What Is Data Cleaning and Why Is It Necessary?


You might be wondering, “what is data cleaning?” It is an integral part of a process where the data needs to be cleaned before it can be used effectively by any organization or business. In recent times, the importance of data has increased exponentially. Data is used not only for storing information but also for many other purposes like processing, analyzing, and then making effective decisions.  

The amount of data generated each day is around 2.5 quintillion bytes. By 2028, Big Data analytics will generate $75.23 billion for the healthcare industry. Currently, the entire digital universe contains more than 44 zettabytes of data. User-generated data accounts for 70% of all global data.  

Data cleaning is a critical task that can make or break your business. Understanding the importance of data cleaning and how it helps improve the quality of your products or services is important. 

What Is Data Cleaning? 

Data cleaning means the process of identifying and correcting data errors. It’s an important part of data analysis because it helps ensure you analyze accurate, reliable information. You need to clean your data before you begin analyzing it so that you don’t end up with false conclusions or inaccurate results. 

There are two main ways to clean your data: manual and automatic. Manual cleaning involves manually removing incorrect values from your dataset, one record at a time, as you go through each row of the dataset. Automatic cleaning involves running software on large batches of rows at once (for example, by uploading them into an online tool). This saves time and effort because there isn’t any manual work involved in the process; however, automatic methods may not always be able to identify all instances where errors exist within each row of text (such as typos). 

What Is the Difference Between Data Cleaning and Data Transformation? 

Data cleaning and data transformation are processes that help transform data from its original state into a more useful format. Data cleaning is the process of removing errors, noise, and inconsistencies from data sets. It can also be used to add missing values or correct other problems with a dataset. Data transformation involves changing the structure of your data set so that it can be better analyzed by Machine Learning algorithms (ML). For example, you could use data transformation to convert categorical variables into numerical ones for easier analysis by ML algorithms. 

While these two processes are closely related and often done together, they are distinct enough that they deserve separate definitions in most cases: 

  • Data cleaning as a process refers specifically to removing noise, errors, and inconsistencies within datasets; while 
  • Data transformation refers specifically to changing the structure of datasets before they are used in machine learning algorithms. 

How to Clean Data? 

Data cleaning is the process of identifying and correcting inaccurate, incomplete, or inconsistent data in a dataset. For example, if a company’s records are not standardized from one system to another or if there are missing values for certain fields in one system but not others, this can lead to problems when data cleaning is necessary. 

The data cleaning process can be done manually by humans or automated with tools. Manual data cleaning may require more time and effort than automated processes do, but it gives you greater control over the results because you have complete access to the details of each record. Automated systems can be useful if they produce sufficient quality work under normal conditions. 

Step 1: Remove Duplicate or Irrelevant Observations  

  • Remove rows that contain the same values in more than one field. If a row has two phone numbers, only keep one of them. 
  • Remove rows that don’t contain any values for your target variable (the column you are trying to predict). For example, if an email has no subject line, remove it from your dataset because there is nothing you can use to predict whether or not this email is spam. 

Step 2: Fix Structural Errors  

Structural errors are more difficult to identify and fix, but the process is much the same. For example, consider an address record that contains a misspelled city name or ZIP code. This could be bad data from an external source or simply sloppy work on behalf of someone in your organization who didn’t check their work before importing it into your system. Regardless of where it came from, you must correct these structural errors by reviewing your entire dataset and ensuring each address has the same spelling across all records within that particular row. 

These types of problems can also occur in other places besides addresses; for example: 

  • Phone numbers: If a phone number is spelled differently between rows (or even within the same row), this may indicate either human error during entry or raw data that was gathered by hand rather than collected automatically via online forms (or another method). Either way, if these variations exist within your dataset, they need to be resolved before moving forward with further analysis projects. 
  • Email addresses: This type of problem occurs when two different email addresses are used for one person’s contact information. It can either be due to bad data coming from an external source or because two separate emails were entered for one individual at some point along their journey through customer service channels. 

