# Data integrity

Concept · [Technical basics](https://ecosystem.samcarlton.com/technical)

Also called: data quality, data accuracy, clean data

Whether data stays accurate, complete and consistent over time. AI tools and reports are only as reliable as the data they read.

**For example:** An [AI assistant](https://ecosystem.samcarlton.com/chatbot) keeps giving the wrong [office hours](https://ecosystem.samcarlton.com/office-hours) because three different hours are stored in the system. That is a data integrity problem.

## Connects to (6)

- [Data normalization](https://ecosystem.samcarlton.com/data-normalization): Making records consistent so the same thing is always written the same way, and, in database design, storing each fact in only one place.
- [Ground truth](https://ecosystem.samcarlton.com/ground-truth): The verified, correct answers or facts that an AI system's output is checked against.
- [Data governance](https://ecosystem.samcarlton.com/data-governance): The rules and responsibilities for how an organization's data is collected, stored, used and protected.
- [Backup](https://ecosystem.samcarlton.com/backup): A copy of data kept so it can be recovered if the original is lost or damaged.
- [Data literacy](https://ecosystem.samcarlton.com/data-literacy): The ability to read, question and use data: knowing what a field or number actually means, where it came from and what it can and cannot…
- [Data lineage](https://ecosystem.samcarlton.com/data-lineage): A record of where a piece of data came from and every step that changed it along the way.

Map view: https://ecosystem.samcarlton.com/data-integrity