What is data accuracy?
Data accuracy is the most basic quality question: is the value actually right?
It is constantly under threat from decay - values that were correct when entered become wrong as the world changes - so accuracy is maintained, not achieved once.
Short answer
Data accuracy is whether the values in your records are correct and reflect reality - the right email, the current company, the true deal amount. It is a core dimension of data quality, distinct from completeness (whether fields are filled) and consistency (whether values are uniform). Accuracy erodes through data decay, and it is maintained through verification and enrichment.
Key takeaways
- Whether values are correct and reflect reality.
- A core dimension of data quality.
- Distinct from completeness and consistency.
- Eroded by decay, maintained by verification and enrichment.
Why it matters
Inaccurate data is actively misleading - worse than missing data, because people act on it. A wrong email bounces, an outdated company misroutes, a stale amount corrupts the forecast. Accuracy is what makes data trustworthy enough to act on, and it requires ongoing verification because it decays.
How Ardovo handles it
Ardovo maintains accuracy through verification and continuous enrichment that re-checks and refreshes values as they decay. Rook flags likely-stale data - a contact who probably changed jobs, an email that stopped resolving - so records stay accurate rather than silently drifting wrong.
Frequently asked questions
What is the difference between data accuracy and completeness?
Completeness is whether fields are filled; accuracy is whether the filled values are correct. A record can be complete but inaccurate - every field populated, but with a wrong email or an outdated company. Both are quality dimensions: completeness is about presence, accuracy is about correctness.
Why does data accuracy decay?
Because the real world changes - people switch jobs, companies rebrand or move, emails and phones get retired. A value that was accurate when entered becomes wrong as reality shifts, even though no one made an error. This decay, around 25 to 30 percent yearly for contact data, is why accuracy requires ongoing verification.
How do you maintain data accuracy?
Through verification that checks values are correct and continuous enrichment that refreshes them as they decay. Because accuracy erodes constantly, a one-time correction is not enough - ongoing re-verification, especially of contact details that decay fast, is what keeps records accurate over time.