Founder @ DQOps open-source Data Quality platform | Detect any data quality issue and watch for new issues with Data Observability
Data cleaning is the process of correcting or removing invalid values. There are several groups of data cleaning techniques that you can apply: 👉 Data format standardization methods help to correct small errors without losing information. 👉 Value modification methods covert values to be usable, but at the cost of losing some precision. 👉 Missing values are fixed by data enrichment, such as looking up values from other sources. 👉 Invalid data removal methods focus on removing incorrect records or values. 👉 All other typical data quality errors are handled by detecting errors using data quality checks. Most of these methods can be automated, making it an autonomous process. #dataquality #dataengineering #datagovernance
Check out my free eBook "A step-by-step guide to improve data quality" for hints on detecting data quality issues: https://dqops.com/best-practices-for-effective-data-quality-improvement/