### Q1: What are the key factors that define data quality in AI/ML initiatives?  
### Q2: How can a fintech company leverage large datasets to improve the accuracy of its credit scoring model?  
### Q3: What are the potential consequences of using outdated or biased data in an AI model for loan assessments?  
### Q4: In what ways can the low-code approach be utilized to enhance data governance in AI/ML projects?  
### Q5: What specific actions can organizations take to implement effective data quality management practices?  
### Q6: How does poor data quality lead to increased costs and resource allocation challenges in AI/ML projects?  
### Q7: How can you effectively communicate the importance of data quality to stakeholders in your organization?  
### Q8: What strategies can you employ to ensure that your AI/ML projects are built on a solid foundation of quality data?  
### Q9: Reflecting on past experiences, how can clear communication about data governance impact decision-making in your business context?