Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. In other words, it is the process of cleaning and unifying messy and complex data sets for easy access and analysis.
- With the amount of data and data sources rapidly growing and expanding, it is getting more and more essential for the large amounts of available data to be organized for analysis.
- This process typically includes manually converting/mapping data from one raw form into another format to allow for more convenient consumption and organization of the data.
The goals of data wrangling:
- Reveal a “deeper intelligence” within your data, by gathering data from multiple sources
- Provide accurate, actionable data in the hands of business analysts in a timely matter
- Reduce the time spent collecting and organizing unruly data before it can be utilized
- Enable data scientists and analysts to focus on the analysis of data, rather than the wrangling
- Drive better decision-making skills by senior leaders in an organization
The key steps to data wrangling:
- Data Acquisition: Identify and obtain access to the data within your sources
- Joining Data: Combine the edited data for further use and analysis
- Data Cleansing: Redesign the data into a usable/functional format and correct/remove any bad data