Thank you for subscribing!

It's great to feel loved.

Data Cleaning Techniques and Tools

Jun 09, 2020 4 min read

Data cleaning is of vital importance for data analysis or any data analytics you do. It ensures the dataset is free of corrupt or inaccurate information. In this way, you know your data yields accurate results.

To perform the data analytics properly, you need proper data cleaning techniques that will ensure your data is ready for analysis. It’s also good to be familiar with some of the best data cleansing tools that will improve efficiency.

What Is Data Cleaning?

Data cleaning is the process of removing or fixing corrupted, incorrect, incomplete, duplicate, or incorrectly formatted data within a dataset to ensure your data is consistent, correct, and useable.

This process is not only about deleting information to make space for new data. It’s actually about maximizing the accuracy of a data set without necessarily erasing information. It requires more actions than removing data.

Why Is Data Cleaning Important?

Data cleaning improves data quality which is extremely important no matter what type it is. Businesses rely on data for a lot of things, but not many of them address data quality. Providing reliable and effective use of data increases the intrinsic value of brands.

Inaccurate, old, or dirty data can affect the results, and combining several data sources increases the risk of mislabeled or duplicated data. No matter how correct the results and algorithms may seem, they will be unreliable if the data you use is incorrect.

Improving data quality through the process of data cleaning can avoid issues like incorrect invoices, manual troubleshooting, and expensive processing errors.

That’s why businesses need to pay more attention to data cleaning.

The advantages of data cleaning include:

  • Removing major inconsistencies and errors which are common when combining several data sources;
  • Improving efficiency since everyone can quickly get what they need from the data;
  • Reducing the number of errors and increasing the number of happier customers and employees;
  • Reducing operational costs and maximizing profits in business enterprises.

Best Data Cleaning Techniques

What data cleaning technique you’ll use depends on many factors, such as the type of your data. You may need to use more than one technique for optimal results. Here are some of the best data cleaning techniques you should use to get rid of useless data.

1.Removing Irrelevant Values

Removing useless data from your system is the first thing you should do. You don’t need any irrelevant or useless data that doesn’t fit the context of your issue.

For example, if you want to know the number of customers you’ve contacted this month, you don’t need the data of customers you’ve reached in a prior month.

Make sure the piece of data you plan to remove is irrelevant and you won’t need it later to check its correlated values. Do not delete something that you’ll regret deleting it later on.

2.Removing Duplicate Values

You don’t need duplicate values; they are useless and are only wasting your time and space. You can remove them with simple searches. There are a few reasons why you may have duplicate values in your system.

The most common reasons include combining the data of multiple sources and repeating a value by mistake, such as a user filling out an online form and clicking twice on “enter.”

Get rid of the duplicates the moment you locate them.

3.Correcting Errors such as Typos

Typos are very common and you can find them everywhere. But, many techniques and algorithms can help you fix them. Mapping the values and converting them into the right spelling is one of them. Correcting typos is crucial as models treat different values differently.

Incorrect capitalization and strange naming conventions are other errors that can cause mislabeled classes or categories. For instance, you can find “Not Applicable” and “N/A” both appear, but they need to be analyzed as one category.

4.Data Type Conversion

Data types should be uniform across a dataset. A numeric can’t be a Boolean, nor a string can be numeric. Here are a few things to consider regarding data type conversion:

  • Make sure numeric values are kept as numerics;
  • See if you’ve entered a numeric as a string – this is incorrect;
  • When unable to convert a certain data value, make sure you enter something like ‘NA value.’ Don’t forget to add a warning to mark a specific value as wrong.

5.Dealing with a Missing Value

You may not be able to avoid missing data, but you should learn how to take care of it so that your data can be clean and error-free. If there are too many missing values in one column in your dataset, make sure you remove the entire column due to a lack of data to work with.

Do not ignore missing values as it can contaminate your data and lead to inaccurate results. Ways to deal with missing values include:

  • Imputing missing values – using median or linear regression to calculate the approximate value, or copying the data from a similar dataset;
  • Flagging missing values – to inform the model that a specific data is missing (you can use 0 if the missing value is numeric, or enter ‘missing’ if it’s a categorical value);

Best Data Cleaning Tools

Using a data cleaning tool can save a database administrator valuable time by helping administrators or analysts begin their data analysis more quickly and have more confidence in it.

Some of the best data cleaning tools that can keep your data clean and accurate and allow you to analyze data to make smart decisions include:

Conclusion

Data cleaning ensures you have clean, accurate, and reliable data that can increase overall productivity and help you make informed decisions. You can use different data cleaning techniques according to the type of data, as well as data cleaning tools for even more efficient business practices and faster decision-making.

Wendy
Written by Wendy

Wendy is a data-oriented marketing geek who loves to read detective fiction or try new baking recipes. She writes articles on the latest industry updates or trends.

In 2017, Economist published an article calling data the new oil, and since then, it became a common refrain. And it’s what companies are looking at more intently than ever before. Even though the brand has complete access to data, just knowing what customers do is not enough. The modern business landscape is a data-driven environment. The big data and business analytics market were valued at $189 billion in 2019, and it’s expected to grow to $274 billion by the end of 2022. 
Read more...
Benediktas Kazlauskas
Jun 04, 2021 6 min read
A data product is a software application or tool that incorporates data to assist organizations in making better decisions and processes. Non-data scientists may use Data Science to include predictive analytics, descriptive data modeling, data processing, deep learning, risk assessment, and various research methods through data elements with a convenient user interface. The main driver for organization adoption is achieving market goals through informed decisions made with insights from data elements. Here, we will provide you with a step by step guidance on how to build your own data products.
Read more...
Whatagraph team
May 27, 2021 8 min read
Descriptive analytics is one of the four main types of data analytics, along with diagnostic, predictive, and prescriptive analytics. DA is here to help businesses interpret data and better understand changes that occurred during any specific period.
Read more...
Whatagraph team
May 24, 2021 5 min read