# Introduction to Automated Data Quality and Governance System Data is one of the most valuable assets of an organization, and it is important to keep it secure and accurate. An automated data quality and governance system helps to ensure that data is reliable, secure, and compliant with industry regulations. This system can be used to identify, monitor, and resolve data quality issues, track data lineage, and provide data governance. This system can be used to automate data quality checks, provide data governance, and improve data accuracy. It can also be used to identify and troubleshoot data quality issues, improve data lineage, and integrate data from multiple systems. Automated data quality and governance systems are essential for any organization, and can help to improve data quality and reduce the risk of data breaches. This article provides an overview of the benefits of implementing an automated data quality and governance system.

## Algorithm Overview The automated data quality and governance system is an algorithm that ensures the accuracy and reliability of data. It is comprised of three steps: data validation, data cleansing, and data monitoring. 1. **Data Validation**: This step checks the data to make sure that it is accurate and of the correct format. It verifies whether data matches the expected format and validates the data against data standards. 2. **Data Cleansing**: This step cleans the data to ensure that it does not contain any errors or inconsistencies. Data cleansing involves removing unnecessary data, correcting errors, and formatting data for consistency. 3. **Data Monitoring**: This step monitors the data to ensure that it remains accurate and reliable. Data monitoring involves tracking changes to the data and ensuring that it remains consistent over time. ## Sample Code This code sample is written in Python. ``` # Initialize Data Quality and Governance System dqgs = DataQualityGovernanceSystem() # Data Validation data = dqgs.validate_data(data) # Data Cleansing data = dqgs.cleanse_data(data) # Data Monitoring dqgs.monitor_data(data) ```