Assess your datasets in real time: insights carefully selected by me, but explored and optimised by Gaby AI in the long run.
Data Quality Layers
- Accuracy: The data represents reality
- Completeness: All the required data is present
- Consistency: Data is consistent across different datasets and databases
- Reliability: The data is trustworthy and credible
- Timeliness: Data is up-to-date for its intended use
- Uniqueness: There are no data duplications.
- Usefulness: Data is applicable and relevant to problem-solving and decision-making
- Differences: Users know exactly how and where data differs
Addressing Problems in real-life
Testing and assurance should not be limited to the user's domain. One solution is to fine-tune variants of Gaby on personalised datasets, with API connections to up-to-date standards. For example:
- Confirming data is documented and reported to strict standards (like FDA, EMA and ICH-GCP guidelines). Even small errors could invalidate a study.
- Auditing data entries made by different scientists, etc.
- For pharmaceutical companies, a variant of Gaby AI should be built on an architecture centred on API routes that draw and test the evidence behind drug safety and efficacy.
Data insights & analytics
Make pretty charts and dashboard pages!
Defaults to 3 pages:
- Summary: Business Insights
- Data Quality Scores
- Data Science Team backpack
Extensions
- Project Manager Page (requires project management integrations / collaboration tools)
- Data Science Team Page