designed databases for business processes. As a result, the data are redundant, inconsistent, inaccurate, and corrupted. For a small data set, the use of non-database tools such as spreadsheet may not cause serious problem. However, for a large organization, corrupted data could lead to serious errors and destructive consequences. The common defects in data resources management are explained as follows. (1) No control of redundant data People often keep redundant data for convenience. Redundant data could make the data set inconsistent. We use an illustrative example to explain why redundant data are harmful. Suppose the registrar’s office has two separate files that store student data: one is the registered student roster which records all students who have registered and paid the tuition, and the other is student grade roster which records all students who have received grades.
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As you can see from the two spreadsheets, this data management system has problems. The fact that “Student 4567 is Mary Brown, and her major is Finance” is stored more than once. Such occurrences are called data redundancy. Redundant data often make data access convenient, but can be harmful. For example, if Mary Brown changes her name or her major, then all her names and major stored in the system must be changed altogether. For small data systems, such a problem looks trivial. However, when the data system is huge, making changes to all redundant data is difficult if not impossible. As a result of data redundancy, the entire data set can be corrupted. Information Systems for Business and Beyond (2019) pg. 69
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