Step 3: Filter Unwanted Outliers  

Another step in the data-cleaning process is to filter out outliers or values that are significantly different from the rest of your data. If you’re looking at averages and have one value far outside of all other values, that would be considered an outlier. Outliers can skew the results of your analysis if they aren’t removed before the analysis begins. 

There are a few ways you can identify outliers in your dataset: 

  • Histogram graphs allow you to see where most of your data lie in relation to each other by creating a bar graph with bins showing how many values fall into different ranges. 
  • Box plots show five key pieces of information about a dataset: minimums, maximums, quartiles (25th/75th percentiles), medians, and mean (average) values for any given variable over time or across multiple variables simultaneously (like comparing two groups). 

Box plots also include whisker lines showing the minimum outlier value within 1% above or below the median value for each variable plotted, so you can easily spot any potential outliers before calculating them! 

Step 4: Handle Missing Data  

Missing data can be a problem for many types of analysis. This issue is not necessarily due to errors in data capture but rather because there is no valid information available on a given subject. Missing values can occur when: 

  • Errors are made during the collection process or through human error or computer glitches 
  • The subject does not respond to specific questions (e.g., “Do you have children?”) 

The subject cannot provide an answer, such as when they are unaware of the relevant information (e.g., “Are you male or female?”). The subject refuses to respond to certain questions (e.g., “How much money do you make?”). 

Step 5: Validate and Verify 

Once you’ve finished cleaning your data, you’ll want to make sure it’s as accurate and up-to-date as possible. This is where validation comes in. It ensures that the cleaned data meets quality standards. 

With so many different types of data available to use, it’s important to ensure that the information is valid before using it in any analysis or visualization. In addition to checking for accuracy (i.e., making sure all the numbers are correct), it’s important to check for completeness (i.e., ensuring all relevant fields are filled).  

If there are problems with your cleaned dataset—if some values have been calculated incorrectly or if certain aspects haven’t been completed satisfactorily—you should know about them as soon as possible so you can fix them! This will allow you to avoid issues later on when trying out new techniques like regression analysis with variables from unreliable sources. 

Components of Quality Data 

Data cleaning, validation, and quality assurance are different processes that can ensure your data is as accurate and consistent as possible. 

Data cleaning is the process of removing duplicates from your existing data set. Data scrubbing is a more advanced form of cleansing that removes non-standard or non-valid entries from your database. Data verification ensures that the information you have is correct, while validation ensures it’s correct and in compliance with company standards or regulations (e.g., industry standards). 

Data quality assurance ensures that your data is correct and consistent. Accuracy refers to whether or not a piece of information is correct. Consistency refers to how closely related pieces of information are to each other (e.g., they should all be the same length). Data cleansing removes duplicates from your existing data set. 

Advantages and Benefits of Data Cleaning  

Data cleaning is an important part of data analysis. It helps you to make sense of your data, it helps you to find the relationships between your data points and to make predictions about future events. In addition, it also helps to reduce bias in your results, as knowing how clean your data is can help you make better decisions for your company or organization! 

Data Cleaning Tools and Software 

You might have heard the terms “data cleaning” and “data cleansing.” They’re two terms for the same process: removing junk data, duplicates, and errors from a dataset. 

Data cleaning tools can be manual or automated. Manual data cleaning is done by data analysts and data scientists who carefully inspect each piece of information to determine whether it belongs in your dataset. Automated tools also exist for this purpose; however, they’re not as effective at identifying subtle differences between similar items (like names) that should be treated differently depending on their context (like addresses). 


Data cleaning is an important part of the data analysis process. It helps identify and remove errors as well as inconsistencies in your dataset, making it easier to use in different contexts. It also ensures that the data you are using meets certain standards and quality control requirements before being used by others. We hope we were able to help you find an answer to “what is data cleaning?” If you’re interested in pursuing a career in the Data Science field, UNext Jigsaw’s PG Certificate Program in Data Science and Machine Learning is recommended. 

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