Data redundancy can have several negative implications:
Increased Storage Costs: Storing duplicate data requires additional storage space, leading to increased costs. Data Inconsistencies: Inconsistent data can arise when the same information is updated in one place but not in another. Decreased Data Quality: The presence of redundant data can lower the overall quality of the dataset, making it less reliable for epidemiological studies. Complicated Data Analysis: Redundancy can complicate data analysis, making it difficult to derive accurate insights and conclusions.
