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.
The Future of Data WranglingData 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.
- 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