What Is Data Wrangling? How It Enables Faster Analysis

Businesses managing massive data volume face complexities in making sense of it. 

Data wrangling helps in such situations. It transforms raw data into readable formats for easy analysis.

Data wrangling involves several steps such as gathering, filtering, converting, exploring, and integrating that enable businesses to analyze data and make better decisions. Many companies use data preparation software to perform data wrangling and speed up their analysis.

Modern businesses are data-driven. Data wrangling helps them clean, structure, and enrich raw data into a clean and concise format for simplified analysis and actionable insights. It allows analysts to make sense of complex data in the simplest possible way. 

Importance of data wrangling

Incomplete and inaccurate data affects business operations. Data wrangling focuses on cleaning unwanted raw data to streamline the business flow.

As data becomes more unstructured, diverse, and distributed, data wrangling becomes a common practice in organizations. It speeds up data analysis and helps gain insights faster. With data wrangling, analysts can access quality data for analysis and other downstream processes.

Data wrangling is a tricky and time-consuming process when done manually. Organizations prefer training employees on data wrangling tools with automation, artificial intelligence, and machine learning features, helping them build a consistent and scalable process.

Data wrangling vs. data cleaning vs. data mining

Data wrangling involves processing data to convert it into an accessible and understandable format and generate actionable insights. In comparison, data cleaning finds and corrects inaccurate data in large datasets. It identifies duplicity and null values and fixes obvious errors to ensure data structure accuracy and consistency.

While data wrangling and data cleaning have different goals in data science, they accelerate data transformation and drive analytical decision-making. Companies perform data preprocessing before wrangling. This ensures data accuracy and valuable output post analysis.

Data mining helps analysts sift through and sort data to find patterns and hidden relationships in large datasets. Data wrangling enhances the mining process and uncovers patterns in customer behavior, market trends, and product feedback.

Data wrangling steps

Data wrangling ensures data reliability. It includes specific steps to feed accessible and formatted data into analysis.

Discovery

The first step in data wrangling is becoming familiar with the data. This includes understanding trends, patterns, relationships, and apparent issues such as incomplete or missing data. 

In this stage, you can identify multiple possibilities or ways to use data for different purposes. It's the same as checking ingredients before cooking a meal.

When you begin with data gathered from multiple sources, it requires formatting to understand relationships. The data discovery step helps you compile and configure disparate data, helping you prepare data for analysis.

Structuring

Data structuring transforms raw data into a structured format for easier interpretation and analysis. Raw data doesn't help analysts because it's incomplete or incomprehensible. It needs to be parsed so that analysts can extract relevant information.

If you have a website's HTML code, you need to parse it to pull the data you need, helping you create a more user-friendly spreadsheet. Data structuring allows analysts to format data and troubleshoot errors for effective and efficient analysis.

Cleaning

People often use data cleaning and data wrangling interchangeably. However, data cleaning is one step in the data wrangling process.  

With data cleaning, analysts can fix inherent issues in a dataset, including:

• Removing empty cells or rows
• Standardizing inputs
• Performing other cleaning tasks for final analysis

Enriching

After transforming data into a usable format, you need to find whether data from other datasets can make your analysis more effective. Consider adding such data points to draw actionable insights. This optional step helps analysts improve data quality if it doesn't meet requirements. For example, combining two databases where one contains customer phone numbers and the other does not.

As you add more data items, repeat the above steps to increase the usability and reliability of newly added data.

Validating

Data validation ensures that data is fit for analysis. It’s an automated process where a program checks data for errors or inconsistencies and issues reports to maintain data quality, accuracy, authenticity, and security.

This includes checking whether the fields are accurate and if the attributes are normally distributed. Analysts can repeat the validation process several times to find and fix errors.

For example, it involves ensuring that all negative bank transactions have relevant transaction types like bill pay, withdrawal, or check.

Publishing

Analysts can publish data after validating it. They can either share it as a report or an electronic document based on an organization's preferences.

