Data quality is the process of ensuring data meets expectations.
There are three main ways to detect a data quality issue:
- A business user reports an issue.
- A data test fails.
- Data monitoring raises an alert.
How to handle Data Quality issues by detecting, understanding, fixing, and reduce
Data Quality: Refers to the accuracy, consistency, and reliability of data. Data observability often includes mechanisms for measuring and improving data quality through automatic tests and checks.
- The principle of "garbage in, garbage out" underscores the importance of high-quality data. Inaccurate or poor-quality data leads to poor model performance, regardless of the model’s sophistication or the expertise of the data scientists.