What does Data Redundancy typically lead to?

Data redundancy typically leads to inconsistencies and increased storage costs because when the same data is stored in multiple locations, any changes made to one instance of that data need to be replicated across all other instances. If this is not done properly or consistently, it can result in discrepancies where different versions of the same data exist in different locations—creating confusion and potential errors in data analysis and reporting.

Moreover, storing duplicate data consumes extra storage resources, which raises costs related to data management. Therefore, while redundancy may initially seem to provide benefits like improved access speed for certain applications, the drawbacks in terms of data integrity and increased costs often outweigh those benefits. Thus, the correct identification of inconsistencies and increased storage costs as a consequence of data redundancy is a critical understanding within data management practices.