Insights for the data driven leader.

Dirty Data: Myths Debunked

Dirty data—it’s a dirty word for some, but it doesn’t need to be. 

What does dirty data even mean? Dirty data includes data that is inaccurate, misleading or even a date that has been duplicated. It can cost your business time and money, but it may not be as bad as it sounds.

MYTH: Clean data exists

You aren’t alone; everyone has dirty data. 100% clean data generation is a myth on par with the existence of a perpetual motion machine or a non-grainy photo of Bigfoot. Dirty data isn’t ideal, but it doesn’t have to stand in the way of your business. What really matters is how you clean your data, not how dirty it is when you started.

Cleaning data is more important than clean data. There are different ways to actively clean data and one of the most important steps to cleaning data is validating your fields. Validating includes steps like finding and replacing words, removing duplicate rows, changing the case of the text, removing illegal characters and the list goes on.

One helpful tip to remember is that not all data will be used immediately, it could be utilized in the future. A lot of information may seem useless at the time but deleting this information can result in dirty, fragmented, or missing data in the future.

MYTH: Your data should always be pristine

Dirty data is not completely avoidable. Yes, you will have periods where your data is pretty dang pristine but human error will always be a contributing factor to dirtying things up. For example, data can become dirty through duplication. This can happen as a result of multiple submissions or just user error. You are constantly collecting and adding more data and as long as you are, there will be a chance for human error.

Us humans aren’t the only ones at fault though. Sometimes we are working as hard as possible and doing everything correctly on our end but that doesn’t mean the information we get for to input for our data will be complete. Data doesn’t always have the opportunity to be complete. If information gathered is incomplete the human submitting the data can enter what they have completely perfectly but we can’t input something we don’t have.

MYTH: Cleaning dirty data is a one and done deal

Data cleansing is all about ensuring your company has the highest quality data possible. The best way to ensure your data’s quality is developing a cleaning routine. The more data your company is collecting, the more often you should be cleaning. What matters is not that you have dirty data, but that you are actively doing something to try and keep your data clean.

When making a cleaning routine you want one of your main considerations to be the impact dirty data has on your company. If you are frequently losing money due to the errors in your data then it may be time to reconsider how often you are cleaning and revising your data.

Interested in keeping your data clean? Check out a demo to see how we keep up!

September 16, 2016

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