By 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.
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The specific steps in data wrangling depend on a project’s unique resources and goals. However, some basic steps are typical of the process.
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.
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.
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.
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.
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.
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.
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Many tools can be used in the data-wrangling process, depending on the resources, goals and preferred approach. These include:
In some cases, a professional’s access to data-wrangling tools may depend on the policy or resources of the organization they work for.
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:
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.
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Data wrangling is increasingly useful in machine learning and other computer science professions.
An information systems manager is a professional who manages and oversees an information technology (IT) department. IT departments play a key role in managing a company’s digital assets; ensuring that data is relatively simple, accessible and well-structured can significantly benefit asset management.
A data analyst is a type of data scientist who specializes in the analysis phase of data compilation and review. To properly analyze data, data analysts need high-quality data sets and strong data analysis skills.
A business intelligence analyst is a type of data analyst who specializes in using data insights to provide relevant intelligence reports for an organization. This information might include such insights as consumer activity and financial trends.
Database architects develop and manage databases, often alongside other professionals. A key part of data wrangling is structuring data to be efficiently stored, organized, processed and accessed, all of which make those skills highly applicable to the duties of a database architect.
Research science describes a broad range of professional pursuits to test hypotheses in a controlled environment. This can be done for a variety of purposes. As for information research scientists, they may conduct experiments such as algorithm testing and end-user testing. The use of new or existing data often supports research efforts by demonstrating the consistency of results under identical or similar conditions.
If any of these careers interest you, learn more about what educational requirements you may need to meet to enter these fields.
If you’re interested in learning fundamental skills involving data, University of Phoenix offers online degrees in data science, information technology and computer science.
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