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Spend an hour examining permitted database rows, not just the code and schema. The records can reveal historical patterns, unexpected values, and edge cases that help explain why a system behaves as it does. Use read-only access or a masked copy, keep queries light, and treat anything surprising as a clue to investigate—not as proof of how common a problem is.
Why inspect rows when you already have the code?
Code and schema describe what data a system is designed to accept and how the application expects to use it. The rows show what is actually present. Those two views may differ because the data has accumulated across migrations, changing business rules, or operational mistakes. A code path that looks unnecessarily defensive may make sense once you see an old value or an unusual record it was built to handle.
Asael Shinder’s October 1, 2026 article on DEV Community makes the case for asking a simple question: have you actually looked at the rows? The point is not to inspect everything or conduct a formal audit. It is to use a focused hour to notice patterns that code review alone may not surface.
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Start with data you are authorized to inspect. Prefer a masked copy when one is available. Work through the questions below, recording surprises as you go; the article does not prescribe a fixed schedule, SQL syntax, or sampling method.
#1 Best Overall
- Check the overall shape. Look at row counts and how they have grown, if that information is available. A sudden change in volume can help put a performance or behavior issue in context.
- Look for missing values. Check null frequency in columns that application code assumes are populated. A handful of exceptions may explain fallback logic or a bug that appears only for certain records.
- Compare older and newer records. Ask: what is the oldest record, and does it look different from the newest? Compare relevant fields rather than assuming today’s format or rules applied throughout the system’s history.
- Inspect status values. Note the most common values and look for values that the current code does not handle. A value can be rare today yet still matter if it remains on historical records.
- Look for unusually large per-customer counts. A customer with far more orders or related records than others may reveal a legitimate outlier, a data issue, or a case worth checking against the code.
- Write down and share the surprises. Bring two or three useful observations to the team. A colleague may know the migration, policy change, or operational event behind a pattern that is not documented.
What patterns can reveal
Old values that outlive the rules
A retired status may remain on older orders even though current application logic no longer uses it. Seeing that value can explain why a code path checks for a status that appears obsolete, or why an old record behaves differently from a new one.
Format changes across migrations
Shinder gives the example of phone numbers appearing in three formats after a 2021 migration. That kind of variation may indicate that records were created or transformed under different conventions. It is a reason to inspect how the application reads and displays the values, not to assume that every format is invalid.
Test data in production
Production test accounts that were not removed can stand out during a row review. Their presence may affect reports or application behavior, but identifying a suspicious account is only the first step: confirm its purpose with the people responsible before changing or deleting anything.
These are illustrative examples from Shinder’s article, not prevalence estimates or independently verified incidents. Their practical value is in showing how data can preserve traces of a system’s history.
Quick Recap
Rank #4
Keep the inspection safe and lightweight
- Stay within granted access. Use only the data and systems you have permission to inspect; request read-only access if needed.
- Protect customer information. Prefer masked data where possible, and do not copy customer records to an unapproved location just to make inspection easier.
- Avoid unnecessary load. Keep queries light, especially against a reporting database that other people depend on. This exercise does not require an exhaustive scan.
- Do not treat a clue as a conclusion. An outlier or unfamiliar value can prompt a useful question, but it does not establish its cause. Check with the team and relevant documentation before proposing a fix.
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