Applications of Data Wrangling
Learn about the importance and applications of data wrangling.
Reasons for applying data wrangling
The following are reasons why data wrangling is an essential part of any data project.
- The application of data wrangling techniques saves time for stakeholders. For example, data engineers apply data wrangling techniques to data so that other stakeholders won’t need to apply them once they start working with data.
- The data collected for analysis can be unreliable and faulty, and because of this, we perform data wrangling techniques to make data usable. For example, we find and handle outliers resulting from data entry errors using data wrangling techniques.
- To aggregate data originating from multiple sources, we apply data wrangling techniques. For example, we combine data originating from multiple CSV files, databases, and web services by using joins.
- As data within an organization increases, extracting meaningful insights becomes difficult. Organizations can make this easy by using automated data wrangling techniques that guarantee data quality and reliability, leading to better decision-making in a shorter time.
