# Data Quality and Assurance ## Data Quality Layers 1. **Accuracy:** The data represents reality 2. **Completeness:** All the required data is present 3. **Consistency:** Data is consistent across different datasets and databases 4. **Reliability:** The data is trustworthy and credible 5. **Timeliness:** Data is up-to-date for its intended use 6. **Uniqueness:** There are no data duplications. 7. **Usefulness:** Data is applicable and relevant to problem-solving and decision-making‍ 8. **Differences:** Users know exactly how and where data differs ## Addressing Problems in real-life Test and Assurance should not limit to the user's domain. A solution for this is to fine-tune variants of Gaby with personalized datasets and equipped API connections to updated standards. For example: * Confirm data is documented and reported according to strict standards (like FDA, EMA, ICH-GCP guidelines). Even small errors could invalidate a study. * Auditing data entries by different scientists etc. * For pharmaceutical companies, variant of Gaby AI should be developed over an architecture centered around the API routes with drawing and testing for evidence behind drug safety and efficacy. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://whoamimi.gitbook.io/blog/projects/readme-1/gaby-ai-agent-features/data-quality-and-assurance.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.