Data Wrangling

What Is Data Wrangling?

Data wrangling is the process of transforming and structuring data from one raw form into a desired format with the intent of improving data quality and making it more consumable and useful for analytics or machine learning. It’s also sometimes called data munging.

The data wrangling process often includes transforming, cleansing, and enriching data from multiple sources. As a result of data wrangling, the data being analyzed is more accurate and meaningful, leading to better solutions, decisions, and outcomes.

Because of the increase in data collection and usage, especially diverse and unstructured data from multiple data sources, organizations are now dealing with larger amounts of raw data and preparing it for analysis can be time-consuming and costly.

Self-service approaches and analytics automation can speed up and increase the accuracy of data wrangling processes by eliminating the errors that can be introduced by people when they transform data using Excel or other manual processes.

Why Is Self-Service Wrangling Important?

Complex data sets have increased the time required to cull, clean, and organize data ahead of a broader analysis. At the same time, with data informing just about every business decision, business users have less time to wait on technical resources for prepared data, which is where data wrangling becomes valuable.

This necessitates a self-service model for a more democratized model of data analysis. This self-service model with data wrangling tools allows analysts to tackle more complex data more quickly, produce more accurate results, and make better decisions. Because of data wrangling abilities, more businesses have started using data wrangling tools to prepare before analysis.

How Data Wrangling Works

Data wrangling follows six major steps: Explore, transform, cleanse, enrich, validate and store.

Explore: Data exploration or discovery is a way to identify patterns, trends, and missing or incomplete information in a dataset. The bulk of exploration happens before creating reports, data visualizations, or training models, but it’s common to uncover surprises and insights in a dataset during analysis too.

Cleanse: Data often contains errors as a result of manual entry, incomplete data, data automatically collected from sensors, or even malfunctioning equipment. Data cleansing corrects those entry errors, removes duplicates and outliers (if appropriate), eliminates missing data, and imputes null values based on statistical or conditional modeling to improve data quality.

Transform: Data transformation or data structuring is important; if not done early on, it can compromise the rest of the wrangling process. Data transformation involves putting the raw data in the right shape and format that will be useful for a report, data visualization, or analytic or modeling process. It may involve creating new variables (aka features) and performing mathematical functions on the data.

Enrich: Enrichment or blending makes a dataset more useful by integrating additional sources such as authoritative third-party census, firmographic, or demographic data. The enrichment process may also help uncover additional insights from the data within an organization or spark new ideas for capturing and storing additional customer information in the future. This is an opportunity to think strategically about what additional data might contribute to a report, model, or business process.

Validate: Validation rules are repetitive programming sequences that verify data consistency, quality, and security. Examples of validation include ensuring uniform distribution of attributes that should be distributed normally (e.g. birth dates) or confirming accuracy of fields through a check across data. This is a vital step in the data wrangling process.

Store: The last part of the wrangling process is to store or preserve the final product, along with all the steps and transformations that took place so it can be audited, understood, and repeated in the future.

Benefits of Data Wrangling

Data wrangling makes it easier to analyze and interpret information, which leads to many benefits, including:

1. Increased Clarity and Understanding: If you’ve ever come across disorganized data, or a large data set that’s not easy to interpret, you understand the pain that comes with not being able to understand what the data represents and can be used for. Properly wrangled datasets can more easily be used for reporting in Tableau and other data visualization tools.

2. Data Consistency: Since businesses often use data from multiple sources, including third-parties, the data can often include many errors. An important step of the data wrangling process is creating uniform datasets that help eliminate the errors introduced by people and different formatting standards across third parties which results in improved accuracy during analysis.

3. Improved Accuracy and Precision of Data: The way data is manipulated and arranged can affect the accuracy and precision of analysis, especially when it’s related to identifying relevant patterns and trends. Examples of good data wrangling include organizing data by numerical data rather than categorical values or organizing data in tables rather than columns. Grouping similar data together improves the accuracy.

4. Improved Communication and Decision-Making: Increased clarity and improved accuracy reduce the time it takes for others to understand and interpret data, leading to better understanding and communication between teams. This benefit can lead to increased collaboration, transparency, and better decisions.

5. Cost Efficiency: Reducing errors, organizing data, and increasing collaboration all lead to more efficient use of time, saving organizations money. As one example, thoroughly cleaned and organized data reduces errors and saves developers time in creating reports or machine learning models. Consistent datasets make it easier for data scientists to reuse algorithms for their models or apply new ones through data science and automated machine learning.

The Future of Data Wrangling

Data wrangling used to be handled by developers and IT experts with extensive knowledge of database administration and fluency in SQL, R, and Python. Analytics automation has changed that, getting rid of cumbersome spreadsheets and making it easy for data scientists, data analysts, and IT experts alike to wrangle and analyze complex data.

Getting Started With Data Wrangling Tools

Alteryx Designer and Designer Cloud (formerly Trifacta Wrangler) use a no-code user interface, making it easy for nontechnical business users to wrangle data in a way that’s auditable and repeatable.

Data wrangling tools are built into every step of the Alteryx Platform with:

• Transformation tools, including Arrange, Summarize, and Transpose
• Preparation and cleansing tools, such as Formula, Filter, and Cleanse
• Data enrichment tools, including Location Insights, Business Insights, and Behavior Analysis

Data Wrangling Templates

Data wrangling templates can help you increase the speed, efficiency, and accuracy of your data during the data wrangling process.

Although you can use spreadsheets for data wrangling, they introduce the potential for human error. Additionally, the processes aren’t easily reproducible, even when you use functions and scripts. Not only that, but spreadsheets can only handle certain types of data.

While open-source programming languages, such as R, can handle data processing, the process can be very time-consuming, even with scripts. They’re also not built for data management, and large datasets can quickly drain available resources.

To assist with data wrangling, many companies offer data wrangling templates and wranglers, such as Alteryx Designer Cloud.

If you’re looking to speed up your data wrangling process, check out these starter kits, which work with the Alteryx Analytics Automation Platform. They cover multiple use cases and industries, plus include integrations with popular data lake and data warehouse services.