What is Data Cleansing? Tools and Steps to Clean Your Dirty Data

The process of locating and erasing inaccurate or faulty records from a database or dataset is known as data cleansing. Data cleansing helps to ensure that the data being utilized is correct and reliable, making it a crucial step in the data management process. It is essential for businesses that use data to make decisions, since insufficient data can result in inaccurate conclusions and wrong actions.

The content of this article will include:

• What is Data Cleansing or Data Cleaning?
• What is the purpose of Data Cleansing?
• Steps Involved in Data Cleansing process
• Requirements to start Data Cleaning
• Characteristics of Quality Data after cleansing
• Python vs SQL vs Excel: Which is best for Data Cleansing?
• Using Excel
• Using SQL
• Using Python libraries
• Is Data Cleansing an important skill in Data Science?

What does data cleaning or data cleansing mean?

Data cleaning, sometimes referred to as data cleansing, is locating and removing errors and inconsistencies from data.

Data cleansing can handle several typical problems, including handling duplicate data, inconsistent data, obsolete or incorrect data, and missing data. To correct these kinds of mistakes in the data, data cleansing often entails a combination of manual examination and automated tools and algorithms.

What is the purpose of Data Cleansing?

The primary purpose of data cleansing is to increase the quality and reliability of the data. Data cleansing is important because poor-quality data can lead to incorrect or misleading conclusions when the data is analyzed or used for other purposes. Data cleansing helps ensure that the information is accurate, consistent, and complete, which can help improve the accuracy of analyses and the reliability of any conclusions or recommendations based on the data.

Advantages of data cleansing include:

Improved accuracy: By identifying and correcting errors and inconsistencies in the data, data cleansing helps improve the data’s accuracy.

Improved decision-making: Data cleansing helps ensure that the data used for decision-making is accurate and reliable, leading to better outcomes.

Increased efficiency: Data cleansing can reduce the time and resources needed to work with dirty data, eliminating the need to identify and fix errors manually.

Improved data security: Data cleansing can help identify and remove sensitive or personal information from the data, enhancing data security and privacy.

This technique aims to prepare data for analysis and other downstream uses, as it helps ensure that the data is accurate and reliable.

Steps involved in Data Cleansing process:

Now, after talking about the purpose and advantages of data cleansing, let’s see how to clean the data and the steps involved.

Step 1: Understand the data and identify the important data fields:

Understanding the data or dataset utilized and identifying which data or data fields are essential for a particular project or activity are the initial steps in data cleansing.

Step 2: Remove duplicate and irrelevant data

The majority of duplicate observations will occur during data gathering. Sometimes duplicate data is created when we integrate datasets from several sources, scrape data, or get data from clients or different departments. We must eliminate data duplicates and unnecessary information that we won’t use much in our study.

Step 3: Fix structural errors

Structural errors usually occur when we measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization. These inconsistencies can cause mislabeled categories or classes. For example, we may find that “age” and “AGE” appear as two different categories but are the same. All these inconsistencies need to be taken care of.

In the above table, the [Currency code] contains text values, and it is opted for “Not avail.” We need to handle such errors.

Step 4: Handle missing data

Since many algorithms won’t allow missing values, data or empty values should be used instead. There are a few options for handling missing data.

One is that you can remove observations with missing values, but doing so will result in the loss of information, therefore you must be cautious before doing so. Imputing missing values based on other data points using statistical techniques like mean, median, and mode regarding the variable and its relevance is another way to handle missing data.

Step 5: Validate your data

To make sure that your dataset is prepared for data transformation and analysis, validate it. One can inquire, “Does the data make sense?” for example. Does the data have the right variables for the field it belongs to? Does it provide any new information? Are there any patterns in the data that can help you develop your next theory? If not, is there a problem with the data’s quality?

Data cleaning includes reviewing the data because it helps to preserve the data’s integrity.

Characteristics of Quality Data after cleansing:

There are several characteristics that can indicate that data has been successfully cleansed and is of high quality:

Completeness: All required fields are present and have valid data. For example, a customer record should include the customer’s name, address, and contact information.

Accuracy: The data is correct and reflects the reality it is intended to represent. For example, a person’s age should accurately reflect their true age.

Consistency: The data is consistent within itself and with other relevant data sources. For example, if a person’s name is recorded as “Jay Smith” in one record and “Smith, Jay” in another, the data is not consistent.

Timeliness: The data is current and up-to-date.

Validity: Data is valid if it meets the criteria for the data type and follows any rules or constraints that have been set. For example, a phone number should only include numerical digits and should be a certain length.

Integrity: The data is not corrupted and has not been tampered with.

Uniqueness: The data does not contain duplicate records or values.

Relevance: The data is appropriate and relevant for its intended use.

By ensuring that your data meets these characteristics, you can have confidence that it is clean and of high quality and can be used effectively for analysis and decision-making.

Excel vs SQL vs Python: Which is best for Data Cleansing?

The ideal solution for data cleansing relies on the particular requirements of your project, as well as your preferences and level of expertise. For each instrument, keep in mind the following:

Using Excel:

The spreadsheet program Excel is popular for handling and analyzing data. It contains a range of data purification features, including sorting, filtering, and pivot tables, which make handling small to medium-sized datasets quite simple. The graphic below serves as an illustration of how structural problems in the data can be fixed using Excel.

