1. **Completeness**: Level of comprehensiveness, missing data, coverage - Does your data fulfill your expectations of what’s comprehensive? 1. **Reliability**: consistency, variability, uniqueness; synchronization across data stores; n. of conflicts - Does information in one place match relevant data elsewhere and is the data aligned? 1. **Timeliness**: On time, when needed, up-to-date - Is your information available when you first need it? 1. **Accuracy**: Reflection of reality, error rate, objectivity - How well does a piece of information reflect reality? 1. **Accessibility**: Validity; data is within range of definitions; usable (format) - Is this information in the right format and following the business rules, and is the data usable for the intended purpose? 1. **Accountability**: All data can be audited and traced back to its origins - Can data lineage be tracked as it moves from the source through different systems? 1. **Relevance**: The information is needed for its intended purpose - Do you, your clients, or the task at hand need this data? Take into account that these quality requirements vary across users and uses of data. Closest ref: https://www.precisely.com/blog/data-quality/data-quality-dimensions-measure Note that Integrity is a mix of Accuracy, Consistency, and Completeness.    Ref.: https://www.databricks.com/discover/pages/data-quality-management ## Others - Cost - Business Value (MAUs)