Data Wrangling vs Data Cleaning – Know the Difference

Data Wrangling vs Data Cleaning: Generally, data or the information is important to small, medium as well as large scale business organizations. Therefore, each organization store data in various forms. They store data in text files, spreadsheets, in XML format, in databases and many other forms. The data from various sources are merged as required and analyzed to make predictions on the business. So overall, There are two methods that we can use to generate useful data. They are data wrangling and data cleaning. So in this article we are discussing about some difference between data wrangling and data cleaning.

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Data Wrangling vs Data Cleaning 

Data wrangling and data cleaning are two related but distinct activities in the process of working with data.

Data wrangling is the process of converting and mapping data from one “raw” data form into another format with the intent of making it more appropriate and valuable for analysis. It typically involves collecting data from a variety of sources, identifying any discrepancies or errors in the data, and then transforming the data into a more useful format.

Data cleaning, on the other hand, refers to the process of identifying and removing errors, inconsistencies, and inaccuracies in the data. This can include tasks such as filling in missing values, correcting inaccuracies, and removing duplicates. The goal of data cleaning is to improve the quality and reliability of the data.

In practice, data wrangling and data cleaning often overlap and are performed together. Data wrangling may involve cleaning the data to some extent, and data cleaning may require some data wrangling in order to perform the cleaning effectively. The two activities are part of the larger process of data preparation, which involves all the steps necessary to get data into a form that is ready for analysis.

What is Data Wrangling?

It is the process of converting and mapping data of one format to another format. In data wrangling, the data is first extracted from a data source in its raw format. Next, this data is sent to an algorithm or parsed into a predefined data structure. The final step is storing this data in a storage unit to use in future. The purpose of this process is to make data more useful for performing tasks such as analyzing. A data wrangler is a person who performs data wrangling and related tasks. Data scientists and business analysts analyze this data to make business decisions.

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The goal of data wrangling is to prepare data so it can be easily accessed and effectively used for analysis. But throughout the wrangling process, it’s important to ensure the data is accurate.

Benefits of Data Wrangling

Although data wrangling is an essential part of preparing your data for use, the process yields many benefits. Benefits include:

• Easy Analysis: Once raw data has been wrangled and transformed, Business Analysts and Stakeholders can quickly, easily, and efficiently evaluate even the most complicated data.
• Simple Data Wrangling: The Data Wrangling method converts raw, unstructured, and jumbled data into useful data in clean rows and columns. In addition, the process enriches the data in order to make it more meaningful and deliver additional intelligence.
• Better Targeting: You may better understand your audience when you mix several sources of data, which leads to better targeting for your Ad Campaigns and Content Strategy. Having the right data to understand your audience is critical to your success, whether you’re trying to hold Webinars to highlight what your firm does for your target clients or using an online course platform to design a training course for your own company.
• Making the Most of Your Time: Analysts can spend less time fighting to arrange unruly data and more time receiving insights to assist them make informed decisions based on data that is easy to read and digest thanks to the Data Wrangling process.
• Data Visualization: Once you’ve wrangled the data, you can quickly export it to any Analytics Visual Platform of your choosing to begin summarizing, sorting, and analyzing it.

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Top Tools Used For Data Wrangling

• Talend
• Alteryx APA
• Altair Monarch
• Trifacta
• Datameer
• Microsoft Power Query
• Tableau Desktop

What is Data Cleaning?

Good data hygiene is so important for business. For starters, it’s good practice to keep on top of your data, ensuring that it’s accurate and up-to-date. However, data cleaning is also a vital part of the data analytics process. If your data has inconsistencies or errors, you can bet that your results will be flawed, too. And when you’re making business decisions based on those insights, it doesn’t take a genius to figure out what might go wrong!

Data cleaning is the process of finding and removing incorrect and inaccurate records from a record set or a data source and modifying or deleting this data. Data cleaning (sometimes also known as data cleansing or data wrangling) is an important early step in the data analytics process. Data cleaning is not just a case of removing erroneous data, although that’s often part of it. The majority of work goes into detecting rogue data and (wherever possible) correcting it.  Data cleaning can include activities such as removing typographical errors or validating and correcting values against a known list of entities. Overall, data cleaning helps to clean the data set and to provide data inconsistency to different data sets that were merged for various data sources.

Benefits Of Data Cleaning

• Staying organized
• Avoiding mistakes
• Improving productivity
• Avoiding unnecessary costs
• Improved mapping

Methods To Clean Data

Step1: Get rid of unwanted observations

Step 2: Fix structural errors

Step 3: Standardize your data

Step 4: Remove unwanted outliers

Step 5: Fix contradictory data errors

Step 6: Type conversion and syntax errors

Step 7: Deal with missing data

Step 8: Validate your dataset

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Tools Used For Data Cleaning

• Microsoft Excel
• Programming languages
• Visualizations
• Proprietary software

Difference Between Data Wrangling and Data Cleaning

As the methods might be similar in nature, data wrangling and data cleaning remain very different processes. Data cleaning focuses on removing inaccurate data from your data set whereas data wrangling focuses on transforming the data’s format, typically by converting “raw” data into another format more suitable for use. Data cleaning enhances the data’s accuracy and integrity while wrangling prepares the data structurally for modeling.

Data wrangling is the process of transforming and mapping data from one raw data form into another form with the intent of making it more appropriate and valuable for various tasks. In contrast, data cleaning is the process of detecting and removing corrupted or inaccurate records from a record set, table or database. So, this is the main difference between data wrangling and data cleaning.

Data cleaning focuses on removing erroneous data from your data set. In contrast, data-wrangling focuses on changing the data format by translating “raw” data into a more usable form. Import’s WDI assists in data cleansing by discovering, analysing, and enhancing the data quality. Data cleaning improves the correctness and consistency of the data, whereas data-wrangling prepares the data structurally for modeling.

Traditionally, data cleaning would be performed before any practices of data wrangling being applied. This indicates the two processes are complementary to one another rather than opposing methods. Data needs to be both wrangled and cleaned prior to modeling in order to maximize the value of insights.

Data wrangling and data cleaning are two processes that we can perform on data to obtain meaningful data. However, the main difference between data wrangling and data cleaning is that data wrangling is the process of converting and mapping data from one format to another format to use that data to perform analyzing while data cleaning is the process of eliminating the incorrect data or to modify them. In brief, it is possible to use data wrangling tools to perform data cleaning.