The data can be deposited into a database or can be processed further to create larger and more complex data structures such as data warehouses.

Sometimes data analysts update their record of transformation logic in the publishing stage. It helps them arrive at results faster for downstream and future projects. Like chefs maintain their recipe book, experienced data analysts and scientists record transformation logic to speed up their process.

Benefits of data wrangling

Data wrangling removes unwanted complexities from raw data. It converts complex data into a usable format, improving its usability and compatibility for better analysis.

Some well-known benefits of data wrangling are:

• Data wrangling structures data and makes it usable to compliment business needs. 
• Enriches data for business insights and behavioral analysis.
• Simplifies complex data for data analysts, data scientists, and IT experts and makes their jobs easy.
• Helps businesses prepare a strategic plan on how data can help business growth. 
• Differentiates data types based on the derived information.

Data wrangling challenges

Data wrangling presents many challenges, especially while preparing a data sheet that defines business flow.

• Analyzing use cases. Stakeholders' data requirements depend entirely on the questions they're trying to answer using data. Analysts should understand the use cases clearly by researching more on questions like what subset of entities is relevant, whether they are trying to predict an event's likelihood, or estimating a future amount.
• Gaining access. It's not always easy for data users to secure access to raw data. They usually submit precise instructions to access deleted data. These limitations make working on data time-consuming and less effective.
• Examining similar entities. Once raw data is downloaded, you can't guarantee what's relevant and what isn't. For example, we consider "customer" an entity. The data sheet may contain a customer "Brad Paul". Another column might have a different customer, "Brad P." In such cases, you need to thoroughly analyze various factors while finalizing the columns.
• Exploring data. Data can be highly related or similar in big files. It makes feature and model selection challenging. Remove redundancies in data before exploring the relationships to the outcome. For example, there can be two columns for color, one in English and another in French. It might lead to complex data models if you don't remove such redundancies.
• Avoiding selection bias. Selection bias occurs when gathered data doesn't represent the true or future population of cases. Ensure that training sample data represents the implementation sample.

Data wrangling best practices 

You can perform data wrangling in many ways. Follow these best practices to save time and optimize the process.

Interpret data

Different organizations use data differently. It's essential to understand how to interpret data to help businesses achieve the expected outcome. 

Understanding your audience goes a long way while wrangling data. When you know who will access and use data, it helps you address their specific needs and goals. For example, while wrangling data for a financial firm, analysts would break down data into particular segments such as amount spent on purchases or employer contribution in 401(k). It would be relevant if businesses use this data to prove their revenue-generating capabilities but would need further segmentation when the goal is reducing expenditures.

Use appropriate data

It's not about having lots of data but the right datasets. Data wrangling provides appropriate data and is crucial for its analysis.

Tips for using accurate data:  

• Avoid repetitive, similar, and null entries.
• Don’t depend on a single data source to extract data. Use different sources instead.
• Filter data based on necessary rules and conditions.

Understand the data 

Evaluate the needed data quality and accuracy for data analysis. You also need to comprehend how interpreted data matches an organization’s needs.

Key points to remember:

• Identify database and file formats 
• Use data visualization to picture the current standard
• Generate data quality metrics as required 
• Be careful with data limitations

Reassess wrangled data

Although carefully optimized, wrangled data can still have room for improvement or errors. Reevaluate wrangled data to ensure quality and reduce inefficiencies. For example, when analysts wrangle financial data, they might find opportunities to enhance quality. They can correspond unpaid invoices to anticipated future payments or detect operational errors.

Transform data for better analysis

Data wrangling is instrumental to analyzing, interpreting, and cleaning raw data for better analysis. It can be time-consuming but saves a lot of time spent analyzing irrelevant information. This brings valuable data together, generates insights, and helps modify or optimize business processes.

Raw data moves through multiple processes in an organization. These processes transform and modify data to make it readable and fit for several analyses. Businesses can track such information assets using data lineage and make it easier for analysts to trace errors back to their root cause.

Learn more about data lineage and why it's important to track data flow.