8 Powerful Data Cleaning Techniques for Better Data

We all know that data is messy and difficult to manage. Data cleaning techniques are part of a data-driven approach to improve value for our customers, reduce costs and even increase revenue. Cleaning up and managing the data in your business is a daily activity that can help boost performance, increase accuracy, and improve results. But how do you know whether your cleaning practices are effective? Where should you start, and which techniques should you use in your particular situation?

In this article, we will discuss some effective data-cleaning techniques that can be used to improve the quality of your business’s performance.

What is Data Cleaning?

Data cleansing is a process to improve the quality of data before it gets to your application and business. In other words, data cleansing is the process of cleaning dirty data. Data cleanup can be applied manually as well as automatically, depending on your purpose.

Importance of data cleaning

You can complete your analysis much more quickly if you start with clean data that are free of erroneous and inconsistent values. By completing this task ahead of time, you would save a lot of time. You could stop many errors by cleaning your data before using it. Your results won't be accurate if you use data with false values. Data purification and cleaning take up a lot more time for a data scientist than data analysis.

• Efficiency

• Identifying data Quality

• Accuracy

• Error Margin

• Consistency

• Uniformity

Effective Data Cleaning Techniques

The first step to cleaning your data is to understand what types of data you have. Once you know this, you can determine what types of tools are best suited for the job.

You can also select specific values from within each row or column and then export them as text files to create a list of all the data elements that need cleaning up. This is useful when working with large amounts of information because it allows you to see exactly what needs fixing before moving forward with any further steps in order

1. Remove Duplicate Data

Duplicate data is a problem that can be solved easily. In fact, it can be done manually or automatically and the results are almost the same. The first step in removing duplicate data is to identify all duplicate records in your database. Next, you need to merge these records into one record. There are two different methods that you can use for merging records:

• Merge by Right-Clicking/Ctrl+Click

• Merge by Using an Excel Add-In

A detailed explanation of data cleaning techniques can be found via the best data science course in Chennai, designed in collaboration with IBM.

1. Remove irrelevant data

Any analysis you attempt to conduct will be complicated and slowed down by irrelevant data. Deciding what information is relevant and what is not is therefore necessary before you start your data cleaning. You don't have to include their email addresses, for example, if you are analyzing the age range of your customers.

You should also eliminate the following components because they don't add anything to your data:

• URLs

• HTML tags

• Tracking codes

• Personal Identifiable (PI)

• Blank space between text

1. Remove Nulls

Nulls should be eliminated as well, as they can cause problems when they are used in arithmetic operations or comparisons. You can do this by using a unique index on the column containing the null values and using a WHERE clause to remove them from your data set.

1. Convert data types

The most typical type of data that needs to be converted when cleaning your data is a number. However, they must appear as numerals to be processed. Numbers are frequently imputed as text.

They are classified as a string if they appear as text, which prevents your analysis algorithms from solving mathematical equations on them.

Likewise, dates that are saved as text are accurate. Make them all numerals, please. For instance, you must change entries to read 09/24/2022, if they currently say September 24th, 2022.

1. Clear Formatting

Your information cannot be processed by machine learning models if it is heavily formatted. Different document formats are probably present if you are using data from a variety of sources. Your data may become muddled and inaccurate as a result.

To start over, you should eliminate any formatting that has been applied to your documents. This is typically not a challenging process; for instance, standardization functions are available in both Google Sheets and Excel.

1. Handle missing values

There would always be some information lacking. It's unavoidable. In order to keep your data accurate and clean, you should be aware of how to handle them. Your dataset might contain too many missing values in one particular column.

In that case, since there isn't enough information to work with, it would be prudent to remove the entire column.

Thus, you should never ignore missing values.

If the missing value is completely removed, your data can now no longer contain insightful information. After all, there was a reason why you initially wanted to gather this information.

Hence, it might be preferable to fill in the missing data by doing the necessary research. You could use the word missing in its place if you have no idea what it is. You can enter a zero in the blank field if it is numerical.

However, you should remove the entire section if there are so many missing values that there isn't enough data to use.

1. Fix the errors

You should obviously take care to rectify any errors in your data before using it. You might miss out on important data findings if you make mistakes as simple as typos. With something as simple as a quick spell check, some of these can be avoided.

You might miss out on communicating with your customers because of misspellings or extra punctuation in data like an email address. It might also cause you to send unsolicited emails to recipients who have not requested them.

Inconsistency in formatting is another type of error. To maintain a consistent standard currency, for instance, if you have a column of US dollar amounts, you must convert any other currency type into US dollars.

1. Language Translation

You need everything to be in the same language to have reliable data.

Software used to analyze data typically uses monolingual Natural Language Processing (NLP) models, which are unable to process multiple languages. Therefore, you must translate everything into a single language.

Summary

To sum up, the best way to go about cleaning data is always dependent on the problem you are trying to solve. The time required for data cleaning will always depend on the data itself, and if any anomalies need to be resolved.

This article is written based on the knowledge of data cleaning techniques applied by experienced professionals. However, you can apply these tips at home to clean your own data, or to help you get a better feel for how much cleaning is needed in your own data before processing or loading it. In the end, invest a little time applying these tips and you'll be rewarded with higher-quality records. For detailed information, you can check Learnbay’s data analytics course in Chennai, and get ready for better and more efficient data for your next projects.

I am Mallik and I am a passionate blogger who loves to write educational and technical content about data science courses, Machine Learning, and Artificial intelligence