SIMULATION - Describe data "scrubbing":
See the solution below.
Scrubbing is a processed used to help identify dirty data.
The process removes formatting (like in a phone number) to line up data to check for duplicates or bad data.
Data scrubbing is the process of identifying and correcting or removing inaccurate, incomplete, or irrelevant data from a database or dataset. The goal of data scrubbing is to improve data quality, which can in turn improve the accuracy and reliability of business decisions based on that data.
The process of data scrubbing typically involves several steps, including:
Identifying data quality issues: This may involve running data profiling tools to analyze the data and identify issues such as missing values, inconsistent data formats, or outliers.
Developing rules for data correction or removal: Based on the analysis of the data, rules are developed to correct or remove data that does not meet certain quality standards. These rules may be based on predefined criteria or may be developed through machine learning algorithms.
Cleaning the data: Once the rules are established, the data is cleaned using automated or manual processes. Automated processes may include running scripts or using data scrubbing software, while manual processes may involve reviewing data records and making corrections manually.
Verifying data quality: After the data has been cleaned, it is important to verify the data quality to ensure that the data is accurate and complete. This may involve running data profiling tools again or reviewing a sample of the data.
Documenting data quality issues: Finally, it is important to document the data quality issues and the steps taken to correct them. This documentation can be used to inform future data scrubbing efforts and to provide transparency to stakeholders about the quality of the data.
Overall, data scrubbing is a critical process for maintaining accurate and reliable data. By identifying and correcting data quality issues, organizations can ensure that they are making informed decisions based on high-quality data.