By Megan Golden
Before diving into a customer relationship management (CRM) platform integration project, one step to take is the always important but somewhat dreaded task of data cleaning. Although many times this to-do is skipped, ignored, or even rushed—and therefore done poorly—it’s a must-do if you want your integration project to go through without a hitch (or if you simply want to keep your CRM data clean for better use in general). The good news is that data cleanup is never as bad as it seems, and the benefits far outweigh the temptation to move the integration project or any data analysis task along a little faster by skipping this crucial step.
What Is Data Cleaning?
Quite simply, the goal of data cleaning is to remove data that is incorrect in an effort to prepare data for analysis or an integration. This could mean removing incomplete data, data that is not formatted correctly, or data that is duplicated.
Generally, you should be looking to remove data that is not going to help your business make a decision or data that is incorrect, and therefore would provide inaccurate results or worse—skew decision-making the wrong way.
Imagine if your bad data was the reason your organization made a specific decision. Data cleaning helps ensure decisions are made with the best possible information. Ultimately, clean data allows you or any of your integration tools to interact with your data and find the best information accurately and efficiently.
5 Things to Remember When Data Cleaning
1. Fix Formatting
Aside from removing bad data, you should also fix spelling errors, naming conventions, and formatting issues so all your data follows the same setup. This is extremely important when dealing with numerical data and using the same unit of measure. Standardizing your data sets will allow you to easily compare and contrast results. Additionally, consistent formatting ensures any sort of automation you have set up follows the same structure.
2. Remove Duplicate Information
When compiling a lot of information or maintaining a large CRM, it’s common to run into duplication issues. Since it’s likely unavoidable, regularly analyzing and de-duplicating your data is crucial to avoid skewed data. Many CRMs offer de-duplication tools to analyze data that can help you clean up your data faster.
3. Deal with Missing Fields
Missing data can lead to inaccurate results. Depending on the fields missing, you may want to remove data from your analysis if the values are missing, or you can input missing values based on averages or data you do have available. Either way, you do risk skewed data, and if you’re running into missing values often, it might be worth reviewing your data-gathering process to make sure those values are included in the future.
4. Remove Unwanted Information
When it comes to data, occasionally less is more. If you have data on contacts that don’t matter, automatically came with your CRM, and so on, it might be time you remove it from your database—especially if it could skew results or confuse the data analyst.
5. Validate Your Data Regularly
Data cleaning isn’t meant to be a one-and-done project. Cleaning your data might be necessary on a quarterly or even monthly basis in order to ensure you’re getting the best information possible from the data you have. If you have an integration set up between two systems, you may also need to incorporate tools on both ends that do regular data hygiene (for example, name and date formatting).
3 Benefits of Data Cleaning
Clean data allows your organization to review high-quality and accurate information, which can better guide the decision-making process in all channels of business. Three specific benefits of cleaning your data include the following:
- Clean data allows you to map different functions across integration tools accurately and more efficiently.
- Regular data cleaning can lead to quicker decision-making, because data is ready for analysis as soon as it’s needed.
- Consistently cleaning and monitoring your data will allow you to see when an issue occurs with your data gathering, and therefore you can make a fix faster to ensure data is accurate and being collected correctly.
Remember, the cleaner your data is, the better your integration project will go, or the easier it will be for your sales and marketing team to find what they need in your CRM to make better business decisions. The bottom line is that messy data is just as bad as no data at all, so clean it up.