What is Data Wrangling?

by Interzoid Team

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Data Wrangling is one of many similar terms that means preparing and transforming raw data into a usable form for a specific downstream purpose, such as Analytics, building a Data Warehouse, feeding into an AI model, Marketing, or any other data-driven application. If done properly, Data Wrangling can result in a far greater ROI for these various data-driven initiatives, from speed of deployment to the actual results and outcomes.

Data Wrangling processes generally consist of the following:

Data acquisition: Collecting the data from various, likely disparate data sources, including databases, data streams, APIs, or various forms of Web scraping.

Data restructuring: As data can exist in multiple shapes, sizes, and formats, manipulating it into a common structure is important for usability and other wrangling processes.

Data cleansing: Identifying and correcting any errors or inconsistencies in the data, such as missing values, incorrect data types, duplicate or redundant data, and potentially misleading outliers.

Data transformation: Converting the data into a final form that is suitable for analysis, such as merging multiple data sources, creating derivative data, removing unnecessary data, or combining data into single datasets.

Data enrichment: Adding additional data to an existing dataset, such as demographic data, location data, weather data, or other purchased or publicly available third party data.

Data validation: Ensuring that the data is accurate and complete, and meets the requirements for analysis. Email verification is an example.

These are all essential sub-components of the Data Wrangling process, ensuring that data is accurate, comprehensive, and in optimal form for its intended purpose for the best possible data-driven outcomes.