Not everyone knows that data can be dirty. Dirty data poses a myriad of problems to businesses all over the world, so they want to know how to clean the data up. As time goes on, data gets dirtier, making the cleaning process more and more challenging. But what is data cleansing, and why does it matter? Read on to find out.
So, what do we mean when we talk about “dirty data?” Dirty data is not information about waste management. Instead, this phenomenon refers to data that’s incomplete, duplicated, or inaccurate. Dirty data doesn’t just spring into existence, though—it comes from somewhere. Typically, dirty data originates due to poor communication, user error, or even a bad data strategy.
Whether you know you have dirty data or you just want to play it safe (which is never a bad idea), data cleansing is your go-to solution. Data cleansing is a process that works to filter out the dirty data and clean it up. Essentially, the cleansing process removes or resolves every instance of incomplete, duplicated, or inaccurate data.
All the data in the world does no good if you’re not working with quality data. Dirty data can waste your time and even cost you serious money. When you work with high-quality data, you don’t need to worry about throwing money at a problem that doesn’t exist except within spreadsheets. That’s what low-quality data can do—tell lies through statistics.
Importance of Data Cleansing
Cleansing your data catalog is important because dirty data is a recipe for misinformation. You may not know the truth about your organization’s processes without a little data cleansing to help you! With master data management, you can clean your data easily and efficiently.
Now that you know what data cleansing is and why it matters, make sure you give your data a good cleaning before trying to use it. Otherwise, you may end up with information that isn’t helpful at all.