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 runs independently from the clock.
What that pace means
The clock and the headline pace figures use a linear first-year average. The 24-month chart below compounds annually and is labeled separately.
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 paths for the same starting cohort. The hygiene path assumes a quarterly correction of the selected share of stale records.
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 scenarios come directly from ELP Data's April 2026 vendor-published audit of its own database. The middle 23.5% is the midpoint of its reported range. As a narrower comparison, Lusha's September 12, 2026 rerun found 12.25% of US sales leaders changed roles over 12 months. Its earlier August run found 12.6%. Role changes are only one way a record becomes stale.
This tool begins with a cohort assumed accurate today. The editable cause mix is for storytelling and does not come from either source. It does not inspect your database.