These top 10 data cleaning techniques will help businesses to grow in 2023
Data cleaning, data cleansing, or data scrubbing is the act of first identifying any issues or bad data, then systematically correcting these issues. If the data is unfixable, you will need to remove the bad elements to properly clean your data. Unclean data normally comes as a result of human error, scraping data, or combining data from multiple sources. Multichannel data is now the norm, so inconsistencies across different data sets are to be expected. Here are the top 10 data-cleaning techniques for businesses to adopt in 2023.
When you collect your data from a range of different places or scrape your data, it’s likely that you will have duplicated entries. These duplicates could originate from human error where the person inputting the data or filling out a form made a mistake. Duplicates will inevitably skew your data and/or confuse your results. They can also just make the data hard to read when you want to visualize it, so it’s best to remove them right away.
Remove Irrelevant Data
Irrelevant data will slow down and confuse any analysis that you want to do. So, deciphering what is relevant and what is not is necessary before you begin your data cleaning. For instance, if you are analyzing the age range of your customers, you don’t need to include their email addresses.
Within your data, you need to make sure that the text is consistent. If you have a mixture of capitalization, this could lead to different erroneous categories being created. It could also cause problems when you need to translate before processing as capitalization can change the meaning. For instance, Bill is a person’s name whereas a bill or to bill is something else entirely. If, in addition to data cleaning, you are text cleaning in order to process your data with a computer model, it’s much simpler to put everything in lowercase.
Convert Data Types
Numbers are the most common data type that you will need to convert when cleaning your data. Often numbers are imputed as text, however, in order to be processed, they need to appear as numerals. If they are appearing as text, they are classed as a string and your analysis algorithms cannot perform mathematical equations on them. The same is true for dates that are stored as text. These should all be changed to numerals. For example, if you have an entry that reads September 24th, 2021, you’ll need to change that to read 09/24/2021.
Machine learning models can’t process your information if it is heavily formatted. If you are taking data from a range of sources, it’s likely that there are a number of different document formats. This can make your data confusing and incorrect. You should remove any kind of formatting that has been applied to your documents, so you can start from zero. This is normally not a difficult process, both excel and google sheets, for example, have a simple standardization function to do this.
It probably goes without saying that you’ll need to carefully remove any errors from your data. Errors as avoidable as typos could lead to you missing out on key findings from your data. Some of these can be avoided with something as simple as a quick spell-check. Spelling mistakes or extra punctuation in data like an email address could mean you miss out on communicating with your customers. It could also lead to you sending unwanted emails to people who didn’t sign up for them. Other errors can include inconsistencies in formatting. For example, if you have a column of US dollar amounts, you’ll have to convert any other currency type into US dollars so as to preserve a consistent standard currency. The same is true of any other form of measurement such as grams, ounces, etc.
Unifying the data structure
You’ll need to ensure data from different sources is consistent by mapping it to a unified underlying structure.
The Natural Language Processing (NLP) models behind software used to analyze data are also predominantly monolingual, meaning they are not capable of processing multiple languages. So, you’ll need to translate everything into one language.
Handle Missing Values
Removing the missing value completely might remove useful insights from your data. After all, there was a reason that you wanted to pull this information in the first place. Therefore, it might be better to input the missing data by researching what should go in that field. If you don’t know what it is, you could replace it with the word missing. If it is numerical you can place a zero in the missing field. However, if there are so many missing values that there isn’t enough data to use, then you should remove the whole section.
Validating your data
This is the final step of the process. It usually involves executing scripts that check if you’ve carried out all the other steps of the process correctly. You’ll often have to go back and repeat some of the earlier steps.
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