Patterns in data modeling: addressing common patterns and best practices

Resumen

This article addresses the importance of standardization in data modeling, as well as its impact on the quality of DBMS. Standardization encourages uniformity by following fixed guidelines, which facilitates comprehension and collaboration among teams. Additionally, we discuss how the normalization of primary keys and indexes enhances data integrity. Reliability is addressed, highlighting security, referential integrity, and traceability. Furthermore, the article under discussion covers best practices such as selecting the best data and adapting standardization to sources of questionable provenance, as inappropriate standardization can lead to errors, given that data might be incomplete precisely due to their origins from dubious sources. However, we acknowledge that selective standardization is crucial for complex data; we also address how standardization within data modeling contributes to a more efficient and accurate system. In summary, standardization in analysis reinforces confidence in the data. Nevertheless, it should be applied cautiously to preserve the original information.

DOI:https://doi.org/10.56238/innovhealthknow-026