[Data Governance]A data analyst receives a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and…

The dataset contains both "dates of birth" and "employees’ birth months." The birth month can be directly derived or calculated from the date of birth. Storing data that can be derived from other fields within the same record is a form of data redundancy. This practice is inefficient as it increases storage needs and, more importantly, creates a risk of inconsistency. For example, if the date of birth is corrected, the birth month field might not be updated, leading to conflicting information within the same record. Identifying and resolving such redundancy is a fundamental step in data cleaning and governance.

B. Equivalence: This refers to different data values representing the same real-world entity (e.g., 'St.' and 'Street'), which is not the issue here.
C. Parallel: This term is not a standard classification for data inconsistency; it typically relates to system architecture or data processing methods.
D. Duplication: This usually refers to entire records (rows) being repeated in a dataset, not redundant information within a single record. ---

1. Elmasri, R., & Navathe, S. B. (2017). Fundamentals of Database Systems (7th ed.). Pearson. In Chapter 15, "Basics of Functional Dependencies and Normalization for Relational Databases," the authors discuss how storing derivable values (e.g., a person's age from their birth date) introduces redundancy and can lead to update anomalies (Section 15.2.1). The presence of both 'date of birth' and 'birth month' is a direct example of this principle.

2. DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Technics Publications. Chapter 5, "Data Modeling and Design," emphasizes that a key goal of sound data modeling is to reduce data redundancy. It defines redundancy as the unnecessary repetition of data, which can be within a single record (as a derivable attribute) or across a database.

3. Stanford University. (2016). CS145: Introduction to Databases - Lecture 8: Functional Dependencies and Normalization. Retrieved from web.stanford.edu/class/cs145/slides/08-fds-and-normalization.pdf. Slide 30 discusses redundancy as information being "repeated unnecessarily" and illustrates the update, insertion, and deletion anomalies that arise from it, which is the core problem with having both a date of birth and a derivable birth month.