What Is Data Wrangling and Why Does It Matter?

Data wrangling ensures data is reliable and complete before professionals analyse 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 2022” report revealed that data scientists spend about 37.75 per cent of their time data wrangling, a percentage that’s a sharp reduction from past surveys, which placed the estimate at closer to 80 per cent [1]. Still, industry experts would like to see the amount of time spent on data wrangling reduced, freeing up data scientists and other professionals to devote more time to creating insights.

If you’re considering 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. 

What is data wrangling?

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

It's an umbrella term that describes several processes designed to transform raw data 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 valuable insight needed to make decisions. 

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

Watch this video from the first course in IBM's Data Analyst Professional Certificate to learn more about data wrangling:

Why it matters?

Imagine building the Taj Mahal 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. You make raw data usable when you use data-wrangling tools and follow the steps. 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, 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 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 organisation wants to gain customer behaviour insight, you might sort customer data according to location, promotional codes, and purchases.

2. Data structuring

Once you've finished the first step, you might find raw data that could be more organised, complete, or misformatted for your purposes. That's where data structuring comes into play. This is the process in which you transform that raw data into a form 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 standardising inputs, deleting empty cells, removing outliers, and deleting blank rows. Ultimately, the goal is to ensure the data is as error-free as possible.

4. Enriching data

Once you've transformed your data into a more usable state, you must determine if you have all the data you need for the project. If you don't, you can enrich it by adding values from other data sets. 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 analysed. This step is typically completed using automated processes and requires some programming skills.

6. Publishing data

After 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 organisation members for analysis purposes. Use written reports or digital files, depending on the nature of the data and the organisation'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.

Read more: How to Become a Data Scientist

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

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

Read more: What Does a Data Architect Do? A Career Guide

Salary info

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

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

• Data scientist: ₹898,897 [4]

• Data warehouse developer: ₹468,690 [5]

• Database administrator: ₹523,115 [6]

Plan your path: Get started

Your career path will depend on your goals. For roles working in data wrangling, you might need a bachelor's degree in computer science, information technology, or a related field. Some employers look for candidates with a master's degree. However, you can also learn data wrangling through courses, bootcamps, and real-world experience. 

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 judgements aligned with business objectives.

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

On Coursera, you'll find Professional Certificates like Google Data Analytics offered by Google. This expert-led curriculum is ready to help you launch your career in data science. The courses included in this certificate are 100 per cent online and available to beginners who have yet to gain prior experience.