What is Data Wrangling?

Data wrangling (or data munging) involves cleaning and structuring data and then transforming it into the correct format. This process results in better quality data for decision-making and business intelligence. Data wrangling typically takes place before big data analytics

You can manually execute data wrangling or use digital tools to facilitate the process. 

Table of Contents

  • What are the Benefits of Data Wrangling?
  • Why You Need a Data Wrangling Strategy
  • What are Data Wrangling Challenges?
  • What is Data Wrangling vs. ETL?
  • How integrate.io Can Help With Data Wrangling

What are the Benefits of Data Wrangling?

Data wrangling is one of the most crucial components of data transformation. Organizations typically have large data sets they want to analyze for better business intelligence. However, sometimes this data is in the incorrect format, rendering it useless for data analytics. Other times, data contains errors or inconsistencies and organizations need to structure it correctly before analysis. 

Why You Need a Data Wrangling Strategy

Before you clean, structure, and transform data into the correct format, you need a data wrangling strategy. That's because data wrangling requires several complex processes, and you can degrade or even lose data if you carry out the wrong steps.

You also need to comply with data governance principles like GDPR and HIPAA during data wrangling. These principles stipulate significant financial penalties for organizations that don't manage data correctly. (For example, cleaning and structuring excessive customer data that no longer serves a purpose.)

Before data wrangling, create a policy:

  • Decide what data to clean, structure, or transform into a different format.
  • Determine the quality of the original data.
  • Choose the right tools for data wrangling.
  • Choose the best data wrangling method.
  • Ensure compliance.
  • Plan for any data wrangling challenges (see below).

What are Data Wrangling Challenges?

Data wrangling involves several challenges for organizations.


Manual data wrangling requires a lot of time that organizations can better spend on data analysis. Use digital tools to automate many processes associated with data wrangling, such as cleaning data from legacy systems and transforming it into useable formats for analytics. Most successful data-driven organizations no longer rely on manual data wrangling methods.

Data Loss

Cleaning, structuring, and transforming data into a new format can cause data loss or data degradation. This problem happens when organizations don't back up their original data and errors or downtime occur during the data wrangling process.

Data Corruption

Data can also become corrupted during data wrangling. This problem occurs when a user applies the wrong rules or validation processes to the original data when cleaning, structuring, or transforming it. Backing up your original data can prevent corruption from happening.

Data Maintenance

Data wrangling is often an ongoing process. Organizations might need to clean or transform data continuously as it moves between locations for analytics

What is Data Wrangling vs. ETL?

Data wrangling and Extract, Transform, Load (ETL) might sound similar. That's because both processes cleanse, structure, and transform data for analytics. However, data wrangling and ETL rely on different methods and serve unique purposes. 

Data wrangling, for example, typically handles "raw" or unstructured data that might be messy or complex in its original form. ETL manages structured, relational data sets (and sometimes semi-structured data sets). 

Identify your organization's data integration needs and choose the best method for analytics and business intelligence.

How integrate.io Can Help With Data Wrangling

Whether you decide to clean, structure, or transform data via data wrangling or ETL, integrate.io can help. This all-in-one data management solution lets you build complex data pipelines for moving data from its original source, transforming it into usable formats, and loading it to new locations for data analysis and business intelligence

integrate.io requires little or no code, so you can integrate data from one location to another even if you lack data engineering experience. That means you can improve time resources and focus on analytics. integrate.io automates data cleansing, structuring, and transformation, which reduces the complexities associated with data integration, such as data loss, degradation, corruption, and failure to comply with data governance principles. Other integrate.io features include a simple pricing structure, a point-and-click user interface, world-class customer service, and pre-built connectors for enhanced data migration.  

integrate.io serves all your data integration requirements with its all-in-one no-code solution. Discover how integrate.io can help your data-driven organization by scheduling a personalized 7-day demo today.

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Glossary of Terms

A guide to the nomenclature of data integration technology.