What Is Data Wrangling? A Comprehensive Guide for 2024

No matter your industry of operation, data is one of the most important vectors that help you understand your customers. However, raw data is not very helpful, and drawing conclusions based on raw data is never a good idea. That’s where data wrangling comes in.

What Is Data Wrangling?

Data Wrangling is the process of cleaning, transforming, and organizing raw data into a usable format for analysis or machine learning. It ensures the data is consistent, accurate, and ready for insights. Key steps include data cleaning, structuring, and enriching. It is also referred to as data munging or data remediation.

A good illustration of data wrangling would be in the process of collecting sales data from various regions and compiling it into a single dataset for competitive analysis. It involves tasks like:

• Removing unnecessary data from the dataset, such as duplicate or irrelevant information. 
• Addressing gaps in datasets by adding further information — for example, filling up empty cells in Excel sheets.
• Grouping of multiple subcategories into one. You could merge categories like ‘T-shirts,’ ‘Jackets,’ and ‘Hoodies’ into a singular ‘Clothes’ category to simplify analysis.
• Extracting data from large datasets and creating multiple categories. For example, you could divide the ‘Address’ field into ‘PIN code,’ ‘City,’ and ‘State’ for more granular data. 

How Data Wrangling Works

While the specifics may differ based on project requirements, there are six general steps that are typically followed to perform data wrangling.

To understand each step in the data wrangling process, let’s take an example of a data analyst who’s working for a fitness app and has to analyze user data to improve the app's features and user engagement.

1. Discovery

This is the first step in the process, where you develop an understanding of the data you have collected. At this stage, you will identify the source of data and quality of data, and look for errors and incomplete information. 

Example: The analyst will collect user data from the fitness app, including user activity logs, demographics, and feedback, and study metrics like step counts workout durations and app usage frequency to understand user behaviour and most interactive parts of the app.

2. Structuring 

The raw data that you source is often unstructured, which makes analysis challenging. As the name implies, the structuring process is all about converting unstructured data into structured tables or databases. 

Example: The fitness app analyst would create tables from the collected data. Perhaps one table for basic user information like user ID, subscription type, and gender, and another table for activity log information, detailing activity type, duration, calories burned, and timestamps.

3. Cleaning 

Data cleaning is a very important part of data wrangling in which you eliminate errors and duplicate values and handle missing values to improve the overall quality of the dataset. 

Example: The analyst will remove duplicate user IDs and those that are no longer active subscribers before focusing on correcting important values like calorie count and time spent on the app. 

4. Enrichment

In enrichment, you add supporting information from external sources to the existing dataset to offer deeper context and enhance the value of the data in providing deeper insights. 

Example: To perform data enrichment, our analyst could add weather data to explore how weather conditions may affect outdoor activities or integrate data from fitness bands to keep track of heart rate and sleep patterns.

5. Validating 

In this step, you will validate all the information that you’ve collected and cleaned, verifying data accuracy using techniques like validation rules and cross-checking data reference points. 

Example: The analyst confirms that every user has a unique ID and looks for inconsistencies in the data. He or she would check the legitimacy of user feedback by matching it with user activity data. 

6. Publishing 

This is the last step of the data wrangling process, where you present the clean data to analysts. You might also create data visualizations in the form reports or dashboards for stakeholders. 

Example: The analyst would prepare dashboards and reports to explain how users are interacting with the fitness app and what features would work best. 

Benefits of Data Wrangling

There are several reasons why data wrangling is a must for organizations that aim to make data-driven decisions. 

Better Quality of Data

Data wrangling significantly improves the quality of data by resolving errors, inconsistencies, and missing values that most raw datasets contain. Wrangling ensures that the data is correct, homogeneous, and valid to work with, all of which are critical for informed decision making and trustworthy insights.

Analytical Efficiency

Simplifying the data preparation process frees analysts to concentrate on actual analysis and not waste their time cleaning data. This efficiency gain accelerates the analytical cycle, thus allowing more data exploration and complex analytics within short time frames. 

Advanced Analytics and Machine Learning Benefits

Data that is structured and clean is a must for many advanced analytics and machine learning models to perform effectively and provide valuable insights, especially so in predictive analytics and customer segmentation. 

Data Integration from Multiple Sources

We have a plethora of data sources today — IoT devices, social media, enterprise systems, and so much more. Data wrangling integrates data from these various sources into a single location, standardizing formats and eliminating discrepancies in order to create one cohesive dataset that enables comprehensive analysis for a wide view of the subject matter.

