Data Quality
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The degree to which data is fit for its intended use – accurate, complete, consistent, and current enough to support the decisions built on it.
What is Data Quality?
Data quality is the degree to which data is fit for its intended use: accurate, complete, consistent, current, and relevant to the question it is supposed to answer. The same dataset can be high quality for one purpose and useless for another.
For AI systems the stakes are direct. A model trained on flawed data learns the flaws, and no amount of tuning downstream repairs what the training data got wrong.
Dimensions of Data Quality
- Accuracy: the data correctly describes the real-world things it represents.
- Completeness: required values are present, without gaps or missing fields.
- Consistency: the same fact does not appear in two contradictory forms across systems.
- Timeliness: the data is current enough for the decision at hand.
- Relevance: the data actually bears on the intended use.
Quality is always judged against a purpose. An address list that is 95% accurate might be fine for a newsletter and unacceptable for legal notices.
Data Quality vs Data Integrity
Data integrity asks whether data has stayed accurate and unaltered through its lifecycle; data quality asks whether it is fit for a purpose. A database can have perfect integrity (nothing corrupted, every rule enforced) and still hold outdated, irrelevant, low-quality data.
| Data Quality | Data Integrity | |
|---|---|---|
| Core question | Is the data fit for its purpose? | Has the data stayed accurate and unaltered? |
| Measured against | The intended use | The data’s correct original state |
| Typical failure | Outdated, incomplete, or irrelevant records | Corruption, unauthorized changes, broken references |
| Main remedies | Cleaning, enrichment, better collection | Validation rules, access controls, checksums |
Example of Data Quality
A hospital builds a model to predict which patients will be readmitted within 30 days. The historical records look plentiful, but a third are missing the discharge-medication field, and two clinics record blood pressure in different units.
The model trains without complaint – it simply learns from wrong and missing values. Its predictions skew against patients from the clinics with sparser records. The failure traces back not to the algorithm but to completeness and consistency, two quality dimensions no one checked before training.
Related AI terms: Data Integrity · Data Lineage · Data Drift · Ground Truth · Big Data
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About