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What is Data Wrangling?

Getting the correct data structured for analysis is becoming more important as the world of data grows quickly. Business users need access to relevant data and information to make business decisions. As a result, cleaning and preparing data for analytics is crucial. Data wrangling is the process of cleaning, formatting, and organizing data for further analysis.

What is Data Wrangling?

The term “data wrangling” refers to preparing data for analysis by cleaning it, standardizing it, and arranging it in a coherent framework.

Data wrangling gives data a logical structure to make it more usable. Data wrangling strategies provide data scientists with a means of identifying the most relevant data to mine for actionable insights since more than 80% of all available data is in its raw form.

Data Wrangling Process:

A data wrangling process, also known as a data munging process, involves rearranging, converting, and mapping data from one “raw” form into another to make it more useful and valuable for a range of downstream applications, including analytics.

Data wrangling is the process of cleaning, organizing, and translating raw data into the format requested by analysts for quick decision-making. It helps organizations deal with more complex data in less time, provide more accurate results, and make better choices. The precise procedures differ based on your data and the purpose you are attempting to accomplish. Enterprises are increasingly using data-wrangling technologies to prepare data for downstream analytics.

Uses of Data Wrangling:

  • Combining data from several sources
  • Methods for detecting data outliers and eliminating them so that the data may be analyzed more accurately
  • Finding and fixing data inconsistencies, such as blank cells in a spreadsheet, so that the data makes sense
  • Correction of mismatched labels and values
  • Combines many forms of data and their sources (like web services, databases, files, etc.)
  • Allow people to effortlessly analyze vast amounts of data and exchange data-flow methodologies.

Uses of Data Wrangling Tools for Business:

  • Expose instances of corporate fraud
  • Back-up data safety measures
  • Maintain reliable and consistent outcomes from data modelling
  • Make sure your company complies with the requirements in your field.
  • Analyze the Habits of Your Clientele
  • Lessen the amount of time spent on data preparation.
  • Figure discover data patterns quickly and capitalize on their commercial worth.

Data Wrangling Tools:

A variety of data-wrangling tools are available in the industry to prepare data for use in analytics and BI applications. Automated tools may be used for data wrangling, with the software allowing for the validation of data mappings and inspection of data samples during the transformation process. Data mapping mistakes may be rapidly discovered and fixed in this way.

Automated data cleansing tools are needed for companies dealing with massive amounts of data. The data team or data scientist is responsible for wrangling in the event of any manual data cleansing activities. But in smaller settings, non-data experts are accountable for data cleansing before using it.

Examples of Data Wrangling Tools:

  • One of the simplest manual data-wrangling tools is the spreadsheet/Excel Power Query combination.
  • OpenRefine is a programming-level automatic data-cleaning tool.
  • Tabula – It’s great for all kinds of data!
  • Google DataPrep is a data service that helps you discover, clean, and ready your data.

What are the skills required for Data Wrangling?

One of the fundamental abilities a data scientist needs is the ability to work with data. You must complete several actions to comprehend your data and prepare it for machine learning. An effective data wrangler should be skilled in combining data from several data sources, handling routine transformation challenges, and resolving data cleansing and quality concerns.

Data scientists must deeply understand their data and always seek to improve it. In actual situations, it is very seldom obtain perfect data. The business context of the data must thus be well understood to readily understand, purify, and turn it into an ingestible form of the data.

Leading IT businesses frequently seek the following skill sets in data science applicants.

  • Ability to conduct a variety of data transformations, such as merging, sorting, and aggregating
  • Using R, Python, Julia, and SQL data science computer languages on predetermined data sets
  • To reach logical conclusions using the underlying business environment

Steps for Data Wrangling Project:

Data wrangling refers to the process of organizing and cleaning up data. To guarantee that the final information is trustworthy and easily accessible, each data project needs a customized strategy. However, the strategy is often informed by several procedures. The most common strategy is as mentioned below:

  1. Data Discovery

“Discovery” refers to the procedures used to learn about data and envision its uses. The discovery phase often reveals data trends, patterns, and issues like missing or incomplete data. This step is critical since it will determine the remainder of the procedure. Before cooking, you check your fridge to see what you have.

  1. Data Structuring

Since raw data is sometimes either missing or improperly structured for its intended purpose, it cannot be used in its original form. In data structuring, unprocessed information is transformed into a form that can be used effectively. The analytical framework you choose to make sense of your data will determine its final shape.

  1. Data Cleaning

During data cleaning, any mistakes or outliers that might compromise the accuracy of your study are eliminated. The process of cleaning might include the elimination of duplicates, the elimination of outliers, or the standardization of inputs, among other things. By eliminating or reducing the number of mistakes in the data, you may improve the quality of your analysis.

  1. Data Enriching

After determining your data and how to utilize it, assess whether you have enough for the current project. It’s important to understand the different forms of data. You might also add values from other sources to enrich your data. Enrichment requires repeating the above steps with any extra data.

  1. Data Validation

Data validation is the process of ensuring that your data is both consistent and of sufficient quality. Validation is often accomplished using a variety of automated methods that need programming. You may identify problems that need to be resolved during validation or determine that your data is ready to be examined.

  1. Data Publishing

You may publish your data after it has undergone validation. This entails making it accessible for study to others inside your company. Your data and the company’s objectives will determine the format you employ to distribute the information, such as a paper report or an electronic file.


Wrangling the data is an essential step in the early phases of the data analytics process. Your data must be converted into a format that can be used before you can conduct a comprehensive study. And here is when data manipulation comes into play.

Given how much data is created almost every minute today, if more ways to automate the process of “wrangling” data aren’t found soon, it’s likely that a lot of the data the world creates will continue to sit there and do nothing for the business. Hence, the technical world is waiting for professional Data Wrangling experts with huge demand.



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