, last names, emails, or other essential personal data, it is helpful to create a plan to locate this information. It might involve making calls, reaching out to any third-parties who sent information your way, or doing some internet research. This is a time-consuming and meticulous process that is crucial to ensuring you have the right data to not only contact those in the database but also to guide them along the sales funnel. Again, there should be guidelines and best practices here for you and your team to find the best sources to acquire any missing data.

Final Thoughts

Each data cleanse process should end with a plan for automating any repetitive tasks for easy-to-acquire information, and a strategy for future monitoring. The goal is to have a routine procedure of checking data for accuracy along with best practices for finding and using quality data. Your marketing efforts and strategy are only as good as the information you have to work from. This reason is why it is essential to make data cleansing a regular part of your data analysis process, as well as instituting quality data entry best practices from the start. If you make quality data cleansing a part of your regular marketing strategies, issues like bounced emails and missed opportunities will be much less likely. This will make your marketing automation and strategy efforts all the more successful.

For reprint and licensing requests for this article, click here.