Data cleaning is essential when it comes to data analytics or uploading batches of data into legacy systems. Most of the time, data cleaning are actions to take based on a set of rules and are often repetitive in nature.
Essential data cleaning to-do list
Data cleaning can easily be the part where people spend most of their time on. Regardless of the project type, data cleaning usually consists of the following actions:
- Merging multiple datasets
- Removing empty or irrelevant observations
- Check for typos or making capitalization consistent across data sets
- Check for observations that are ‘not applicable’
- Filtering out the variables that you don’t need and handling missing information
- Categorizing data into specific types or categories
- Changing data types
After finishing the validations, you may have already spent hours on cleansing the dataset and haven’t even started your actual work! This is where bots can bring in values and ease your burden from eyeballing thousands or millions row of data.
This article focuses on how robotic process automation (RPA) bots can automate the data cleaning step.
