Data Wrangling is a very common term encountered by data professionals and data experts in their daily work and also by the students or learners, during the phase of them getting ready to be a data expert and start their careers in the field of data. Whether it is for data analysis, machine learning, data science, data research or any other related purposes. But, “What is data wrangling?”, “Data wrangling techniques?” and “Data wrangling benefits?”

In simple words, Data Wrangling is a process starting from:

1. Data understanding,
2. Data collection,
3. Data enrichment,
4. Data selection,
5. Data cleansing (Data cleaning),
6. Data Preparation (Structuring and Transformation),
7. Data Validation, and at last
8. Saving or Storing data (Data wrangling process ends here).

Now the data is ready for further usage like for analysis and research purposes or may be used directly for machine learning and data science purposes. This clean data is then used by companies and organizations to extract insights and patterns about their customers, users or for other important purposes.

Data wrangling is one of the most important, critical and time-consuming aspect of data analysis process. Further in this article, we will examine some of the most popular data-wrangling techniques, methods used by data analysts, data scientists and research analysts. Along, with data wrangling need and importance.

Table of Contents

What is Data Wrangling or Data Munging?

Data wrangling is also referred “Data Munging”. It is the process of collecting, cleaning, converting, preparing and getting raw data ready for analysis or other purposes.

This complete procedure comprises of a number of stages designed to enhance the data’s quality, increase its usability and accessibility, and prepare it for analysis.

Some of the activities performed during data wrangling process are:

Removing duplicate values, adding or removing missing values, fixing formatting issues, changing data types, creating data values, adding or removing rows and columns consisting of values, normalizing data, filtering data, combining or merging data from various sources (in-house or third-party sources), rearranging data, and enlarging dataset.

These are some of the tasks that are frequently performed in data wrangling process mainly by either data analysts or data scientists in an organization.

The end result of data wrangling is the creation of a clean, well-structured dataset that can be used for analysis to draw conclusions, spot trends, and guide best data driven decision-making.

In order to ensure that the findings of data analysis are accurate, trustworthy, and useful, data wrangling is an essential step in the data analysis or data science process. Without data wrangling, data analysis may be a time-consuming and error-prone process that produces results that could be deceptive or challenging to understand and would give bad results to a business.

Data Wrangling Techniques

Data Wrangling or Data Munging Techniques: Data analysts uses a range of methods and techniques to make sure the data is clean, correct, consistent, and prepared for analysis. The specific steps or techniques involved in data wrangling may vary depending on the nature of the data and the goals of the analysis and may sometimes depend on the structure of a company.

Here are the most common steps and techniques involved in the data wrangling process:

1. Understanding the Data

Data professionals or data analysts starts with the understanding of data, like what data they have to collect which eventually needed for analysis or further purposes (data tables, rows and columns inside them). What is the structure of data, variables present in the data, variables data types, data size and other critical factors.

2. Data Collection and Data Merging

After understanding the data, data analysts starts collecting data from different sources and tables. Data sources (data stored in the systems) can be located in the premises or may be the third party sources, where the data is stored like in data centers or with the data services providers.

Data professionals collects data from the multiple sources and integrate different files of data into a single dataset.

In some companies, this step may be performed by data analysts or data scientists (especially in smaller organizations), but in bigger companies this step is generally performed by database experts, database administrators (DBA) or other database professionals).

Data Merging is also done in this step, if required. Data merging is a process of combining multiple datasets into a single dataset. Common examples include joining datasets based on a common key or merging datasets with different levels of granularity.

3. Data Enrichment

After collecting the data, data analysts may add additional data, metadata or create new rows and columns of data, if required. This step is performed to provide additional context or information to the data, which may be helpful in extracting useful insights.

4. Data Selection or Data Filtration

Now, the next step is to select relevant data from the complete collected data to reduce the efforts put in subsequent steps for cleaning and preparing the data. For example, suppose only data of last 5 years is useful to form a strategy for new marketing campaign or only particular segment of customers have to be studied for the launch of a new product.

Then, in that case only relevant data related to that time period or customer segments need to be prepared, studied and should be analyzed to make next strategy. This step helps in reducing the time efforts and costs of a company.

5. Data Cleansing (Data Cleaning)

Data cleansing or data cleaning is one of the most crucial step of a data wrangling process. How, well the data is clean forms the base of good quality data or good data quality.

Now, data experts work on identifying and correcting errors, issues or inconsistencies in the data. Which includes but not limited to, filling in missing values, removing duplicates, fixing formatting issues, treating outliers, creating data rows and data columns, arranging the data, correcting data types, converting date time fields etc.

6. Data Preparation (Structuring and Transformation)

Data preparation is the final step performed comprehensively on the cleaned data. This step is very important because most of the times data needs to be prepared, adjusted or converted for it to become suitable for further analysis purposes.

This step may include converting data from one format to another, standardizing data, converting data formats, data reshaping (pivot or unpivot data), creating new features like continuous or categorical variables, label encoding, one-hot encoding, re-arranging data or optimizing data. Which helps in aligning data with the requirements of further usage and different teams.

7. Data Validation

Now the next step is, checking, validating and ensuring that the prepared data is correct. In this step, prepared data is validated for its accuracy and completeness. Checking that the data variables have correct relationships and dependencies, keys (primary key, foreign keys) are setup correctly, data quality is matching required standards and more.

8. Saving or Storing data

After executing all above steps correctly, final step of data wrangling process is to save, store or export the cleaned, prepared and validated dataset in the right place and right format (e.g., CSV, Excel, Database (SQL, MySql, MonogoDB, PostgreSQL, MS Access etc.), or JSON).

