Data can inform many business decisions, from marketing to financing and blind hiring. So if you have flawed data, you will have flawed business strategies, too. To ensure that your data are usable, you need to organize them, clean them, and check their accuracy. In other words, you need data wrangling. But what is data wrangling, and how do you approach it? Let's find out.

Data Wrangling Definition

Data wrangling, or data munging, is simply the process of transforming raw data into a usable format. Raw data are text, images, code, or any other data you haven't yet processed and integrated. You can perform data wrangling manually, or you can automate it with a machine learning or neural network platform.

The job of data wranglers is to map, clean, organize, and enrich raw data so it's ready for publication and analysis. On average, data analysts spend 45% of their time on data wrangling. This makes it the most time-intensive part of an analyst's job. It's also the most important since inaccurate data lead to inaccurate analyses and poor business decisions.

What Does Data Wrangling Involve?

Data wrangling involves a variety of tasks, including:

• Combining data from different sources.
• Identifying and filling gaps in the data.
• Cleaning the data to remove outliers and inaccuracies.
• Deleting unnecessary data.
• Performing an exploratory analysis of the data.
• Validating the data.
  

 

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Why is Data Wrangling Important?

Raw business data come in a variety of formats and locations, so without data wrangling, it's difficult to compare them. Also, you can miss important insights if your data are incomplete, duplicated, or wrong. By integrating and cleaning the data, data wrangling gives you a full picture of your business and helps you make informed decisions.

Many business leaders overlook the importance of data wrangling as there's often little to show for it. So it's important to emphasize the benefits of data wrangling, such as:

• Ensuring datasets are complete and usable.
• Understanding complex datasets and their business implications.
• Getting the data ready for automation and machine learning tools.
• Ensuring you can easily compare and reuse data throughout the business.
• Guaranteeing the quality of the data and later analyses.

How to Approach Data Wrangling

Whether you approach data wrangling manually or with automated software, you should follow these six key steps:

1. Discovering

The first step in data wrangling is learning about your data. This helps you organize data for later analysis. To familiarize yourself with the data, you should perform an exploratory data analysis (EDA). EDA gives you data insights like a dataset's structure and any patterns and trends. It can also highlight incomplete or missing values.

In addition, you need to think about how you will use the data. Are you going to compare them? Are you testing for significance? Why you need the data can also determine how you structure it, so make sure you have a clear goal.

 

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2. Structuring

Raw data are usually unstructured, so before you can use them, you need to organize them. How you organize the data depends on what you discovered in step one - that is, the nature of the data and how you will use them. For instance, say you wanted to show the UX competitive advantages to C-suite executives. You would probably want the data in two columns: one of business performance with UX, and one without.

To structure data, you need to parse it. Parsing means taking out the data you need and deleting the data you don't. The end result is a spreadsheet that only contains relevant data.

3. Cleaning

On average, data analysts spend around one-quarter of their time cleaning data. Why? You need clean data for data mapping and analysis, so accuracy is essential. You can use programs like Python or Apache to clean data quickly and accurately. For instance, Apache Kudu can process and analyze large datasets, and it's easy to learn with an Apache Kudu tutorial. The process of cleaning data usually involves:

• Standardizing the data.
• Deleting duplicate or missing values.
• Removing outliers.
  

When you standardize data, you ensure all the labels and values are formatted the same way. For example, let's say some data are percentages and others are fractions. Converting the fractions into percentages would standardize the dataset.

4. Enriching

Data enrichment is an optional step since it depends on whether your dataset contains enough information. You will need to enrich data if:

• There are gaps in the dataset.
• You don't have enough data to achieve statistical significance.
  

Put simply, data enrichment involves adding information to your dataset. For instance, by adding an apps store review column to your free electronic signature software engagement dataset. You can enrich data by adding information from extra sources, or by combining two or more datasets. Bear in mind, though, that you'll need to repeat the previous steps for any extra data.

 

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5. Validating

Once your data are clean and rich, you need to make sure they are accurate. In other words, you need to ensure your data are:

• High quality.
• Consistent.
• Accurate.
• Secure.
• Authentic.
  

To validate your data, you can use automated software to check them against predetermined rules. If you find errors, you'll need to repeat the process until the data are error-free.

6. Publishing

The final step in data wrangling is publishing your dataset. This could be on your organization's system or making it available online for anyone to use. End-users may be other data analysts, scientists, engineers, or even content writers or students.

Once you publish your dataset, it may be used to write a business report for stakeholders. Or it may become part of complex data structures like data warehouses. It could even be used to create Free Videos or infographics. So, it's important the data are in an accessible format, especially if you plan to make the data open-access.

Data Wrangling Tools

Data wranglers use a variety of software, including Python, R, and Excel. Python is the most commonly used programming language, with 75% of analysts always or frequently using it. R comes in second with 27% of analysts using it regularly. There are also cloud-based software platforms like Databricks for data wrangling, analytics, and machine learning - all in one place.

Programming may seem daunting, but software like Python is relatively easy to learn. Plus, thanks to APIs, Python is compatible with a range of other software. For instance, the PySpark API allows Python to collaborate with Apache Spark. You can find a PySpark tutorial online, as well as tutorials for Python, R, and other programming languages.

 

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Data Wrangling Best Practices

There are several best practices for data wrangling you should be aware of, such as:

Know your audience

You need to think about who will use the data and what they will use it for. This will ensure you include the data they need to achieve their goal. For instance, say your marketing department wants to know if customers engage more with social media or email marketing. You can include this information in the dataset.

Choose the right data

"Quality over quantity" definitely applies to data! How much data you have isn't as important as the kind of data you have. After all, you could have a large amount of poor quality data. To choose the right data, you should:

• Avoid data with null or duplicate values.
• Use data from the original source.
• Combine data from several sources.
• Ensure the data are recent.

Understand the data

You need to understand the data so you know how it will help you achieve your goals, as well as the best way to analyze it. To understand your data, you should:

• Learn common database and file formats.
• Explore and visualize the data.
• Use data profiling tools to check the quality of your data.

Check your work

Once you finish data wrangling, you should re-evaluate your dataset to ensure it is high quality and efficiently organized. You should also write down anything you did differently for future reference.

Business Use-Cases of Data Wrangling

There are many ways businesses can use data wrangling, for example, you can:

• Detect fraud or suspicious activity.
• Track emails and other customer engagement data.
• Ensure your business conforms to industry standards.
• Analyze customer behavior.
• Predict business trends, like year-on-year growth.
• Unify your database.
• Improve the quality of your reports.
• Get a holistic view of your business and identify areas for improvement.

Takeaway

Data wrangling is a crucial skill for data analysts to have. It ensures the data are usable, understandable, and ready to analyze. It's also vital if you want to use the data for machine learning and other automated processes.

Good data wranglers must be able to piece together data from a variety of sources. They must also be able to clean them, standardize them, enrich them, and confirm their accuracy. After all, you rarely find raw data in a usable format. Most importantly, though, data wranglers need to understand the business context of the data. So, set clear goals - and get wrangling!

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ABOUT THE AUTHOR

Pohan Lin - Senior Web Marketing and Localizations Manager

 

A Senior Web Marketing and Localizations Manager at Databricks, Pohan Lin specialises in demonstrating the impact of massive scale data engineering, data analysis, acid transactions, and collaborative data science. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth, Pohan Lin is dedicated to innovating the way we use data in marketing.