Data Quality and Assurance

Assess your datasets in real time - carefully selected insights by me but explored and optimized by Gaby AI on the long run.

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.