# Data Quality Indicators

- [Introduction - Indicators of Data Quality](https://docs.telm.ai/academy/data-quality-indicators/introduction-indicators-of-data-quality.md): This chapter focuses on six key data quality indicators: completeness, uniqueness, timeliness, validity, accuracy and consistency.
- [Selecting Data Quality Indicators](https://docs.telm.ai/academy/data-quality-indicators/selecting-data-quality-indicators.md)
- [Completeness](https://docs.telm.ai/academy/data-quality-indicators/completeness.md): Completeness,  is one the most basic and but crucial indicator of data quality
- [Uniqueness](https://docs.telm.ai/academy/data-quality-indicators/uniqueness.md)
- [Freshness](https://docs.telm.ai/academy/data-quality-indicators/freshness.md): Data thats been used is current and not stale and unreliable
- [Validity](https://docs.telm.ai/academy/data-quality-indicators/validity.md)
- [Accuracy](https://docs.telm.ai/academy/data-quality-indicators/accuracy.md): One of the most complex yet critical data quality indicator
- [Consistency](https://docs.telm.ai/academy/data-quality-indicators/consistency.md)
- [Data Lineage](https://docs.telm.ai/academy/data-quality-indicators/data-lineage.md): At Telmai we believe that even though not a Data Quality Indicator, Lineage is plays an important part in Data quality


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