Glossary · 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.
In plain terms
Accuracy asks the simplest question about a record: is it right? Is the email address real, is the date possible, is the amount plausible, and is this the only record for this customer?
It carries the heaviest weight in the Truth Score™, 22%, because AI acts on values directly. A person reading a CRM knows that “test@test.example” and a close date in 1900 are junk. A model doesn’t; it learns from them, and an agent acts on them.
Inaccuracy usually comes from ordinary operations: duplicates created when two people enter the same customer, placeholder values typed to get past a mandatory field, imports that shifted a column, and amounts entered in the wrong currency or unit. None of it looks dramatic in any single record.
Duplicates matter most for AI. Three records for one customer split its history three ways, so a churn model sees three thin customers instead of one at risk, and a forecast can count the same deal twice. Finding them needs fuzzy matching, because duplicates are rarely spelt the same way.
- 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 accuracy, answered.
The questions people ask about data accuracy, answered in plain English.
Book the callHow do you find duplicates that are spelt differently?
With fuzzy matching on names, normalised company names (stripping Ltd, Inc, GmbH and the like), domains and IDs, then a manual check of samples before anything reaches the memo.
Why does accuracy carry the most weight?
Because AI acts on values directly and at scale. A wrong value that a person would ignore becomes training data for a model or an instruction for an agent.
Is accuracy the same as completeness?
No. Completeness asks whether a value is there; accuracy asks whether it is right. A field can be filled on all records and still be wrong on many of them.
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
- Method Entity resolution Entity resolution is working out which records, in one system or across several, refer to the same real customer, person, supplier or deal, even when they are spelt differently. Read the definition
- The six pillars Data consistency Data consistency is whether the same thing is recorded the same way, across records and systems: formats, categories, units and dates that agree. 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 accuracy. 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.