Glossary · The six pillars
Data completeness
Data completeness is whether the fields an AI use case depends on are actually filled in, on enough records, for long enough, to be worth acting on.
- Customer nameRequired99%
- OwnerKey88%
- IndustryKey41%
- Renewal dateKey for churn62%
- FaxOptional7%
Average fill rate looks fine. The field churn prediction needs does not.
In plain terms
Completeness asks whether the value is there at all. It carries 18% of the Truth Score™.
An average fill rate across every field is close to meaningless. A CRM can be 90% complete overall and still have renewal dates on only a fraction of contracts, which is the one field a churn model or a renewal forecast can’t do without. Completeness has to be measured on the fields the decision depends on.
So we separate required fields, key fields for the AI play in question, and optional ones, and measure each. We also look at whole records: a customer with a name and nothing else is a record in the count but useless to a model.
History counts too. Many AI plays need enough past data to learn from: deals with outcomes, invoices with payment dates, tickets with resolutions. A system migrated eighteen months ago may hold complete records with no history behind them.
- See it in a real memoThe sample Truth Score ReadReal engine output on generated test data, not a client
- How we measure itThe method, including the capPillars, weights, bands and what is never guessed
- What happens to your exportsSecurity and data handlingExports only, NDA and data processing agreement first
Questions
Data completeness, answered.
The questions people ask about data completeness, answered in plain English.
Book the callWhat is a good completeness rate?
It depends entirely on the field and the use case. A missing fax number doesn’t matter; a missing customer ID on an invoice can block a whole forecast. That is why we measure the fields the decision needs, not an average.
Can AI fill in missing data?
It can guess. For a decision that will be acted on, a guessed value is often worse than a blank, because it looks real. We report what is missing and what it blocks.
Is a blank field always a problem?
No. Some fields are legitimately empty. We separate required, key and optional fields so a blank only counts against you where it matters.
Related terms
Keep reading.
- AI readiness Data quality Data quality is how far data is fit for the job it is about to do: correct, filled in, consistent, owned, connected across systems and traceable to its source. Read the definition
- The six pillars Data accuracy Data accuracy is whether the values in your records are correct: real emails, valid dates, plausible amounts, and one record per real customer rather than three. Read the definition
- The Truth Score Leverability read The Leverability read says which AI ideas your data can use now, which need fixes first, and which it can’t support at all, with the reason in your own records. Read the definition
- The Truth Score Truth Score™ The Truth Score™ is DataTruth’s score out of 100 for how far a company’s data can be trusted, built from six weighted pillars and placed in one of four bands, A to D. Read the definition
See how your data scores on completeness. Measured, not self-reported.
Exports from two or more systems, matched between them, every red flag checked by hand. A signed memo with your Truth Score and a Leverability read, within 5 working days of complete exports.
Starts with a 30-minute call. Commits you to nothing.