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What Are Data Wrangling Techniques?

18 Apr
Data sorting from to a more easily used structural format

Every day, over 338 million terabytes of data are created as people engage with the internet for business and personal use.1 Data analysts and other data professionals can leverage this data to gather business insights and drive business decisions. However, data-driven insights are only as good as the data behind them.

Unfortunately, most of the data that’s created is in an unstructured format. This raw data is often incomplete, inconsistent and filled with errors. Data-wrangling techniques find and correct defects in data so you can use that data to get accurate, consistent, and reliable results.

What Is Data Wrangling?

Data wrangling is the process of transforming raw data into a more easily used structural format. It’s also called data cleaning, data remediation, data structuring or data munging. There are multiple steps involved in data wrangling, and the exact process you use will depend on the type of data you’re wrangling and how you’ll use it.2

The Importance of Data Wrangling

Before you can use data, it must be collected from its unstructured format and checked for errors to ensure its quality. This process of extraction and cleaning complex data is done through data-wrangling techniques. There are many benefits to data wrangling.3

Easier Analysis

After cleaning, your data set is easier to compare and analyze. You can also have more confidence in your analysis if you know your data is accurate and complete. Once wrangled, data collections are easier to read and compare than multiple, jumbled formats are.

Increased Usability

After processing raw data, you can use it for multiple purposes, including analysis and machine learning, which is a type of programming that mimics human intelligence. As artificial intelligence (AI) continues to advance, the benefits of data wrangling will grow in importance. The success or failure of machine learning projects depends on the quality of the data you’re using.

Better Results

Finally, your results will be more accurate if you’ve cleaned your data. Errors in your data will result in errors in your evaluation. One reason data is so valuable is that it gives us information about patterns of human behavior and even our larger social structure. If your data isn’t accurate, your data insights and conclusions won’t be either.

The Data Wrangling Process

Although the exact steps may be different depending on your data and the purpose of converting data for your project, there are some general processes you’ll use to wrangle data.4

Data Discovery

The first step in data management and wrangling is thinking critically about your data and what you hope to learn from it. How you intend to use your data will inform how you handle it. When you understand your data and your goals, you can begin gathering your data.

Your data will likely be in different formats, such as CSV, JSON, or XML, which are various methods that computer programs use to store data. This is particularly true if you’re collecting data flows from various sources such as application programming interfaces, databases, or files.


After you’ve gathered your data, you need to organize it into a structured format. When it’s first gathered, raw data is usually incomplete or in an incompatible format for its intended use. You’ll organize your data based on data quality and the analytical model you plan to use to interpret it.


Once your data is organized, you can clean it by removing errors that can make it less accurate or valuable. You may need to correct values, remove outliers, or get rid of duplicate data or data sources. This cleaning of data can be a complex and time-consuming process. Cleaning unstructured data is much more difficult than cleaning data harvested from a database. The goal of cleaning data is to eliminate any errors that can negatively affect your final analysis.


Data enrichment is the process of adding data to your dataset. You may not need to do this, but if your dataset is missing valuable information or you don’t have enough data to complete your project, augmenting your data can increase its value.

If you decide to enrich your data, you’ll need to perform the data-wrangling steps on the new data as well. There are several methods, tools and techniques for enriching data, including:

  • Adding missing data points by using external sources to estimate or infer the missing values
  • Standardizing and normalizing to a common format, such as converting all temperature readings to the same scale, to make it easily comparable
  • Geocoding to add geographic coordinates to enable spatial analysis, such as converting an address to its latitudinal and longitudinal coordinates
  • Data appending to add new data elements to existing data, such as behavioral or demographic information


Data validation verifies that your data is secure, consistent, and of high quality. During validation, there are several methods you can use, including:

  • Format validation to ensure the data follows the correct format, such as a date or phone number
  • Range validation to ensure data falls within the expected range, such as making sure someone’s age isn’t recorded as 367
  • Completeness validation to ensure there are no missing data points
  • Consistency validation to ensure the data is consistent throughout the system, such as verifying a customer’s name is spelled the same in all records
  • Cross-field validation to check that the relationship between fields is correct, such as verifying a password and its confirmation match


Once your data has been validated, it’s ready for publication. Publishing data makes it available for research and analysis by others, either within your company or by the public. You may need to supply notes or documentation of your data wrangling tools and process.

Data wrangling is an iterative process, so you’ll need to revisit your data and make adjustments to each phase of data analysis as needed.5

Become a Leader in Business Analytics

Data science and analytics are invaluable in today’s business environment. William & Mary’s Online Master of Science in Business Analytics (MSBA) program will prepare you to become a data scientist and/or business leader who can generate insights from raw data to drive corporate strategies and improve profitability.

You’ll learn the skills you need to gain a competitive advantage and advance your career. Our online MSBA is a dynamic, hands-on program with real-world applicability. When you graduate, you’ll have a deep understanding of how to apply your new skills.

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