3 Types of Dirty Data to Beware
The word “pollution” is often associated with the environment. Plastic bags, straws in the ocean—you get the drift. However, what many people don’t know is that pollution also exists in the business world. Yes, you heard that right. Since many businesses and firms are data-driven organizations, there’s a chance that data may become corrupted or obsolete. When data becomes insufficient, outdated, or inaccurate, it typically referred to as “dirty data”.
Dirty data isn’t something you can just ignore. For many businesses, dirty data can lead to interruptions during regular business operations and pose countless other risks. As marketers, it’s important to identify the difference in dirty data that we may encounter at some point. Here are 3 types of dirty data that you need to watch out for:
When you’ve been a marketer for some time, there’s a chance you’ll come face-to-face with data that is dated several years old. This type of data is useless, especially since most information has likely evolved over a significant period of time. Most causes of outdated data involve a past rebrand of an organization, former workers leaving their positions, and updates on software. The digital world is rapidly evolving so you shouldn’t stop for a flimsy piece of old data. Be sure to install software to help you purge old files from your device’s system.
There’s nothing more irritating than finding a piece of data that is not located in the right place. Of course, it’s common; marketers make mistakes too! The problem with incorrect data is that it can lead to confusion or stop business operations from moving forward. It can also cause inaccuracies that lead to further issues later on. It’s highly important to ensure that all data goes into their correct location, whether it’s a specific folder or a device.
If a piece of data lacks key processing information or fields, it is commonly referred to as “incomplete data.” Incomplete data can prevent you from gaining competitive insights. For instance, segmentation and other data processes rely on these missing fields or information in order to operate. Without these fields, you can miss out on opportunities for campaigns and other marketing strategies. You can combat this by using specialized software that optimizes your data by filling in the missing fields.
Dirty data poses a big problem to a business’s success and growth. As such, it is incredibly important to take the next initiative and perform sufficient data cleaning so that you can make the most of your data. Remember that your data plays a big role in your campaigns and the overall operations of a business. By identifying and eliminating dirty data types, you can ensure you or your client’s business runs smoothly without any interruptions to its flow.