A company is adopting a new ETL tool that leverages AI for data quality improvement. What are key factors to consider in this transition?
- Data Warehousing, Database Normalization, Agile Project Management, Data Encryption
- Integration with existing systems, Scalability, AI Model Performance, Data Security
- System Maintenance, Front-End Development, API Integration, Network Latency
- User Interface Design, Data Visualization, ETL Syntax, Cost of Implementation
When adopting an ETL tool with AI for data quality improvement, key factors include integration with existing systems, scalability to handle large datasets, AI model performance, and ensuring robust data security measures are in place. Integration and scalability are crucial for a seamless transition, while AI model performance and data security are essential for effective data quality improvement.
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