Data Wrangling vs. Data Cleaning: How Are They Different?

Reading time: 6 min read
Raj Vardhman
Written by
Raj Vardhman

Updated · Aug 22, 2023

Raj Vardhman
Chief Strategist, Techjury | Project Engineer, WP-Stack | Joined January 2023 | Twitter LinkedIn
Raj Vardhman

Raj Vardhman is a tech expert and the Chief Tech Strategist at, where he leads the rese... | See full bio

Lorie Tonogbanua
Edited by
Lorie Tonogbanua


Lorie Tonogbanua
Joined June 2023 | LinkedIn
Lorie Tonogbanua

Lorie is an English Language and Literature graduate passionate about writing, research, and learnin... | See full bio

Techjury is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission. Learn more.

Most big data analysts spend around 80% of their time on data cleaning and wrangling. With the world creating over 1 trillion MB of data daily, wrangling and cleaning have become more useful than ever.

Data wrangling prepares data for analysis by converting it to a more usable format. On the other hand, data cleaning checks for errors and fixes them to make the data set reliable.

Both data wrangling and data cleaning have roles comparable to each other. Thus, many wonder about how they differ from each other.

Keep reading to learn the differences between data wrangling and data cleaning! This way, you'll understand how they can lead to more valuable data.

🔑 Key Takeaways

  • Data wrangling and data cleaning are essential processes in data analysis, occupying about 80% of analysts' time due to the immense daily data generation.
  • These processes vary in steps, focus, work, and goal. Data wrangling has six steps: discovery, structuring, cleaning, enriching, validating, and publishing. Data cleaning includes four stages: removing, fixing, managing, and handling.
  • Data wrangling benefits access and insights, while data cleaning provides error-free data, cost reduction, but entails automation risks

Differences between Data Wrangling and Data Cleaning

Despite their exact nature, data wrangling and data cleaning differ in a lot of ways. 

Data wrangling means translating and mapping data to make it uniform for analysis. It works on raw and unstructured data and turns them into one format.

This process is essential since raw data comes in various forms. With data wrangling tools, you can organize and format data for others to understand.

In essence, it makes a set of data accessible for automation. It also creates a reliable source for every analysis and interpretation.

📝 Note: Wrangling is vital for understanding large amounts of data. With over 95% of businesses facing challenges with unstructured data management, many businesses see data wrangling as vital to their operations. 

Data cleaning means locating and fixing inconsistent data from a source. It needs detailed checking to see if there's anything to fix.

This process is necessary since it's common for data sets to contain errors or invalid data. With cleaning, you can remove or fix these errors to improve reliability.

In essence, it makes a set of data error-free for further use. It also makes the scene more reliable as it avoids errors.

Here are some insights for a better understanding of the differences between the two:


The data wrangling process involves the formatting and mapping of data. It turns raw data from one or more resources into a usable and uniform format. 

As a result, it offers a final output that you can automate to give a data-based insight or action.

The data cleaning process involves locating and resolving inconsistent data within a source. It finds any missing or false data and adds or changes it for correction. 

As a result, it offers error-free data you can use for research or wrangling.


Data wrangling is a time-consuming process. It involves six steps:

  1. Discovering - understanding the data from one or more sources
  2. Structuring - formatting every data to make them uniform
  3. Cleaning - removing any false, irrelevant, or insufficient data
  4. Enriching - adding relevant data to fill any blank spots
  5. Validating - confirming every data to see if they are accurate or valid
  6. Publishing - sharing the data with the team or organization

Meanwhile, the data cleaning comprises four stages. These are:

  1. Removing - removing duplicate, irrelevant, or redundant data
  2. Fixing - fixing typos, different names, capitalizations, mislabels, etc. 
  3. Managing - removing any data point that stands out from the rest
  4. Handling - dealing with missing data by providing observations


Data wrangling focuses on transforming the data format. It works on every piece of raw data and turns it into one style or design for uniformity.

On the other hand, data cleaning focuses on locating and removing invalid or irrelevant data. It works on one set and checks the data, removing anything erroneous to get a reliable source.


Data wrangling work involves the preparation of data for analysis. It changes the structure to have a set with only one style of data.

Meanwhile, data cleaning work applies to improving consistency and reliability. It checks the data and ensures everything is valid to create a reliable source.


Data wrangling's goal is to prepare every piece of data in a set. Its final output is supposed to be accessible for future use—usually to create insights.

Alternatively, data cleaning aims to solve discrepancies in a data set and preserve the data for analysis.

With all the above points, it is now easier to conclude that data wrangling and data cleaning differ in multiple ways. To put it all together, check out the table below: 


Data Wrangling

Data Cleaning


Formats and maps data

Identify and fix data inconsistencies


A six-step process that includes understanding and enriching data 

Composed of four steps focused on removing and fixing data


Remaking the data format to an ideal structure

Extracting irrelevant data


Prepares data for analysis

Enhances quality and reliability of data


To set up data in a set for future use

To overcome discrepancies in a data set

Benefits And Drawbacks 

Other than the qualities above, data wrangling and data cleaning also differ in their benefits and downsides. If you plan on going through these processes, expect the following positives and negatives. 

Pros and Cons Of Data Wrangling

Below are some of the benefits and drawbacks you can expect from data wrangling:



Enhances the user's access to data

Takes too much time, especially when handling a high volume of data

Makes it faster to get insights through efficient analysis

Challenging to turn data from various sets into one format

Improves business intelligence with data-driven decisions and actions

Faces security and privacy restrictions in sensitive data

Pros and Cons Of Data Cleaning

Here are some advantages and disadvantages you can expect with data cleaning:



Offers error-free data sets

Lose insights or actions due to insufficient data

Lesser costs and mistakes caused by errors

Leads to more risks when automated

Improves reliability of data for analysis

Takes too much time, especially with a high volume of data

Provides high-quality information for decisions and actions

Costs a lot with both tools and process


Data wrangling and data cleaning may have methods that are similar by nature. However, they remain two different processes. 

Despite the differences, note that cleaning and wrangling complement each other. In data management, cleaning and wrangling go hand-in-hand for better analysis.


What is an example of data wrangling?

An example of data wrangling is combining data from several sources into one. Each source and data have different formats, so the process turns them into one structure for uniformity—and, eventually, analysis.

What tools might you use for data cleaning?

Some data cleaning tools that you can use are OpenRefine, Winpure Clean & Match, and TIBCO Clarity. You can also use the Melissa Clean Suite and the IBM Infosphere Quality Stage.

Why is data cleaning important in machine learning?

Data cleaning is important because you can only get good results from good data. This fact applies regardless of what machine learning algorithm you use. With data cleaning, any algorithm will be successful.



Facebook LinkedIn Twitter
Leave your comment

Your email address will not be published.