Data Quality Index, or DQI, is a measurable score that shows how reliable, complete and usable production data is for analysis, reporting and decision-making.
In a manufacturing environment, DQI helps assess whether data from machines, operators, systems and processes can be trusted. A high DQI means the data is complete, accurate, timely and accessible enough to support KPIs, operational reviews, quality checks and improvement actions.
DQI can be calculated using several quality dimensions, such as:
| Metric | What it measures |
|---|---|
| Data completeness rate | Percentage of records with all required fields populated |
| Data accuracy rate | Percentage of values that match verified sources |
| Timeliness | Delay between data generation and data availability |
| Data freshness | Percentage of data updated within a defined time window |
| Error rate | Number of data issues, such as duplicates, missing values or nulls |
| Availability | Percentage of time data pipelines are operational and accessible |
A practical DQI model can combine these indicators into one score, for example:
DQI = weighted score of completeness, accuracy, timeliness, freshness, error rate and availability
For manufacturers, DQI is useful because poor data quality can distort OEE, downtime analysis, quality reporting, batch records, maintenance planning and production decisions.