If you need to execute straightforward data transformations on a limited volume of data, Excel is a suitable option. If you are more comfortable using spreadsheets than computer languages, it is also a wise choice.

Using SQL:

Relational databases use the computer language SQL (Structured Query Language) to manage and manipulate data. It is best suited for data purification operations that require handling huge datasets and carrying out intricate data transformations.

When you have a lot of data and need to execute more complex data purification operations, such as combining and deduplicating data, SQL is an excellent option.

Using Python:

Data purification and modification sometimes involve the usage of Python, a well-liked computer language. It offers many tools and libraries made especially for working with data, like pandas and NumPy. The sales data has three null values in the Sales column and two null values in the pricing column, as can be seen in the example below. The appropriate handling of these missing values is required.

Python is particularly useful for complex data-cleansing tasks that require custom algorithms or scripts. It is also useful for automating data cleansing tasks or integrating them into larger workflows.

Is Data Cleansing an important skill in Data Science?

Yes, data cleansing is a crucial Data Science ability. Finding and fixing data mistakes and inconsistencies is known as data cleansing, sometimes known as data cleaning or data wrangling.

When working with huge datasets, data purification is especially crucial because manually identifying and fixing data mistakes can be time-consuming and error-prone. Data Scientists must, therefore, possess the knowledge and resources necessary to successfully clean and prepare data in order to analyze it and derive meaning from it.

Data cleansing might involve not just finding and fixing problems in data but also transforming or combining data from different sources, as well as locating and dealing with missing or incomplete data. To ensure that the data being utilized for analysis is of high quality and reliable to accurately reflect the underlying phenomena being examined, all of these tasks are crucial.

Conclusion

Overall, data cleansing is a crucial Data Science ability that is vital for guaranteeing that data is accurate, can be properly analyzed, and can be used to make decisions. For instance, if you conduct a survey and ask participants for their phone numbers, they may enter their numbers in a variety of formats. In these types of data sources, we must recognize, manage, and segregate the various types of data that are collected. This is where data cleaning comes in handy and aids us in deriving the proper conclusions from the data.

FAQs:

1. Do I need to know programming to do data cleansing?

It depends on the tools and methods you choose to use for data cleansing. Some data cleansing tasks, such as removing duplicates or standardizing formatting, can be accomplished using simple spreadsheet software such as Microsoft Excel. These tasks typically do not require programming knowledge.

However, more advanced data cleansing tasks, such as identifying and correcting inaccuracies in data, may require programming skills. For example, you may use Python or R programming language to write scripts that can automatically identify and correct data issues in large datasets. It’s also common to use SQL (Structured Query Language) to clean and manipulate data in relational databases.

So, if you don’t have programming skills, you can still cleanse data, but it might be more limited in scope and a more time-consuming task. On the other hand, if you have programming skills, it can automate many tasks and allow you to handle large datasets much more efficiently.

2. Can you automate the process of data cleansing?

Yes, the process of data cleansing can be automated to some extent. Automation can make the process of data cleansing more efficient and less prone to errors, particularly when working with large amounts of data.

There are several ways to automate the data cleansing process. One common method is to use programming languages such as Python or R to write scripts that can cleanse data. Another way to automate data cleansing is through the use of specialized data cleansing tools and software packages Like OpenRefine, Trifacta Wrangler, Talend Data Cleansing, Informatica Data Quality, etc. that are powerful tools and come with easy-to-use interfaces to cleanse data. These tools typically provide a graphical user interface (GUI) that makes it easy to perform data-cleansing tasks without programming knowledge.

Additionally, more advanced data cleansing tools can include machine learning techniques that can be used to identify patterns and automatically cleanse data, such as correcting data entry errors, standardizing data, and identifying duplicates. Overall, the process of data cleansing can be automated to some extent, but it depends on the complexity of your data and the tools you’re using.

3. Is data cleaning a part of ETL?

Yes, the Extract, Transform, and Load (ETL) process includes data cleaning. Data migration, or ETL, is the process of moving data from one system to another. It frequently entails numerous processes, including data extraction from various sources, data transformation to make the data consistent and usable, and data loading into the target system.

In the ETL process, data cleansing is frequently seen as a component of the “transform” step. Data is prepared for loading into the target system during the transformation process. This involves activities including locating and deleting duplicate information, standardizing data types and formats, and filling in blanks. Prior to loading the data into the target system, it is crucial to clean the data to make sure it is correct, consistent, and reliable for analysis or decision-making.

4. What tool is easy to use for data cleansing?

There are several tools that are easy to use for data cleansing, depending on your specific needs and the complexity of the data you are working with. The most commonly used tools are Microsoft Excel, SQL, and Python. Apart from these, there are many software packages and tools like OpenRefine, Trifacta Wrangler, and Talend Data Cleansing.

There are many other tools available. Some are paid, and some are open-source. It really depends on your specific needs and the complexity of the data you are working with. It is a good idea to evaluate different options and choose a tool that is easy for you to use and can perform the specific data cleansing tasks you need.

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