If you are working with a customer relationship management (CRM) solution, you are probably household with data duplication and data cleaning. To work with the most accurate data possible, it needs to be reduplicated and free of errors. There are several best practices for ensuring clean and accurate data let is look at a few.
As you begin the data cleansing process, you should understand the current state of your customer database. How many data duplication issues to you see? How did these issues occur, and are there currently processes in place to prevent it from phenomenon again? Once you can answer these questions, it’s time to establish a regular safeguarding plan for your CRM system.
Setting a schedule for when you’ll be clearout out your CRM system can provide blocking measures needed to ensure data is accurate from the beginning. You don’t have to do a full-on data cleanse on a weekly or even monthly basis, but it should be at least every year. It might depend on the seasonality of your business.
Utilize the tools already at your throwing away through your customer relationship managing solution, specifically your software’s built-in data deduplication feature. This feature is already productively filtering through your data as you enter it, but it’s not going to find everything. There’s always a way for data to become accidentally duplicated or dirty. When this happens, you should rely on the automation tools within your CRM to manually scrub and reduplicate data within your database. This way, you’ll be in a run of your own data and can ensure its accuracy. In many cases, CRM solutions will also offer data reporting tools to show you the metrics on data health. Utilize these reports to save yourself time from yourself going through piles of data.
In some cases, it’s O.K. for your business to purge old data from its database. For others, it’s frowned upon. In those instances, consider utilizing tools within your CRM to archive data that’s irrelevant to your users. In a cluttered database of historical data, it can be challenging to find what you’re looking for, especially if you’re trying to search for something specific. Move data that’s not important out of the way so your users can find what they need faster and with fewer headaches.
In general, it’s important to be as careful as possible when it comes to data importing. While duplicate or dirty data may not seem like much on the surface, it can cause major troubles down the line.