We rely on data as an industry to separate the signal from the noise, uncover insights, and make smarter decisions. Inconsistencies in data entry, incorrect or missing values, and unnecessary information all muddy the waters, making accurate insights difficult and ultimately losing confidence, even in organizations with mature business intelligence programmes. As the saying goes, “garbage in, garbage out.”

If your users do not trust the data, it makes no difference whether you have enabled them to analyze the data themselves using self-service analytics tools. They simply will not accept them.

That is why data cleaning is crucial to maximizing the value of the current data stack.

The process of discovering and repairing mistakes and inconsistencies in data sets so that they can be used for analysis is known as data cleaning. By doing so, data professionals may gain a better understanding of what is going on in their organizations, give trustworthy statistics that any user can utilize, and assist their organizations in operating more efficiently.

The more precise your data set, the more precise your insights. And, as a Harvard Business Review study shows, every insight counts when it comes to making business decisions, whether by executives or frontline decision makers. That is why, if you want to get the most out of your data, data cleaning should be at the top of your priority list.

To begin, we should remember that each scenario and data set will necessitate a unique set of data cleansing techniques. The approaches we are about to go over address the most prevalent concerns that are likely to develop. When cleaning your data, you’ll most likely need to use a few of these strategies.

Before you begin cleaning, consider your goals and what you intend to gain from cleaning and analyzing this data. This will assist you in determining what is and is not important in your data. It’s also a good idea to establish some ground rules or standards before you begin entering data. Using only one style of date format or address format is an example of this.

Here are 8 effective data cleaning techniques:

1. Remove Duplicates

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. 

2. 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.

Other elements you’ll need to remove as they add nothing to your data include:

• Personal identifiable (PII) data
• URLs
• HTML tags
• Boilerplate text (for ex. in emails)
• Tracking codes
• Excessive blank space between text

3. Standardize Capitalization

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. 

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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. 

4. 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.

5. Clear Formatting

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. 

6. Fix Errors

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. 

7. Language Translation

To have consistent data, you’ll want everything in the same language. 

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. 

8. Handle Missing Values

When it comes to missing values you have two options:

1. Remove the observations that have this missing value
2. Input the missing data 

What you choose to do will depend on your analysis goals and what you want to do next with your data. 

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.