STRUCTURED DATA EXTRACTION · Workflow idea
Read a selected structured data file, separate its repeating records, and arrange the values into clear table rows. Prepare a matching set of column headings and preview the result so a data owner can verify completeness and approve the destination before any shared file is created or changed.
01 / The starting point
A person-selected XML document containing repeated records and named fields
“Extract the selected records and fields, prepare a table with appropriate headings, and wait for a data owner to approve its contents and destination.”
02 / The work
Helps a team turn records from a structured document into rows that are easier to inspect and work with in a spreadsheet.
Confirm the file and the record group to extract, and make sure it is appropriate for the intended table.
Review the document structure and decide which named values should become columns and which items form rows.
Create clear headings from the selected fields and flag nested, missing, or unexpected values for review.
Place each selected record into a row and retain a source reference so the output can be checked against the file.
Compare the preview with the source, review blank or repeated values, and exclude fields that are not needed.
Require a data owner to approve the exact rows, headings, and shared destination before a sheet is created or populated.
03 / The result
A row-based table preview with source fields and record counts, held for approval before a shared spreadsheet is created or populated.
Reviews & approvals · Example outcome
A row-based table preview with source fields and record counts, held for approval before a shared spreadsheet is created or populated.
Source documents can contain sensitive details, unexpected fields, or malformed records. Check the file, field mapping, record count, and destination; require an authorized person to approve creating or changing the shared table.
04 / Make it yours
A few choices shape the work around your team.
Define which documents and repeating records may be processed and who is responsible for confirming the source.
Agree which values become columns, how missing or nested data is represented, and which fields must be excluded.
Choose an access-appropriate spreadsheet location and name who verifies the preview and approves writes.
Check a typical file, one with missing values, and one with unexpected structure; confirm nothing is written before approval.