Best Data Cleaning Techniques for Preparing Your Data

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve their quality, accuracy, and reliability for analysis or other applications. It involves several steps aimed at detecting and rectifying various types of issues present in the data.

What is Data Cleaning?

Data cleaning, also referred to as data scrubbing or data cleansing, is the process of preparing data for analysis by identifying and correcting errors, inconsistencies, and inaccuracies. It’s essentially like cleaning up a messy room before you can use it effectively.

Raw data, which is data in its unprocessed form, is often riddled with issues that can negatively impact the results of analysis. These issues can include:

• Missing values: When data points are absent from a dataset.
• Inconsistent formatting: Inconsistency in how data is presented, like dates written in different formats (e.g., MM/DD/YYYY, YYYY-MM-DD).
• Duplicates: When the same data point appears multiple times in a dataset.
• Errors: This can include typos, spelling mistakes, or even data entry errors.

Data cleaning helps ensure that the data you’re analyzing is accurate and reliable, which is crucial for getting meaningful insights from your data.

Why Is Data Cleaning so Important?

The important thing about the data cleaning process is that data accuracy and reliability will be at the center of the process of the information used for analysis. Let me explain that with a cooking example, you cannot feed the wrong ingredients to the recipe – the dish will be a mess. In data, we have to credit the “garbage in, garbage out” rule. Here’s why cleaning data is so important:

• Better Decisions: Dirty data generates lying output, or disinformation. With accurate data and clean data, your analysis connected to reality, and guides you for good options.
• Saved Time and Money: Incorrect data can make the right decision making very difficult, cause wasted efforts that might be directed towards unsuitable and wrong leads and solutions. Clean data saves the time and expense of redesigning processes that crashed due to a dirty data issue.
• Improved Efficiency: When data stays clean, the functioning of the whole system becomes easier. Dirty data leads to friction and inefficiency. The duplication of efforts to obtain reliable information will only add to the losses.

Data Cleaning Techniques

Here are Some Important data-cleaning techniques:

• Remove duplicates
• Detect and remove Outliers
• Remove irrelevant data
• Standardize capitalization
• Convert data type
• Clear formatting
• Fix errors
• Language translation
• Handle missing values

Techniques	Description
Remove duplicates	It is likely that you will have duplicate entries if you scrape your data or get it from a variety of sources. These duplication may result from human error on the part of the individual entering the data or completing a form.
Detect and Remove Outliers	Outliers are data points that fall significantly outside the expected range for a particular variable. They can be caused by errors in data collection or measurement, or they may represent genuine but unusual cases. Leaving outliers in your data set can skew your analysis and lead to misleading results. There are a number of statistical methods for detecting outliers, and the best approach will depend on the specific nature of your data. Once outliers have been identified, you can decide whether to remove them from your data set or to investigate them further.
Remove Irrelevant Data	Any analysis you wish to perform will be slowed down and confused by irrelevant data. Thus, before you start cleaning your data, you must determine what is and is not significant. For example, you do not need to provide your customers’ email addresses if you are studying the range of ages of your consumers.
Standardize Capitalization	You must ensure that the text in your data is consistent. Different incorrect categories may be formed if your capitalization is inconsistent. Since capitalization can alter meaning, it could also be problematic if you had to translate something before processing. For example, a bill or to bill is something else entirely, yet Bill is a person’s name.
Convert Data Types	When cleaning your data, numbers are the most frequent data type that needs to be converted. Numbers are frequently imputed as text, but they must appear as digits in order to be processed. They are categorized as strings and cannot be used by your analytical algorithms to solve mathematical equations if they are shown as text.
Clear Formatting	Your input cannot be processed by machine learning models if it is highly structured. There probably are a variety of document formats if you are gathering data from several sources. Your data may become erroneous and unclear as a result. To start from scratch, you should erase any formatting that has been applied to your papers. Usually, this is not a tough task to do; for instance, there is a straightforward standardization feature in both Google Sheets and Excel.
Fix Errors	It should go without saying that you must take great care to eliminate any inaccuracies from your data. Typographical errors are just as prone to error and might cause you to overlook important insights from your data. Something as easy as a fast spell check can help prevent some of them. Errors in spelling or excessive punctuation in data, such as an email address, may prevent you from reaching out to customers. Additionally, you can end yourself sending unsolicited emails to recipients who never requested them.
Language Translation	You will want everything in the same language if you want consistent data. The majority of Natural Language Processing (NLP) models that underpin data analysis tools are monolingual, which means they cannot process more than one language. Thus, everything will have to be translated into a single language.
Handle Missing Values	Eliminating the absent value entirely could lead to the loss of valuable information from your data. You intended to extract this information in the first place for a reason, after all. Thus, it could be preferable to fill in the blanks by looking up the appropriate information for that field. You might use the word missing in its place if you’re not sure what it is. You can enter a zero in the blank box if it is numerical.

Conclusion

Although cleaning your data can take some time, skipping this step will cost you more than just time. You want your data clean before you start your research because “dirty” data can cause a lot of problems.