What is dirty data?
Dirty data is the umbrella term for everything wrong with a database: duplicates, gaps, staleness, and inconsistency all count.
It is costly precisely because it is invisible until something breaks - a bounced campaign, a misrouted lead, a report that does not add up.
Short answer
Dirty data is any CRM record that is inaccurate, incomplete, duplicated, outdated, or inconsistently formatted. It includes wrong emails, missing companies, duplicate contacts, stale records, and mismatched value formats. Dirty data breaks routing, inflates reports, wastes rep time, and slowly erodes trust in the whole system.
Key takeaways
- Covers inaccurate, incomplete, duplicate, stale, and inconsistent records.
- Usually invisible until it causes a downstream failure.
- Erodes trust in the CRM, causing reps to work around it.
- Prevented with validation, dedupe, and enrichment rather than periodic cleanups.
Why it matters
Dirty data has a compounding cost. One bad record wastes a little time; a database full of them breaks automation, corrupts forecasts, and pushes reps back to spreadsheets. The damage is rarely dramatic, which is what makes it dangerous.
How Ardovo handles it
Ardovo attacks the sources of dirty data - duplicates, gaps, and inconsistency - at the point of entry, and Rook cleans what slips through. The database stays clean enough that people trust it, which is the whole point.
Frequently asked questions
What are examples of dirty data?
A contact with a bounced email, two records for the same person, an account with no industry, a lead created two years ago with no activity, and a country field with five different spellings of the same country. All of it is dirty data.
What does dirty data cost a business?
It wastes rep time on bad contacts, misroutes leads, sends duplicate outreach, and produces reports leadership cannot trust. The bigger cost is erosion of confidence: once people stop trusting the CRM, they stop using it and revert to spreadsheets.
How do you prevent dirty data?
Stop it at the source with validation rules, required fields, duplicate blocking, and enrichment, rather than cleaning periodically. Prevention at the point of entry is far cheaper than repeated cleanup projects.