What Is Data Wrangling & How Does It Work?

Content

What Is Data Wrangling?

Organizations deal with large amounts of raw data and preparing it for analysis can be timely and costly. Wrangling alleviates that burden by transforming, cleansing, and enriching data to make it more applicable, consumable, and useful. Unlike data pre-processing or preparation, wrangling happens throughout the analysis and model-building stages of the data analytics process.

Wrangling improves the quality of the data being analyzed, which means rather than waste time and resources dealing with the consequences of bad data, organizations can create accurate, meaningful analyses that allow for better solutions, decisions, and outcomes.

How Data Wrangling Works

Data wrangling follows five major steps: Explore, transform, cleanse, enrich, 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.

Transform: Transforming or structuring data is important; if not done early on, it can compromise the rest of the wrangling process. Data transformation involves putting the 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.

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 missing values based on statistical or conditional modeling to improve data quality.

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.

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.

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. Analytic Process Automation (APA) 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

The Alteryx APA Platform™ uses a graphical interface, so it’s easy to document, share, and scale critical data wrangling work in a way that’s auditable and repeatable. No-code, low-code modes allow users to either drag-and-drop or tackle one line of programming at a time. Users can also save their work in formats similar to a spreadsheet file or as part of a larger data model to a shared platform.

Data wrangling tools are built into every step of the Alteryx APA 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