Data Quality Done Wrong!
Bad Strategies for Implementing Data Quality!
This text only reflects my opinion and experience, do not use it as a silver bullet, but reflect and start creating your own point of view, please ;)
Data quality is essential for the effective functioning of any organization that relies on information to operate and make decisions. However, poor data management practices can often compromise this quality and harm business performance.
One of the most common problems is the need for more standardization. In an ideal world, all data would be consistent and formatted similarly. But in reality, data is often entered in multiple formats, making analysis and comparison a problematic task. Lack of standardization can lead to a backlog of inconsistent and low-quality information.
Another harmful practice is the lack of data cleaning and maintenance. Outdated or irrelevant data can pollute the database, making analyses inaccurate and potentially leading to better-informed decisions. Furthermore, the presence of duplicate data can cause confusion and inaccuracy in the interpretation of the data.
Excessive data collection is also a bad practice. While having as much information as possible seems beneficial, collecting more data than necessary can lead to information overload and…
