What Is Data Wrangling? Definition, Steps, and Why It Matters

Data wrangling is the process of converting raw data into a usable form. It may also be called data munging or data remediation.

You'll typically go through the data wrangling process prior to conducting any data analysis in order to ensure your data is reliable and complete. This way, you can be confident that the insights you draw are accurate and valuable.

In this article, we'll discuss what data wrangling is, go through the basic steps, and discuss why it's an important piece of the data analysis process.

Build job-ready skills with a Coursera Plus subscription

Start 7-day free trial

• Get access to 7,000+ learning programs from world-class universities and companies, including Google, Yale, Salesforce, and more
• Try different courses and find your best fit at no additional cost
• Earn certificates for learning programs you complete
• A subscription price of $59/month, cancel anytime

Start 7-day free trial

What is data wrangling? 

Data wrangling describes a series of processes designed to explore, transform, and validate raw datasets from their messy and complex forms into high-quality data. You can use your wrangled data to produce valuable insights and guide business decisions. 

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

Data wrangling steps 

There are four broad steps in the munging process:

1. Discovery

2. Transformation

3. Validation

4. Publishing

Let's take a closer look at each step.

1. Discovery

In the discovery stage, you'll essentially prepare yourself for rest of the process. Here, you'll think about the questions you want to answer and the type of data you'll need in order to answer them. You'll also locate the data you plan to use and examine its current form in order to figure out how you'll clean, structure, and organize your data in the following stages.

2. Transformation

During the transformation stage, you'll act on the plan you developed during the discovery stage. This piece of the process can be broken down into four components: structuring, normalizing and denormalizing, cleaning, and enriching.

Data structuring

When you structure data, you make sure that your various datasets are in compatible formats. This way, when you combine or merge data, it's in a form that's appropriate for the analytical model you want to use to interpret the data.

Normalizing and denormalizing data

Data normalization involves organizing your data into a coherent database and getting rid of irrelevant or repetitive data. Denormalization involves combining multiple tables or relational databases, making the analysis process quicker. Keep your analysis goal and business users in mind as you think about normalization and denormalization.

Data cleaning

During the cleaning process, you remove errors that might distort or damage the accuracy of your analysis. This includes tasks like standardizing inputs, deleting duplicate values or empty cells, removing outliers, fixing inaccuracies, and addressing biases. Ultimately, the goal is to make sure the data is as error-free as possible.

Enriching data

Once you've transformed your data into a more usable form, consider whether you have all the data you need for your analysis. If you don't, you can enrich it by adding values from other datasets. You also may want to add metadata to your database at this point.

3. Validation

During the validation step, you essentially check the work you did during the transformation stage, verifying that your data is consistent, of sufficient quality, and secure. This step may be completed using automated processes and can require some programming skills.

4. Publishing

After you've finished validating your data, you're ready to publish it. When you publish data, you'll put it into whatever file format you prefer for sharing with other team members for downstream analysis purposes.

Importance of data wrangling

Data wrangling prepares your data for the data mining process, which is the stage of analysis when you look for patterns or relationships in your dataset that can guide actionable insights.

Your data analysis can only be as good as the data itself. If you analyze bad data, it's likely that you'll draw ill-informed conclusions and won't be able to make reliable, data-informed decisions.

With wrangled data, you can feel more confident in the conclusions you draw from your data. You'll get results much faster, with less chance of errors or missed opportunities.

Keep learning

Explore more data analysis processes with industry leaders on Coursera. With both IBM's Data Analyst Professional Certificate and Google's Data Analytics Professional Certificate, you can build key skills and practice using data analysis tools. Sign up for your seven-day, all-access trial and start learning today.