A useful data management report should answer operational questions. It should not include every possible table. A typical weekly report may include:

1. Report metadata: export date, report date, project, script version.
2. Enrollment summary by site and time.
3. Data completeness for priority fields.
4. Open queries by site, priority, and age.
5. Overdue follow-up outcomes.
6. Critical data inconsistencies.
7. Actions required before the next review.

The report should distinguish between information and action. A table showing all variables and all missing values may be informative but overwhelming. A report that highlights critical missing outcomes, unresolved severe adverse event queries, and sites with delayed entry is more actionable.
The following code creates report metadata:

report_metadata <- tibble(
project = “Clinical Research Data Management Practice Study”,
data_export_date = as.Date(“2026-06-01”),
report_generated = Sys.Date(),
prepared_dataset = “data_clean/prepared_participant_dataset_2026-06-01.csv”
)

This metadata can be printed at the top of the report. It helps prevent confusion when multiple report versions exist.