The particular data wrangling methods or techniques chosen and employed may depend on the type of data being utilized, the objectives of the research (purpose) and the size of the organization. 

Need of Data wrangling

Data wrangling strategies are required and essential during the data analysis and data science process for a number of reasons. It is because your data should be clean and of high quality before you do any analysis on it. To understand it better, here are some of the most significant reasons why data wrangling is required:

1. Raw data is frequently unorganized and incoherent: Raw data is harmful for any organisation for any further conclusion. It can be challenging to deal with, since it frequently has missing values, duplicates, formatting issues, and inconsistent information. It is often referred to as bad data or poor data. By identifying and fixing these problems, data wrangling techniques make sure the data is reliable and consistent.
2. Variables Formats: Data collected from different data sources have distinct formats and variables may have different data types. This create issues at the time of analysis and often results in errors and wrong predictions, when apply data science techniques or algorithms on bad data. Because each source often has its own structure and format. Techniques for “data wrangling” assist in cleansing, transforming and preparing the data into a standardized format that facilitates analysis.
3. Data quality and accuracy: Data analysis accuracy is impacted by data quality and directly depends on it. Incorrectly cleansed and prepared data might produce erroneous conclusions. By enhancing the quality of the data, data wrangling techniques help to ensure the accuracy, data quality and dependability on the analysis results.
4. Working with large datasets (big data) can be cumbersome and challenging: Data wrangling techniques helps in simplifying tedious activities, when working with huge datasets and make it simpler to spot patterns and trends in the data.
5. Data wrangling can save time and money: By properly executing data wrangling methods on your data and further automating data cleaning and data preparation tasks, data wrangling can save time and reduce the cost of data analysis.

Overall, data wrangling techniques are essential for preparing quality data for analysis. By ensuring clean data and good quality data, extracting meaningful insights from the data becomes much easier and accurate. Without these techniques, the data analysis process would be much more difficult and less reliable.

Importance and Benefits of Data Wrangling

Data wrangling is a crucial step in the data analysis process because it helps to ensure that the data is clean, accurate, consistent, and ready for analysis. Here are some of the key reasons why data wrangling is important and data wrangling benefits:

1. Data quality: Collected data which is raw data is frequently disorganized, contradictory, and insufficient for any further analysis. Data cleaning and transformation techniques help to identify and correct errors, inconsistencies, and missing values, resulting in higher-quality data for analysis. Thus, we can say that data wrangling helps to enhance the quality of the data.
2. Data usability: Raw data can’t be used and analyzed further, because it often results in bad insights and findings. While, clean and well-organised data is significantly simpler to use and analyze. Data wrangling can make it simpler for data analysts and data scientists to extract correct insights and draw conclusions from the data. Data wrangling process helps data arranged into a more usable structure.
3. Saving time and money: By correctly executing data wrangling techniques on the raw or bad data helps employees to save their and other team members time. Which eventually results in saving the company’s cost. And further by automating time-consuming operations like data wrangling, company’s can lower the need for manual data cleansing and preparation. This can save time and reduce the cost of data analysis processes incurred by the organization.
4. Greater accuracy: By identifying and correcting errors or inconsistencies at the right time, data wrangling techniques improve the accuracy and reliability of data analysis.
5. Better decision-making: When data has been correctly and adequately wrangled, it help companies in making better-informed decisions based on data-driven insights by ensuring that the data is accurate, correct and consistent. With better quality data analysts can bring out more reliable insights and thus data-driven decision making can be more effective and produce good results.
6. Improved data analysis: Data wrangling makes it simpler for analysts to carry out more complex analyses and derive deeper insights from the data after putting the data into a more clean and usable structure.
7. Better collaboration: Data wrangling techniques can make it easier for different teams and individuals to work together on data-related projects and collaborate seamlessly.
8. Better data security: By reducing the risk of errors and inconsistencies in data, data wrangling techniques can enhance data security and privacy. Cleaned and well prepared data enhance confidence among the team members to execute data without any worries, without compromising privacy and security of the client and customers data.

Data Wrangling Tools

Here is the list of most popular and best tools used for data wrangling in different organizations:

1. MS Excel

It is a tool offered by Microsoft and is used widely for data wrangling and other purposes.

2. Python (Numpy and Pandas libraries)

Python is a programming language. It’s libraries “Numpy” and “Pandas” are widely used for numerical computing, data manipulation and analysis purposes.

3. R Programming Language

R is also a programming language and package, useful for data wrangling purposes like cleaning, filtering, transforming, and summarizing data tasks.

4. Tableau

Tableau is a tool which is basically used for cleaning and preparing visually. It is one of the best tool for visual representation of data and data charts.

5. OpenRefine

OpenRefine is a free and open source tool used widely for data cleaning, data transformation and data wrangling purposes.

Conclusion

After understanding the basics and know-how of “What is Data Wrangling”, data wrangling techniques, it’s benefits. This is evident that data wrangling techniques provide numerous benefits to the organizations of all sizes and across all industries.

By improving the quality, accuracy, and efficiency of data and thus data analysis, data wrangling can help organizations make better decisions, improve operations, and gain a competitive advantage.

Executing data wrangling methods and techniques are crucial for any organization, whether small or large. As, it transforms raw data into something useful that can be correctly analyzed and thus can be used by different teams without any concern.

Working with raw data presents students, learners, data analysts and data scientists with a variety of difficulties, such as missing information, duplicate data, formatting mistakes, inconsistent structure and discrepancies. Data analysts can clean, transform, and prepare data by following the data wrangling steps and make data more usable and available for analysis.

Thus it is always good and advisable, that every company should use a well defined data wrangling process and approach in their ecosystem and apply it comprehensively.