How has my data quality improved over time?
You cleaned up your data last quarter. But has it stayed clean? Are your match rates improving or quietly degrading? Here's how to track data quality over time, and why a one-time cleanup is never enough.
The short answer
Is your data getting better?Data quality is not a one-time project. It is a trend line. Without monthly tracking of your match rates, orphan counts, and gap patterns, you have no way to know if last month's cleanup held or if new problems are creeping in.
Data quality degrades by default unless you actively maintain it
Every new customer who signs up through a different channel, every email that changes, every team member who enters data slightly differently introduces a potential mismatch. Without continuous monitoring, your data quality silently decays.
You might do a big cleanup in January. Match rates go from 55% to 78%. Great. But by April, new records have been added without proper matching, an integration hiccup went unnoticed for two weeks, and your match rates are back to 62%. Without the trend line, you would not know you lost those gains.
Tracking data quality over time turns a vague feeling (“our data is messy”) into a measurable trend you can manage.
The four numbers to track month over month
- Overall match rate. The percentage of records that can be linked to at least one other system. Rising means improvement. Falling means new gaps are forming.
- Exact match percentage. The percentage of matches that are high-confidence (email-based). This is the quality of your matches, not just the quantity.
- Orphan count. The number of records that exist in only one system. This should shrink over time if your data practices are improving.
- Weakest pair match rate. Your weakest system pair sets the floor for cross-system insights. Tracking it shows whether you are improving your worst connection.
How to track data quality trends in a spreadsheet
The manual approach requires repeating the export-and-match process every month and recording the results.
Create a tracking sheet with one row per month. Record the overall match rate, exact match percentage, orphan count, and weakest pair rate. After three months, you have the beginning of a trend. After six months, you can see whether your processes are actually improving things.
The problem is that the underlying export-and-match work takes 3-5 hours each time. Multiply that by 12 months and you are looking at 40-60 hours per year of spreadsheet work just to track a trend line.
Most businesses attempt this once or twice, then stop. The effort-to-insight ratio is too high. By the time you finish the spreadsheet, the data has already changed.
Or track your data quality trend automatically
Bottomline recalculates your match rates, orphan counts, and gap patterns every month. It stores the history so you can see the trend over time without doing any manual work.
In this example, a data cleanup in January drove match rates from 55% to 78%. A small dip in February (new records added without matching) was caught and corrected. By April, the rate has steadily climbed to 84%. Without the trend, the February dip would have gone unnoticed and likely worsened.