Data migration work rewards patience. A strong review produces a clean spreadsheet with reliable meaning for the next system and the people who depend on it.

Here is the lightweight review sequence I use as a starting point.

1. Confirm the file’s purpose

Before changing a column, identify what the file is supposed to support. Is it a load file, a reconciliation report, a reference table, or a working extract? The same field can have different rules depending on its role.

Write down the expected output and the fields that affect it. This creates a boundary for the cleanup and reduces the temptation to “fix” information that is outside the task.

2. Look for structural problems

Check headers, duplicate rows, blank required fields, unexpected formats, and inconsistent naming. Use filters and conditional formatting to make patterns visible, then verify the pattern before changing the data.

Simple functions such as COUNTIF, VLOOKUP, and IF can help identify duplicates, unmapped values, and exceptions. These formulas surface the rows that need human judgment.

3. Test the business logic

Clean-looking data can still produce the wrong result. Review the relationships between fields: earning or deduction codes, effective dates, category values, totals, and any rule that determines how a record is interpreted.

Ask what should happen when a value is missing, duplicated, or outside the expected range. Those edge cases often reveal the gap between a file that is technically valid and a file that is operationally ready.

4. Reconcile before handoff

After changes, compare the record count and important totals before and after the cleanup. Review a sample of unchanged rows, corrected rows, and exceptions. Keep a short change log so the next person knows what was adjusted and why.

The best migration preparation is quiet. It prevents avoidable problems from appearing at load time or after go-live, when the cost of correction is higher and the explanation is harder.

Applied principles

  • Understand the output before editing the input
  • Use formulas to find questions that need judgment
  • Test edge cases and business rules
  • Leave a clear handoff record

Composite, anonymized examples. No real member information. Views are my own and don't represent HCSC (HealthSpring).