Data Wrangling: What Is It and Why Is It Important?

It’s not a secret that many companies use public data because it helps make various strategic decisions. However, getting valuable insights from this information can be a challenge. Usually, public data that companies collect is generally raw. This is where the data wrangling process comes in. 

This article discusses what data wrangling is, the key steps of data wrangling, and why it’s crucial for data-driven decision making.

What is data wrangling?

Data wrangling (also known as data preparation or munging) is the process of restructuring and cleaning the raw data into a more processed format. After data wrangling, data analysts can fasten the decision-making process. The exact methods differ depending on the size and format of the data, and the goal data specialists are trying to achieve.  
Data cleaning is often a manual but essential process.

According to Forbes, data specialists spend most of their time – around 80% on preparing and managing data for analysis.

Steps of data wrangling

Even if data wrangling usually requires a unique approach to ensure that the final dataset is readable and helpful, this process has the most common steps. To prepare the unclean public data for analysis, there are six basic steps to follow.

Discovery

This step involves simply understanding what the unclean data is all about. It’s a process of familiarizing with raw information – data analysts can conceptualize how they might use it. It’s a crucial part of the data wrangling process because data analysts can discover patterns in the data and issues that need to be solved before further steps, such as missing or incomplete values, etc.

Structuring

Most of the time, when raw public data is extracted from multiple data sources, it’s typically unusable because this information hasn’t a definite structure or schema, and it’s hard to work with it. Data analysts need to transform this data into a more readable format. Of course, to avoid this step, there are advanced data collection tools that gather structured data in the first place. For example, Oxylabs’ web scraping solutions provide structured data in JSON format.

Cleaning

The main goal of this step is to ensure there are no issues left, or, at least, data analysts deal with all the errors they find at the time. Unexpected problems can distort the final analysis results; this is why this step requires thoroughness and caution. Data cleaning includes simple actions such as deleting empty cells or rows, removing outliers, standardizing inputs, etc.

Enriching

The next step is to determine whether this data is enough for the goals set in the beginning. Simply put, it’s essential to understand if this information provides valuable insights. If data specialists decide not, they need to augment this data by incorporating values from other datasets. Of course, repeating the steps above for any new information is a must.

Validating

Validation requires programming knowledge because it’s usually achieved through various automated processes. The primary purpose of this step is to verify the consistency and the quality of data after processing.

Publishing

It’s the final step of the data wrangling process. Once the data is ready, data analysts can make it accessible to others for actual analysis. Usually, they prepare a written report for easier further usage.