Glossary · 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.
- Accuracy22%
- Completeness18%
- Governance18%
- Consistency16%
- Connectivity14%
- Lineage12%
The weights behind the Truth Score™. They add up to 100.
In plain terms
Data quality is often treated as tidiness: no duplicates, no blank fields. A more useful definition is fitness for purpose. Data is good enough when it can do the job it is about to be given, and AI is a demanding job, because it acts on all of the data at once and doesn’t stop to ask.
The Truth Score™ breaks quality into six pillars: accuracy (is it correct), completeness (is it there), governance (is it owned and kept current), consistency (is it recorded the same way), connectivity (does it link up across systems) and lineage (can it be traced to a source).
The common mistake is to measure quality one system at a time. A CRM can score well on its own and a billing system can score well on its own, while the two disagree about which customers exist. The expensive problems usually live in those gaps, which is why we compare records across systems before saying anything about quality.
Quality also depends on the use case. A field that is irrelevant to lead scoring can be the one that blocks a cash-flow forecast. That is why a quality score is read alongside a Leverability read, play by play.
- 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 quality, answered.
The questions people ask about data quality, answered in plain English.
Book the callWhat are the dimensions of data quality?
We use six, weighted for AI: accuracy 22%, completeness 18%, governance 18%, consistency 16%, connectivity 14% and lineage 12%. Together they make up the Truth Score™.
How is data quality different from AI readiness?
Quality is whether the data can be trusted. Readiness adds the use case: whether the data can run a particular AI play. Data can be of decent quality and still not be ready for the play in front of you.
Can data quality tools fix this?
Tools can clean records inside a system. They rarely tell you whether your systems agree with each other, or whether the data can support a specific AI proposal. We don’t sell tools or fixes; we tell you what needs fixing and in what order.
Related terms
Keep reading.
- 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
- 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 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 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. Read the definition
Definitions are the easy part. Your own number is the useful one.
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.