Faster Decision Making

Data wrangling can help speed up the decision-making process of any business. Organizations that continually ensure their data is structured, accurate, and reliable are always prepared for timely analyses, enabling rapid insights to drive strategic decisions that keep them ahead of their competition.

Data Wrangling Best Practices

We’ve already seen how data wrangling is important in making data-driven decisions. To derive maximum value from it, though, there are some best practices to keep in mind, which can elevate the data quality and results even further. 

Here are some best practices to follow in data wrangling:

Understand Your Audience and Objectives

First things first, data wrangling requires a proper understanding of who will use the data and for what purpose. Identify the stakeholders, their data needs, and the specific questions they need answers to. 

The marketing team for the fitness app in our earlier example may want data to target certain demographics, based on which the data analyst will extract data and clean it on aspects of age, location, and buying habits of the users. Knowing the requirements will help choose the right data attributes and structure them accordingly.

Select Relevant Data

You must select the right data for analysis — not just an avalanche of unnecessary data. Gather data that’s relevant to the case, accurate, and as close to the source as possible. Avoid data with many null values or duplicate entries, which could skew and bias the analysis. 

The fitness app analyst may consider focusing on active users who log their workout sessions regularly. By its very nature, this information is more likely to deliver valuable insight into user engagement and application performance than that derived from less active users. 

Utilize Modern Tools and Techniques

Using state-of-the-art data-wrangling tools and techniques can reduce effort and increase data accuracy. Such tools offer automation, visualization, and advanced data manipulation capabilities to facilitate the wrangling process. 

The analyst might use the integrated data wrangling tool of the fitness app’s database for automated cleaning and transformation of its data. Automatic error detection and correction can easily be done for common errors like duplicate entries or wrong data formats.

Verify and Validate Data

Even with the use of automated tools, there is a need for human oversight. Verify that the data is accurate and free of all errors before performing analyses. Validate the data by applying necessary rules and checks to ensure that the data is consistent and meets required standards. 

In our example, after cleaning and formatting, the analyst would implement checks for unique user IDs and validly formatted activity logs, and might check the data against external data sources for consistency.

Document the Process Description

Documentation is an often forgotten yet very important aspect of data wrangling. Make note of each step taken in the course of wrangling the data, including any transformations, assumptions, and decisions made in the process. This advances transparency, reproducibility, and compliance. 

The analyst documents each step of the data-wrangling process, noting any assumptions made. This documentation is then shared with the larger team so everyone can understand the data lineage and how any given data preparation occurred.

Data Wrangling: Challenges and Limitations

While data wrangling offers multiple benefits, there are some challenges that every analyst must take into consideration, lest they hinder the overall output: 

Scalability and Performance

One problem with large volumes of data is that computing resources are burnt in order to handle it effectively. As datasets grow bigger, data-wrangling processes should be optimized so as not to become a bottleneck. 

Changing Sources and Formats

Data sources and formats vary vastly, and each introduces its own challenge. For example, integrating data from social media with transactional databases requires in-depth knowledge of the structure and semantics of each source, complicating the wrangling process.

Data Quality and Consistency

Data quality is a big concern, as datasets often have missed values, outliers, and inconsistencies. When cleaning such data, maintaining data integrity and accuracy is paramount. Inappropriate cleaning might introduce biases or distortions, which could then taint any analyses performed on the dataset.

Privacy and Security Concerns

With increasing regulations around data privacy, such as GDPR and CCPA, wrangling data while ensuring compliance is a challenge. Data masking, anonymization, and other security measures are necessary to safeguard sensitive information. There’s a difficult balance to strike between these requirements and the need for detailed analyses.

Handling Noisy Data

Noise is irrelevant or misleading information that may get in the way of otherwise meaningful patterns in data, thus affecting the accuracy of analysis. Noise identification and mitigation are key to reliable analyses and model training; however, it’s not always easy to separate noise from valuable data points.

Wrapping Up…

Data wrangling is one of the most efficient ways you can achieve data-driven results. To get the most out of it and be sure that you’re doing it right, follow the basic steps we’ve covered in this article. 

As powerful and useful as data wrangling is, though, it also poses some not-insignificant challenges and limitations. You can tackle or work your way around most of these limitations by implementing the recommended best practices we’ve discussed. 

Savant’s automated data wrangling capabilities empower you to draw maximum value from your business data quickly and effectively. With advanced structuring, cleaning, and enrichment tools, you’ll have access to high-quality, accurate data for strategic decision making in no time! Contact us today to elevate your data transformation strategy.