6 Steps of Data Wrangling and How Automation Helps in 2024

Businesses often need to work with huge datasets to perform analytics and run machine learning models. As the size of datasets grows, interpreting it also gets more challenging. This raw data is often unstructured, complex and disoriented. 

Data wrangling, also called data munging, helps in making raw data structured and easily interpretable, getting it ready for analysis. In this article, we will explore 6 steps of data wrangling and how automating these steps can improve efficiency.

Steps for Data Wrangling

Once the data collection is completed, data wrangling can be started. It involves a few steps, starting from discovery and ending with publishing. 

These steps of data wrangling can be repeated until the data is ready to be analyzed:

1)  Discovery: The first step is to familiarize yourself with the raw data. It involves looking into its patterns and structure, noticing any ambiguities or errors, and seeing what needs to be removed in order to make the data ready for use.

2)  Structuring: Now that you have discovered what the data is and what your end goal is, you can begin by giving a structure to your raw data. For instance, this can be conversion of images to texts, columns to rows, and creating a storage format.

3)  Cleaning: This is an important step of data wrangling. Here you will determine what you decide to keep and what you decide to remove from your data set. Data cleaning ensures the final data set has fewer errors and redundancies. This can be done in various ways depending on your needs, such as by removing rows/columns, taking care of outliers, converting string characters to numerical ones, and removing null values.

4)  Enriching: Once the data set has been cleaned, you can now check to see if more data is required. If another set of data can enhance your analysis, then you may choose to do so and follow all the steps till cleaning. You may also want to augment your data by creating more subsets/variables from the existing dataset.

5)  Validating: Validation often involves use of automated processes to check for consistency in the data. You need to verify that your dataset does not have any discrepancies, such as ensuring the variables follow the right distributions, and seeing whether the data is ready to be analyzed.

6)  Publishing: After validation, the data is now ready to be published and becomes available to the organization for analysis.

How can automation tools help?

Raw data cannot give any useful solutions. According to a survey, data scientists spend around 60% of their time cleaning and organizing data and around 19% for collecting data sets. This makes data preparation take approximately 80% of their time, making it an important and lengthy task. Using automation tools can help in several ways.

• Use of DataOps: DataOps is a collection of data management practices which enable improved data flows and continuous use of data across the organization. It helps with accelerated data insights, data matching and end to end security improving data quality and data architectures, which speed up the processes. 
• Time reduction: data scientists can have more time to concentrate on modeling and analysis if automation tools are used for preparing data. These can significantly reduce time spent on cleaning and validating data, allowing for an efficient analysis.
• Prevents data leakage: data leakage is when the data set used for a machine learning algorithm contains more than relevant information which results in over estimated outcomes. Using data wrangling automation tools can reduce the likelihood of this leakage as data is scrutinized and prepared accordingly. 

Further Reading

The Ultimate Guide to Top 10 Data Science Tools

If you have other questions about data wrangling, we can help:

This article was drafted by former AIMultiple industry analyst Rijja Younus.