What is data wrangling and how is it used?

At a glance

• Data wrangling is the process of removing errors from raw data and preparing the data for analysis.
• Data wrangling follows these steps: discovery, structuring, cleaning, enriching, validating and publishing.
• Benefits include improving data usability, building data flow, preventing resource waste and simplifying data processing.
• To learn more about data management, explore data science and IT online programs at University of Phoenix.

Data wrangling — also called data cleaning, data remediation or data munging — is the process of removing errors from raw data and preparing the data for analysis. This preparation includes organizing the raw data, presenting it in a more accessible format and adding important context and peripheral data.

A variety of professionals, including data scientists and data engineers, can benefit from developing this skill. Sometimes, it’s even required. While it’s simple in concept, data wrangling can be a relatively complex process, particularly when the professional has to manage large amounts of data or when the data sets themselves are highly complex.

What steps are involved in data wrangling?

The specific steps in data wrangling depend on a project’s unique resources and goals. However, some basic steps are typical of the process.

Discovery

Data discovery identifies target information, collects relevant data and conducts early evaluations of the data. This evaluation typically involves finding more obvious patterns — trends and outliers — in the data and extracting the most useful data sets. Professionals conduct discovery using one or multiple tools or databases, depending on the scope and complexity of the analysis.

Structuring

Data structuring organizes data in preparation for storage. The goal is to make the data easier to store, process and access, as well as improve users’ ability to update the data. Professionals might use many types of data structures, including arrays, linked lists, stacks, queues and trees.

Cleaning

Data cleaning reviews the data for mistakes or other issues and then corrects or removes the problems. The errors and issues might include incorrectly transcribed data, corrupted data, incorrectly formatted data, incomplete data or misfiled data. The professional overseeing this process may also remove data that is irrelevant to the project.

Enriching

Data enrichment supplements target data sets with other relevant data sets from a different source. Often, this involves combining complementary data sets from internal sources and third-party sources. However, the overall goal is to supplement the primary data set with further information for greater context, breadth and accuracy.

Validating

Data validation is essentially a comprehensive review process. The professional overseeing the project ensures that all previous steps have been completed correctly and no mistakes are present. They also further review the quality of the data.

Publishing

Data publishing is the process of organizing data for review and use and publishing the information through appropriate platforms in a manner that is accessible to the target audience. A professional’s goals for the project will determine the target audience. For example, data for a company’s internal use might be reserved for internal access, whereas data compiled for a research hypothesis might be published publicly for replication and review among peers. 

What are some common data-wrangling tools?

Many tools can be used in the data-wrangling process, depending on the resources, goals and preferred approach. These include:

• Spreadsheet software
• Data integration platforms
• Data management platforms
• Cloud applications
• Enterprise application integration platforms
• Open-source web crawlers
• Web scraping

In some cases, a professional’s access to data-wrangling tools may depend on the policy or resources of the organization they work for.

What are some benefits of data wrangling?

Data mining and data analysis are increasingly common and valuable processes for modern businesses. As such, data wrangling is also becoming increasingly valuable. The specific benefits of data wrangling include:

• Improving data usability
• Building data flow
• Integrating information
• Reducing risk
• Increasing accessibility
• Increasing visibility
• Preventing resource waste
• Simplifying data processing
• Minimizing storage use
• Optimizing cybersecurity

In general, data wrangling can help a target audience better interpret and use the data. For example, a clear understanding of relevant data can help a business make more timely, informed decisions.