Normalization is a critical process in designing and implementing large databases. Normalizing a database reduces data redundancy and, therefore, the risk of errors by developing rules according to which data will be stored (Lemathieu et al., 2018; Tilley & Rosenblatt, 2016). These rules ensure that data is only stored once, and relationships are used to link relevant entries instead of creating redundancy (Mallach, 2016; Tilley & Rosenblatt, 2016). This reduces the possibility of errors as it minimizes the amount of times entries need to be modified to change data. It also makes the database easier to use and maintain.
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Although normalization is not strictly required in database design, and a non-normalized database can still function, it becomes necessary as the database’s size and complexity increase. Since a data item, such as a user’s e-mail address, is only stored once and generally accessed by links in a normalized database, it only needs to be modified once if the data changes (Mallach, 2016; Tilley & Rosenblatt, 2016). This means that all entries referencing this user’s address will now contain the correct updated address. In contrast, in a non-normalized database, each field containing the address needs to be modified. This can take longer the more such fields exist, and lead to errors if some fields are not modified for any reason, leading to inconsistent data.
Normalization provides a safeguard against input errors as data only needs to be entered once. Finally, adding or removing entries in an insufficiently normalized database can be problematic as nonkey fields can be dependent on other nonkey fields (Tilley & Rosenblatt, 2016). This can be resolved by maintaining “dummy” records, but this can be unreliable in large and quickly-changing databases. Normalizing a database, therefore, ultimately saves time on maintaining it and prevents errors that will take more time to resolve.
Lemathieu, W., vanden Broucke, S., & Baesens, B. (2018). Principles of database management: The practical guide to storing, managing and analyzing big and small data. Cambridge University Press.
Mallach, E. G. (2016). Information systems: What every business student needs to know. CRC Press.
Tilley, S., & Rosenblatt, H. (2016). Systems analysis and design (11th ed.). Cengage Learning.