### Q1: What are some key indicators to quickly assess the quality of data used in AI/ML projects?  
### Q2: How does the quantity of data enhance the predictive capabilities of AI/ML solutions in the finance sector?  
### Q3: Can you describe a complex scenario where poor data quality led to significant business implications in a fintech context?  
### Q4: How can low-code development tools streamline the integration of high-quality data into AI/ML models?  
### Q5: In what ways can businesses apply data-driven insights to improve customer engagement and satisfaction?  
### Q6: What are some challenges faced when scaling AI/ML solutions due to data quality issues, and how can they be addressed?  
### Q7: How can effective communication of data findings among team members enhance the decision-making process in a fintech environment?  
### Q8: In what ways can transparency in data usage improve trust with customers in AI/ML applications?  
### Q9: How can honing communication skills help in conveying the importance of data quality to stakeholders in finance?