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 State of Data Science 2022 report revealed that data scientists spend about 38 per cent of their time data wrangling, a percentage that’s a sharp reduction from recent years, which placed the estimate at closer to 50 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 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 that take messy, complex raw data and transform it into more easily used data sets. 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. It may fall to non-data professionals to clean data before use if an organisation doesn’t have a dedicated data staff. 

Why does it matter?

Imagine if The Shard in London 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: 6 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 organisation wants to gain customer behaviour 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 disorganised, 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 standardising 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 your data is in a more usable state, figure out if there’s any information missing or if you need more data sets for your project.. If you do, 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 error free, consistent, and of sufficient quality. 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 organisation members for analysis purposes. You might 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.

• 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 organise systems to secure and store data. Additional tasks include backing up data, ensuring databases operate without errors, and keeping data secure.

Market outlook and salary info

The worldwide 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].

The job outlook for you will likely depend on the role you ultimately choose to pursue. As of August 2023, the average annual salaries for several common roles in the UK include: 

• Data scientist: £55,490 [4]

• Data warehouse specialist: £70,371 [5]

• Database architect: £72,265 [6]

• Database administrator: £41,510 [7]

Build your career in data 

If you’re considering a career that includes data wrangling, consider the Google Data Analytics Professional Certificate on Coursera. Learn from experts at Google as you develop job-ready skills from anywhere with an internet connection.