Glossary · 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.
One customer, recorded three ways. A model sees three different things.
In plain terms
Consistency asks whether the same thing is recorded the same way. It carries 16% of the Truth Score™.
Inconsistency is the quiet failure. “Ireland”, “IE” and “Republic of Ireland” are one country to a person and three to a model. Dates written day first in one system and month first in another can make a March renewal look like one in December. A status of “Closed”, “closed-won” and “Won” splits a single outcome into three.
The pillar checks four things: format standardisation (the same field written the same way), cross-field rules (a closed deal has a close date, a leaver has a leave date), temporal sense (nothing ends before it starts) and value ranges (discounts between 0 and 100%, not 1,500).
Consistency across systems matters as much as within one. If the CRM and the ledger use different customer categories, every report that joins them is quietly wrong, and so is any AI trained on the join.
- 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 consistency, answered.
The questions people ask about data consistency, answered in plain English.
Book the callIs consistency the same as accuracy?
No. A value can be accurate and inconsistent: “IE” and “Ireland” are both correct. The problem is that systems and models treat them as different things.
Why does consistency matter for AI?
Models group and compare values. Inconsistent values split one group into several, which weakens the signal and can make a model confidently wrong.
Can consistency be fixed automatically?
Much of it can, once the rules are agreed. The roadmap names the rules, the owner and the test for done; your team or any outside firm does the work.
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
- Method Cross-system reconciliation Cross-system reconciliation is matching records between systems, such as CRM and billing, or HR and the CRM, to find where they disagree. Every system can look clean on its own; the truth is in the gaps between them. 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 consistency. 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.