clean data tips

The data should always be meaningful and clean. You should always be familiar with data analytics for planning of your data. The very first step is the cleaning of the data that you are receiving or sending.

You use all your different types of data cleaning efforts. You can enhance your data cleaning efforts by using our best of the tips.

Top Tips to Clean Data

Here, we discuss our top 10 tips for cleaning data, be it for a student or a business consultant.

Remove the Duplicate Observations

You can easily remove the duplicate elements or irrelevant type of observations from your data set. So, remove this type of observation, and you are good to go.

Remove the Extra Space

Getting rid of the extra space is another way to clean your data. You can use the Trim function for that. It just takes a single argument that can either be the manually written text or cell reference. It would then remove all leading, trailing, and extra spaces between words. The extra space is a waste and so should always be removed. This will clean the data.

Fix Those Errors

You can fix the structural errors, which will help clean the data. You need to remove typos and other inconsistencies or any mislabeled class. This will clean your data and make it better. Any business consultant would want error-free data.

Do Not Ignore the Missing Values

You can not ignore the missing values and then have clean data. Be it the missing categorical data or the missing numerical data; you should not ignore it. It would help if you allowed the algorithm to estimate the data for any missing values. This will help you clean your data.

No Unwanted Outliers

Another tip you can use for cleaning the data is that you should only filter the unwanted outliers. Outliers are not always harmful. Therefore, you should only remove those kinds of outliers that can harm the data. Like the suspicious measurements which can not be real data, they can be removed. Filtering out such outliers would help you clean your data well.

No Need for False Conclusions

You can always remove the false conclusions that affect the data and its cleaning. Algorithms get affected by false conclusions. They can arise due to unwanted outliers or missing data. Therefore, whenever you are ready to clean your data, you should also remove those false conclusions.

Use the Cleaning Tools

You can always use some data tools like JASP, Rapid, Orange, etc. These tools can always ease up your need to clean the data. They are the best data cleaning tools that you can use any time. They also have other benefits, along with data cleaning help.

Change the Case of the Data

One good data cleaning tip is that you can change the data text to Lower or Upper or Proper cases. This will help make all of your data consistent, and you do not have to worry about the missing parts of the text. It can quickly help to clean your data whenever you wish to.

Use the Spell Check

Spell check is one right way to clean your data well. Any typos or grammatical errors can make your data imbalanced. So, it would be best if you made sure that whatever data it is you are presenting, it is error-free. one right way is to select all your data and then press F7. It shall run the error detection check and will make your data clean. It will show you the errors in the data along with suggestions to clean those errors.

Use Formatting Option

The last but not the least tip for cleaning the data is that you can delete all the formatting. All you need to do is select the data. Then you need to press the home button and select the option of exact format. This will very quickly help. To remove all the data service and clean it. Or you can remove the comments or the hyperlinks if you don’t want that to be in the data.


Make the most of the right steps to clean data that is no more required. It will help you to de-clutter while keeping important data organized.

By Anurag Rathod

Anurag Rathod is an Editor of, who is passionate for app-based startup solutions and on-demand business ideas. He believes in spreading tech trends. He is an avid reader and loves thinking out of the box to promote new technologies.