What Is Data Wrangling and Why Does It Matter?

Written by Coursera • Updated on

Data wrangling is useful for a variety of roles ranging from data scientists to database administrators. Learn more here.

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Data wrangling ensures data is reliable and complete before professionals analyze it and use it to create insights. Thanks to this process, those insights are based on accurate, high-quality data.

Anaconda's “The State of Data Science 2020” report revealed that data scientists spend about 45 percent of their time data wrangling, a percentage that’s a sharp reduction from past surveys, which placed the estimate at closer to 80 percent [1]. Still, industry experts would like to see the amount of time spent on data wrangling process reduced, freeing up data scientists and other professionals to devote more time to creating insights.

If you’re thinking of going into a career in data, at some point, you’ll likely have to deal with data wrangling in some capacity. We’ve put this guide together to help you better understand what it is, why it matters, and how you might use it going forward. 

What is data wrangling? 

Data wrangling also goes by a few other names, including data cleaning and remediation.

It's an umbrella term that describes several processes, all designed to take raw data and transform it from messy, complex data sets into more easily used formats. When you engage in data wrangling, you find and transform data so you can use it to answer a question or produce a valuable insight needed to make decisions. 

Professionals conduct data wrangling in one of two ways: manually or automated. In businesses with a data team, data scientists and other team members usually head up the data wrangling process. In smaller organizations, it may fall to non-data professionals to clean data before use.

Why does it matter?

Imagine if the Empire State Building in New York City was built on a shoddy foundation or if the builder who constructed your home slapped it together without paying meticulous detail to the quality of the foundation and the building supplies. Data wrangling works similarly as a solid foundation for research and analytics.

Once the process is complete, you'll get results much faster, with less chance of errors or missed opportunities. When you use data wrangling tools and follow the steps, you make raw data usable. Other benefits include:

  • Data wrangling enables you to gather data from multiple sources into a central spot.

  • Cleaning and converting data into a standard format enables you to perform cross-data set analytics.

  • Data wrangling prepares data by removing flawed and missing elements, readying it for data mining, and empowering businesses to make concrete, data-driven decisions.

Explore the process: Six common data wrangling steps 

If you work with data, then you’ll likely also work with several tools to help you easily navigate the data wrangling process. Some popular tools include Tabula, DataWrangler, Pandas, and Python. Each project might require you to take a slightly different approach and may present unique challenges throughout the process.

Harvard Business School Online identifies six common processes used to inform your approach to data wrangling: discovery, structuring, cleaning, enriching, validating, and publishing [2]. 

1. Data discovery

The very first step helps you make sense of the data you're working with. You'll also need to keep the primary goal of the data analysis during this step. For example, if your organization wants to gain customer behavior insight, you might take customer data and sort it according to location, promotional codes, and purchases.

2. Data structuring

Once you've finished the first step, you might find yourself with raw data that's disorganized, incomplete, or misformatted for your purposes. That's where data structuring comes into play. This is the process in which you take that raw data and transform it into a form that's appropriate for the analytical model you want to use to interpret the data.

3. Data cleaning

During the data cleaning step, you remove data errors that might distort or damage the value of your analysis. This includes tasks like standardizing inputs, deleting empty cells, removing outliers, and deleting empty rows. Ultimately, the goal is to make sure the data is as error-free as possible.

4. Enriching data

Once you've transformed your data into a more usable state, you must figure out if you have all the data you need for the project. If you don't, you can enrich it by adding values from other datasets. And if you do so, you might have to repeat steps one through three for that new data.

5. Validating data

When you work on data validation, you verify that your data is consistent and of sufficient quality. During this step, you might find some issues you need to address or that the data is ready to be analyzed. This step is typically completed using automated processes and requires some programming skills.

6. Publishing data

After you've finished validating your data, you're ready to publish it. In this step, you'll put it into whatever format you prefer for sharing with other organization members for analysis purposes. You might use written reports or digital files, depending on the nature of the data and the organization's overarching goals.

Discover potential career paths

Learning about data wrangling can open the door to several career paths. Some of the roles you might consider pursuing include:

  • Data scientist: In this role, you might collect data, transfer it into new analysis-friendly formats, and build tools to collect data. You might also create frameworks to collect data and create presentations and reports to distribute according to business objectives.

  • Data warehouse specialist: In this role, you can be a liaison between data analysts, programmers, and data architects. You might actively work to make sure data is managed correctly, in addition to manipulating and combining data and performing tech-related administration tasks.

  • Database administrator or architect: In this role, you can create and organize systems to secure and store data. Additional tasks include backing up data, ensuring databases operate without errors, and keeping data secure.

Job outlook and salary info

The data wrangling market itself is predicted to remain strong. According to Mordor Intelligence, the market could reach $2.28 billion by 2026, up from $1.31 billion in 2020 [3].

The job outlook for you will likely depend on the role you ultimately choose to pursue. The average annual salary and projected outlook for several common roles include: 

  • Data scientist roles are expected to grow at a rate of 22 percent between 2020 and 2030, with a median salary of $131,490 according to the US Bureau of Labor Statistics in 2021 (BLS) [4].

  • Data warehouse specialist roles are expected to grow at a rate of 9 percent from 2018 to 2028, with an average annual salary of $83,671, according to Zippia [5].

  • Database administrator or architect roles are expected to grow at a rate of 8 percent from 2020 to 2030, with a median salary of $98,860 in 2021, according to BLS [4].

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Plan your path: Get started 

Your career path will depend on your goals. For most roles working in data wrangling, you might need a bachelor's degree in computer science, information technology, or a related field at a minimum. If you want to become a data scientist, many companies look for candidates with a master's degree, as noted by Indeed [6].

Coursera offers several data science master's degree programs, including Master of Science in Data Science from the University of Colorado Boulder and Master of Applied Data Science from the University of Michigan.

Employers look for candidates with a solid foundation in the business context of data with skills such as:

  • Ability to perform data transformations, including aggregating and merging

  • Proficiency in data science programming languages, including Julia, SQL, Python, and R

  • Sharp critical thinking skills and the ability to make logical judgments aligned to business objectives

If you’re considering a career that includes data wrangling, you have educational options. You could pursue a traditional degree or an online curriculum. Data Bootcamps can also help you develop the skills you might need to work with data. Additionally, you might consider taking online courses to get your feet wet in specific subjects to see if this is a good match for you. 

At Coursera, you'll find courses like Introduction to Data Analytics from IBM and Professional Certificates like Google Data Analytics from Google. These expert-led curriculums are ready to help you launch your career in data science.

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Article sources

1. Anaconda. “2020 State of Data Science, https://know.anaconda.com/rs/387-XNW-688/images/Anaconda-SODS-Report-2020-Final.pdf." Accessed May 13, 2022.

2. Harvard Business Insights Blog. “Data Wrangling: What It Is & Why It's Important, https://online.hbs.edu/blog/post/data-wrangling." Accessed May 13, 2022.

3. Modor Intelligence. “Data Wrangling Market: 2022 - 27: Industry Share, Size, Growth,  https://www.mordorintelligence.com/industry-reports/data-wrangling-market." Accessed May 13, 2022.

4. US Bureau of Labor Statistics. “Computer and Information Research Scientists: Occupational Outlook Handbook, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm." Accessed May 13, 2022.

5. Zippia. “Data Warehouse Specialist, https://www.zippia.com/data-warehouse-specialist-jobs/." Accessed May 13, 2022.

6. Indeed. “Data Scientist Job Description [Updated for 2022],  https://www.indeed.com/hire/job-description/data-scientist." Accessed May 13, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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