Although data has always been valuable, in recent years, it has grown exponentially. The primary cause of this is technology, which makes gathering, transferring, and storing data simpler than before. However raw data typically needs a lot of cleaning and processing before it can be used. Among the most important duties of a data analyst are organizing data for analysis, addressing outliers, and working with unorganized or incomplete datasets. In addition to fixing mistakes, this process involves reshaping the data to meet the particular aims and objectives of the analysis.

Data wrangling is an iterative, frequently creative process that necessitates a thorough comprehension of the analysis’s goals as well as the data. Robust and significant insights are the result of successful data wrangling, which facilitates more efficient decision-making and precise model training. An essential first step in the pipeline for data analysis and machine learning is data cleaning.

In this article, we will learn all about data wrangling and data cleansing and most importantly data wrangling vs data cleaning.

What is Data Wrangling?

Data wrangling, often called data munging, is the act of transforming and processing raw data into a format appropriate for analysis. Ensure that the data is accurate, complete, and ready for analysis, This entails finding and correcting errors and inconsistencies in the data, converting the data from one format to another so that it is suitable for analysis, and combining the data from multiple sources into a single, unified dataset. 

Data munging necessitates a thorough comprehension of the raw data, the kinds of analyses that must be performed, and the information that must be left out. A variety of strict standards and requirements must be implemented in order to prevent bias and ensure that the data is cleansed and processed correctly.

Why do we Need Data Wrangling?

Data wrangling is the process of cleansing and organizing raw data into the necessary format so that business users can make informed decisions quickly. Top firms are increasingly using data wrangling as a standard procedure as their data grows more diversified and unstructured.

Precise data handling guarantees that high-quality data is sent into analytics or other downstream procedures for cooperation and consolidation. Optimizing the data-to-insight process and facilitating precise decision-making is crucial.

Data integration technologies with automation features that clean and transform data sources into a reusable format in accordance with the end requirements help organize data wrangling into a consistent and repeatable process. Important cross-data set analytics can be carried out once the data has been converted to a standard format. 

What is Data Cleaning?

Data cleaning is the process of locating and fixing erroneous data from a specific data set or data source, sometimes known as data cleansing. Finding and eliminating discrepancies is the main objective—while preserving the data that is required to provide insights. To improve the quality of the data set, it is crucial to eliminate these inconsistencies.

Finding duplicate entries, filling in blank spaces, and correcting structural flaws are just a few of the many tasks that go under the umbrella of cleaning. These chores are essential to guaranteeing accurate, comprehensive, and consistent data quality. Cleaning helps to reduce downstream errors and difficulties.

Why do we Need to Cleanse Data?

The quality of analyses and algorithms depends on the quality of the data they are based on. Organizations generally think that roughly thirty percent of their data is erroneous. Companies lose more than just money when they use this contaminated data, which accounts for 12% of their total income. Cleansing generates reliable, accurate, and consistently organized data that enables decision-making that is well-informed. In order to save time and money both now and in the future, it also identifies areas where upstream data input and storage settings need to improve.

Data Wrangling vs Data Cleaning

Although they are similar procedures in the data management and analysis cycle, data wrangling and data cleaning differ slightly from one another: Finding and fixing flaws, inconsistencies, and inaccuracies in data is the process of data cleaning. It involves things like dealing with missing data, fixing typos, and getting rid of outliers and duplicates. The goal of data cleaning is to guarantee that the data is reliable, consistent, and prepared for additional examination.

On the other side, data wrangling encompasses a wider range of activities that get the data ready for analysis. In addition to data cleansing, it also involves data integration, data transformation, and data reorganization. It entails merging and combining data from several sources, changing data between formats, extracting new variables and characteristics, and fusing disparate data sources into a single, cohesive dataset.

Put differently, data wrangling is a broader process that includes all tasks associated with preparing raw data for analysis, whereas data cleaning is a subset of data wrangling. Data wrangling makes sure that the data is correct, consistent, and in a format that is appropriate for analysis and satisfies the particular needs of the study.

In summary, the data management and analysis cycle requires both data wrangling and data cleansing as necessary processes. The goal of data cleaning is to eliminate errors and inconsistencies from the data, whereas data wrangling includes all the steps needed to get the data ready for analysis, such as integration, cleaning, transformation, and restructuring.


Data wrangling, is one of the most important phases in the data analysis process. Its objective is to convert unstructured, unclean raw data into a well-organized format that can be utilized for analysis and decision-making.

It’s important to keep in mind that organizing data might take a lot of time and resources, particularly when done by hand. In order to benefit from the greatest and most outcome-driven BI and analytics, an organization must first master the art of data wrangling.

Many businesses require data to contain specific information or be in a certain format before being uploaded to a database. These policies and best practices are designed to assist employees in streamlining the data cleanup process.