What is Data Wrangling? Tools, Examples, and Steps

By | October 25, 2021
What is Data Wrangling

Today, every business relies heavily on data. Companies and organizations process data to get insights that assist in making better business decisions. Therefore, having the right set of data can make a major difference in the growth of a business.

Also, it is important to note that if the data collected is incorrect, it may lead to unnecessary business risks and significant downfalls.


The data collected is initially raw and cannot be utilized for business operations. Thus, it becomes necessary for companies and organizations to convert the raw data into meaningful insights, and this is where data-wrangling comes into the picture.

With data wrangling, businesses can process data to extract meaningful information. Also, businesses utilize actionable data to carry out data analysis and generate predictions.

This article will introduce you to data wrangling, some prominent data wrangling tools, typical examples of data wrangling, and the need for data wrangling. Also, you will get to know the different steps involved in the data wrangling process.

What Is Data Wrangling?

Data wrangling, also known as data munging or data cleaning, is a process of cleaning, organizing, and enriching the collected raw data into a more refined format. Business analysts and data scientists use this refined format of data to make accurate and wise business decisions.

The data wrangling or data munging process is quite time-intensive as it involves multiple iterative steps. Not every business project needs to have the same data wrangling process, and it entirely depends on the type of project, its goals, and the type of data leveraged.

Therefore, we can define data wrangling in three steps:

  1. Collecting data from various sources.
  2. Assembling data together.
  3. Cleaning data to find inaccuracies and missing elements.

There are two approaches to carry out data wrangling, namely manual and automated. The manual approach is suitable for small data sets. On the other hand, the automated approach is ideal for extensive data sets.

Large-scale organizations employ a data team or data scientists to perform data wrangling. However, in small startups, even non-data professionals carry out the data wrangling.

Having a solid knowledge of programming languages, such as Scala, PHP, and SQL is essential for performing data wrangling. In addition, an in-depth understanding of statistical languages, such as R or Python, is also mandatory.

Data Wrangling Tools

Several tools are available out there to carry out the data wrangling process. Some of the most typical data wrangling tools are:

  • Tabula: This data munging tool is ideal for all types of data.
  • Spreadsheets / Excel Power Query: It is a manual data wrangling tool.
  • Google DataPrep: It is a tool that explores, cleans, and makes the data ready in a usable format.
  • Data Wrangler: A tool that cleans and transforms data.
  • OpenRefine: It is an automated Java-based data wrangling tool that cleans data.

Data Wrangling Examples

Some typical examples of data wrangling are:

  • Collecting various data sources in one location for analysis.
  • Determining empty fields in the data set and either assigning them with values or discarding them.
  • Eliminating unwanted and irrelevant data.
  • Detecting critical outliers in data and either deleting them or describing the inconsistencies.

Moreover, companies use data wrangling to:

  • Determine corporate fraud.
  • Ensure accurate data modeling results.
  • Support data security.
  • Carry out customer behavior analysis.
  • Significantly reduce the time required for preparing data for the data analysis process.
  • Identify data trends and patterns.

Need for Data Wrangling

The only approach to enhance the value of raw data collected from heterogeneous sources is data wrangling. It helps in improving the data usability by transforming the raw data into the desired format. Moreover, it seamlessly assembles data from different sources, like databases, web services, spreadsheets, and files.

Businesses collect user or customer data from various sources and store it on various systems across different folders, files, spreadsheets, etc. Such storage of data may result in data redundancy, data inconsistency, or data loss.

Therefore, it is essential to store every business data in a centralized location for smooth and effective business operations and determine what is going on within the business. Data wrangling helps in piecing data stored across various systems. Also, it cleans the data, adds missing facts, and converts data into valuable business information.

Steps Involved in Data Wrangling

Data wrangling is an iterative process and involves six steps, as explained below:

1. Discovering

Discovering means making yourself familiar with the collected data. You need to look into the data and understand what it includes and what part of the data can be utilized.

To make it more simple, discovering is similar to looking into a refrigerator for the ingredients required to cook your meal. Let us look at another more precise example to understand the discovering step. Consider the customer data. Discovering involves what products did customers buy, location details, etc.

While you walk through the raw data, you can detect missing or incomplete elements and identify data trends and patterns.

2. Structuring

Raw data is not in a usable format because either it is incomplete or in an incompatible format. Therefore, structuring or organizing the raw data into the desired format is a pivotal step. Structure the raw data into the format that is compatible with the analytical method you choose for analyzing data.

3. Cleaning

After converting the data into the desired state, now it is time to clean that data. This step involves the elimination of errors that can mislead the further data analysis process. Some of the typical cleaning activities include deleting empty rows, assigning values to NULL fields, standardizing inputs, etc.

4. Enriching

Once you eliminate as many errors as possible from the data, you need to determine whether your data contains all the necessary information required for the project. If not, you need to enrich your data by collecting the required data or missing values from other data sets.

If you enrich or augment your data, it becomes essential to follow the above three steps for the augmented data.

5. Validating

As its name suggests, this step involves verifying the data and ensuring that the collected and processed data is of high quality with minimal inconsistencies. If there are any inconsistencies, you need to fix them. Otherwise, it is ready for the analysis process.

6. Publishing

The final step in data wrangling is publishing or making the validated data available to everyone involved in the data analysis process within an organization. Moreover, the format for publishing the validated data entirely depends on an organization’s goals and the type of data.

Here is a simple example that illustrates how data wrangling is essential and how it converts any raw data into easily readable and valuable insights.

Let us take customer data in the form of a table as shown below:

Name Phone Birth Date State
John, Brown 435-445-2345 June 12, 1978 Tx
George Williams +1-345-234-5678 12/04/1988 LA
Bartley, Smith (867)234-7689 1988-08-17 California
Jacob Alan 345-7688 16/09/1976 Texas
Brown, Oliver 2344657896 NULL Oh

Result Table

The below table is obtained after performing data wrangling on the above data set.

Name Phone Birth Date State
John Brown 435-445-2345 1978-06-12 Texas
George Williams 345-234-5678 1988-04-27 Los Angeles
Smith Bartley 867-234-7689 1988-08-17 California

When you apply data wrangling to the first table, you get a formatted result table. The result table involves all names in the ‘first-name last-name’ format, phone numbers in ‘area code-XXX-XXX’, birth date in ‘YYYY-MM-DD’, and the full name of the state.

However, the data of Jacob Alan is eliminated from the table, as the code area in the ‘Phone’ column is missing. Similarly, the birthdate field of Oliver Brown is NULL, hence it is discarded from the result table.


Data being a pivotal element of any business, it has to be highly accurate and authentic. Data wrangling helps companies convert non-resourceful and scattered data into actionable information, which assists in making smart business decisions. Moreover, this transformed valuable data allows data analysts to carry out the data analysis process smoothly.

Hopefully, this article provided you with all the information that you need to understand data-wrangling better. Also, feel free to share your thoughts in the comments section below.

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