Which of the following should be of GREATEST concern to an IS auditor when using data analytics?

If the data source lacks integrity, meaning the data is inaccurate, incomplete, or unreliable, any analysis performed on it will produce flawed or incorrect results, making the audit findings unreliable and potentially misleading. Data integrity is foundational for valid data analytics.

The open-source nature of data analytics software does not inherently mean it is unreliable or a greater concern than data integrity, as many robust open-source tools exist and are subject to peer review.

Irrelevant fields in a data set might make analysis more complex or inefficient, but they do not fundamentally undermine the reliability of the valid data that is present.

While auditors often prefer to extract data themselves for control, if the data extraction process is validated and reliable, not personally extracting the data is less of a concern than the integrity of the data itself.