Data wrangling, also known as data munging, is the process of transforming and mapping raw data into a format that is usable and structured for analysis. The process contains a series of steps to clean, organize, and integrate data from a variety of sources to ensure that it is ready for analysis. Data wrangling is used to make data suitable for modelling and visualization. The components of data wrangling are:
- Data collection – collecting data from various sources and importing it into a tool for further processing
- Data cleaning – identifying and correcting errors, standardizing formats, and correcting inconsistencies in data entries
- Data transformation – normalizing, adjusting, and combining data into formats that can more easily be analyzed
- Data integration – merging and linking data sets from different sources
- Feature engineering – generating new variables or features from existing data to improve analytical models
- Data enrichment – adding external data and creating new metrics based on existing data to enhance dataset
- Data validation – performing quality checks to ensure the data is accurate
Data wrangling is a vital process for any organization that uses data. The process improves data quality, enhances usability, and supports better decision making. By integrating more data and leveraging different tools and techniques to maximize the value of the data.