A company keeps two sets of data regarding its customers. The first is maintained by the accountant and contains each customer's billing, credit, and payment information, identified by federal tax id. The second records monthly sales using a customer number assigned by the sales staff. It is difficult to match information between the two systems. This is an example of a:

This scenario illustrates a data comparability issue because it involves two different systems that use distinct identifiers for the same customers, leading to challenges in correlating and matching their information. When data is maintained in separate silos with varying identifiers—such as using a federal tax ID in one instance and a customer number in another—it becomes difficult to integrate or compare the data effectively. This lack of uniformity means that even though the datasets may be accurate on their own, there's no straightforward way to align them to analyze customer behavior, assess their billing against sales, or derive insights that depend on a comprehensive view of each customer’s data.

Data accuracy issues would pertain to the correctness of the data itself within each system, while data relevance considerations focus on how pertinent the data is to the particular analysis being conducted. Data completeness refers to whether all the required data is present. However, none of these concepts adequately capture the core problem here, which is the inability to effectively compare and relate the two distinct datasets due to the differing identifiers. Thus, the most fitting description of the issue at hand is indeed a data comparability issue.