In the tech industry, data quality is crucial for the success of any project, particularly in the field of data science, machine learning and data analysis. Poor quality data can lead to inaccurate results and flawed decision-making, which can have serious consequences for businesses and organizations.

To ensure the quality of data, many organizations rely on data quality monitoring platforms. These platforms provide a range of tools and capabilities for identifying and addressing issues such as missing or incorrect values, duplicates and inconsistencies. One important feature of these platforms is anomaly detection, which can help identify data points that do not conform to expected patterns or distributions. This can be particularly useful in detecting errors or inconsistencies in data, which can then be corrected or flagged for further investigation.

One of the key benefits of using data quality monitoring platforms is that they can help organizations to identify and fix issues with their data early on, reducing the risk of errors and improving the overall accuracy of their analysis, decision making, and machine learning models. Additionally, these platforms can help organizations to identify trends and patterns in their data that may not have been visible without the use of these tools.

This talk will discuss the importance of data quality in the tech industry and how data quality monitoring platforms can help ensure that data is accurate, complete, and relevant, as well as the benefits they offer to ensure the data is reliable and trustworthy. It also explores the role of anomaly detection in identifying and addressing