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Data Modelling and Data Quality – Liliendahl on Data Quality
Source: liliendahl.com
Article summary:
1. Data modelling and data quality have intersections that can be examined using a data quality mind map.
2. Data integrity, validity, completeness, and uniqueness are all impacted by the data model used.
3. Emerging technologies such as graph databases and document databases offer new ways of solving data quality issues.
Article analysis:
The article is generally reliable in its discussion of the intersections between data modelling and data quality, providing an overview of how different aspects of data quality are impacted by the chosen data model. The article also provides a good overview of emerging technologies that can be used to solve various challenges related to data quality.
However, there are some potential biases in the article that should be noted. For example, the article does not explore any counterarguments or alternative approaches to solving the challenges discussed in the article. Additionally, it does not provide any evidence for its claims or discuss any possible risks associated with using emerging technologies for solving these challenges. Furthermore, it does not present both sides equally when discussing how different aspects of data quality are impacted by the chosen data model; instead it focuses primarily on how they can be improved through a given model rather than exploring potential drawbacks or limitations as well. Finally, there is some promotional content in the article which could lead readers to believe that certain solutions are better than others without considering other options or alternatives.