More than 80 percent of existing data is raw, and data wrangling techniques give data scientists a way to find the most useful information so it can be mined for real-world insights. In other words, data wrangling gives data a coherent shape, which makes it more usable.
What Is Data Wrangling?
Data wrangling is the process of taking raw data and transforming it into a higher-quality, usable format. It can include removing outliers from a data set, filling in gaps in data, deleting unnecessary data and merging multiple data sets into a single data set.
Data wrangling may be a manual or automated task, but it is always automated when dealing with large data sets. Depending on the size and capabilities of an organization, a company may have an entire data team dedicated to data wrangling. Otherwise, non-technical personnel are assigned to perform this process.
What Is an Example of Data Wrangling?
Data wrangling examples include merging multiple data sources into a single data set, identifying gaps in data and removing outliers.
Data wrangling uses a variety of processes to transform raw data into easily understandable and ready-to-use formats, with methods varying from project to project. This flexibility allows an organization to maintain a backlog of accessible data so insights can be more easily unearthed from within a data set.
Data wrangling is also known as data cleansing, data remediation and data munging. If a company wants to standardize dates within a data set where entries vary in formatting, data wrangling tools make that possible at scale.
Data Wrangling: 4 Common Uses
- Merging multiple data sources into a single data set
- Identifying extreme outliers in data and removing them to allow for proper analysis
- Identifying gaps in data, such as empty spreadsheet cells, and removing or filling them
- Cleaning up inconsistent values and tags
What Is Data Wrangling vs. ETL?
Data wrangling is the act of extracting data and converting it to a workable format, while ETL (extract, transform, load) is a process for data integration.
While data wrangling involves extracting raw data for further processing in a more usable form, it is a less systematic process than ETL. Data wrangling and ETL have a variety of uses and should be applied in different instances.
- Data wrangling is better suited for business managers and data analysts looking to uncover insights from data, while IT professionals tend to prefer ETL pipelines to ensure data transmits easily from source to target.
- ETL has more uses when working with structured data, while data wrangling is best for raw data.
- Data wrangling has more uses than ETL when combing through large batches of data.
- ETL is good for extracting enterprise data on a regular basis.
How Does Data Wrangling Work?
Data goes through various stages during the data wrangling process. These are the six steps of data wrangling:
1. Discovery
Data discovery involves identifying and analyzing the data that will undergo data wrangling. This is an opportunity to determine trends and patterns in the data, making note of factors like missing or unnecessary data. Based on these early observations, you can develop a plan for executing the following steps.
2. Structuring
Structuring or transforming data means converting raw data into an accessible format. The form of the data depends on the analytical model teams plan to use, so this must be decided before structuring the data. This step is crucial for making data readable, allowing teams to create data visualizations, reports and other helpful formats later on.
3. Cleaning
Raw data is often filled with errors as a result of human error, faulty sensors and other variables. You must then clean the data by removing duplicate data, fixing incorrect values, resolving data biases and taking other actions. This is a tedious task, but cleaning data is essential for ensuring data analyses are accurate and reliable.
4. Enriching
Once data is in an accessible format, teams must determine if they have enough data to complete their study or if they’re missing vital data points. If so, the data set can be enriched or augmented by adding data from trustworthy third parties and other data sources. Any new data must also go through the discovery, structuring and cleaning stages.
5. Validating
Data validation includes reviewing enriched data to make sure it is consistent, secure and of a quality that meets the standards of the project. This step is typically automated, and some programming may be involved. You may need to repeat the previous steps if you discover errors during this stage. Otherwise, you can move forward with analyzing the data.
6. Publishing
Data that has been validated is ready for publishing. Depending on the goals of the project and the data involved, you may produce a report, data visualization or another format that makes it easier for you and other stakeholders to analyze the data. You may also want to attach notes about the data wrangling process and tools to your published format.
What Are Data Wrangling Tools?
Data wrangling tools allow you to transform, clean and prep data, among other capabilities. Some examples include:
- Excel Power Query
- Alteryx
- Tabula
- OpenRefine
- Dataprep by Trifacta
- Zoho DataPrep
What Are the Benefits of Data Wrangling?
Data wrangling is an important part of any data-driven organization. Below are some of the main advantages data wrangling offers:
Data Consistency
A company’s data may come from a range of sources, each with its own guidelines around data quality and formatting standards. In addition, data gathered from consumers often contains inaccurate information. Data wrangling eliminates these inconsistencies, resulting in data sets that are uniform and reliable.
More Accurate Data Analysis
Clean and consistent data makes it much easier to analyze and deliver accurate insights. Without errors and biases in a data set, businesses can trust the results they’ve gleaned and add credibility to their findings.
Increased Cost-Efficiency
Sharing error-free data enables you to complete data analyses much faster. Developers also don’t have to worry about scrapping inaccurate analyses and going back to look for specific data mistakes. This means you can save more time and money in the long run while boosting productivity.
Enhanced Collaboration
Data that is clean and organized is much more accessible to different teams and stakeholders, including non-technical personnel. This makes it convenient to understand data-based findings, collaborate across departments and coordinate new initiatives. As a result, data wrangling can contribute to a culture of transparency and communication.
Frequently Asked Questions
What are the steps of data wrangling?
The steps of data wrangling are discovery, structuring, cleaning, enriching, validating and publishing.
What is data wrangling vs. ETL?
Data wrangling is the process of collecting raw data and transforming it into a more usable format while ETL refers to integrating data from a range of sources into a single, large data warehouse. In this sense, data wrangling occurs on a much smaller scale than ETL.
What is data wrangling vs. cleaning?
Data wrangling covers the entire process of converting raw data into a more convenient format while data cleaning refers specifically to removing errors from data to make it consistent and reliable. Data cleaning is then a stage in the data wrangling process.
