Data Quality
Data quality is the composite measure of how trustworthy a dataset is — accuracy, completeness, consistency, freshness, validity, uniqueness — and it makes or breaks every downstream automation.
What is data quality?
Data quality is a multi-dimensional property of a dataset. Industry frameworks usually call out six dimensions: accuracy, completeness, consistency, freshness (timeliness), validity (conforms to rules), and uniqueness (no dupes). High quality means high marks across all six; weakness in any one shows up as broken automations and unreliable reporting.
Why it matters
- AI is only as good as its training and runtime data — bad quality means confident, plausible-sounding wrong answers
- Sales productivity is gated by data quality more than by tooling
- Compliance and audit risk both rise with poor quality
How to measure it
- Define KPIs per dimension (e.g. % records with valid email, % records under 90 days old)
- Sample-audit monthly against a small reference set
- Trend the KPIs — quality degrades silently if not watched
How TexAu helps
Use TexAu workflows to enforce quality rules continuously: verify, dedupe, normalize, and refresh on a schedule so quality stays high without a quarterly cleanup project.
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