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 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.
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