New knowledge captured that is not covered in your template may require a careful revision of the template so it accurately reflects your completed Data Cards.
This is especially true if you are documenting several datasets with similar modifications or additions to their Data Cards. Consider revisiting your template with a broader group of stakeholders using activities from the Data Cards Playbook.
Copy with care.
A simpler route to creating a Data Card is to model your responses after the Data Card of a similar dataset. This is common practice when documenting a newer version or a subset of a dataset. While this is efficient, it becomes very easy to copy incorrect or outdated information unknowingly. Verify the correctness, completeness and recency of any Data Card that you use as a reference.
Maintain parity between Data Cards and Templates.
Sometimes a Data Card that is overly efficient in its creation could also under-represent your dataset. Working off copies of Data Cards or Model Cards that describe upstream sources make it more efficient to produce these at scale, but these may be missing sections that are relevant to your dataset or model. Always cross-verify your examples against a fresh template. Periodically check your template to see if any questions that were previously considered irrelevant have become relevant.