Data Decay Clock
This is roughly how fast your data is going bad. Start with a contact count, choose an assumption, and watch a clean cohort age.
Runs in your browser. No CRM connection, uploaded records, trackers, or stored inputs. The share link contains numbers only.
Your database, roughly
Adjust the assumptions
The breakdown assigns one primary reason to each stale record. These shares are illustrative, not measured rates. They must add to 100%.
The clock is running
About one record every 9 minutes in real time.
A 45-second illustration of the kinds of fields that can change. It is independent of the estimate above. In Halloween mode, each little headstone represents eight estimated stale contacts on the clock.
What that pace means
Bad data can cost more than cleanup work
- Wasted outreach: paid sends, enrichment, and campaign effort spent on unreachable contacts.
- Sales time: researching a replacement contact or following up with the wrong person.
- Missed opportunity: a relevant buyer is missed, delayed, or routed incorrectly.
- Poor decisions: inaccurate segmentation, attribution, and forecasts.
Salesforce’s sales-ops discussion describes the productivity and forecasting effects.
24 months from a clean start
Two estimated paths for the same starting cohort. The hygiene path assumes a quarterly correction of the selected share of stale records; it is a scenario, not a forecast.
What do I do now?
- Check a sample where it matters. Inside your CRM, verify a small set of contacts from a list your team will actually use. Record which fields are wrong and how old those records are.
- Catch problems at the point of use. Re-verify high-value campaign and sales lists before outreach, and route bounces or known job changes into a correction workflow.
- Choose a cadence from your evidence. Compare segments and recheck them after a quarter. Use your measured error rate and correction effort to make the budget case.
Where the numbers come from
The 18% technology, 22–25% cross-industry, and 31% manufacturing figures come from ELP Data's 2026 vendor-published audit. They describe different cohorts and are not universal rates. The middle 23.5% is our midpoint of that reported range. For a narrower comparison, Lusha measured 12.25% annual role changes among US sales leaders, which does not measure every kind of stale field. Your database could differ substantially.
The real-time pace uses a linear first-year average: size × annual rate ÷ seconds per year. The 24-month chart compounds the annual rate. Every figure is an estimate from an assumed clean start. A job move can change several fields, so the editable breakdown is a storytelling allocation, not a measured cause mix. No record is checked here.