How should data applications help with data collection, cleaning, and visualization?
The success of AI transformation depends on people, processes, data, and technology, not just on models.
How are incomplete data, duplicates, and different structures across systems handled in practice?
Without high-quality, consistent, and trustworthy data, AI can lead to incorrect results or hallucinations.
Why is data quality more important for AI than the model itself?
In practice, incomplete data, duplicates, incorrect codes, and different data structures across systems and countries occur.
What is the benefit of data governance for the reliability of data flows?
A data application can combine scraping, LLM, data cleaning, and visualization into a single flow.
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