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

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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:

Process

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.

Steps

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

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

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

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

Focus

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.

Work

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.

Goal

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:

Criteria	Data Wrangling	Data Cleaning
Process	Formats and maps data	Identify and fix data inconsistencies
Steps	A six-step process that includes understanding and enriching data	Composed of four steps focused on removing and fixing data
Focus	Remaking the data format to an ideal structure	Extracting irrelevant data
Work	Prepares data for analysis	Enhances quality and reliability of data
Goal	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:

Benefits	Drawbacks
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:

Benefits	Drawbacks
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

Conclusion

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.

FAQs.

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.

Sources.

NY Times

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