In order to continue developing and applying AI, we will need to increase ease of access to data in a wider range of sectors with following principles: 1. Data Cleaning and curation to improve trust and ease around sharing data 2. Making research data more appropriate for machine readability, insights and report generation 3. Supporting text and data mining as a standard and essential tool for research. This speech focuses on how clean and accurate data can simplify cloud migration projects and enhance AI performance and results with more accurate precisions. By curating data properly before migrating from any source systems to destination (AWS or Azure or GCP), the overall process becomes more reliable and efficient, allowing for quicker completion with fewer unexpected / unknown issues. Clean and trusted data reduces errors, minimizes downtime, and ensures faster and most successful migration. The clean data layer forms the solid foundation to run AI models effectively and efficiently. When data is accurate and well-organized, AI models can deliver more reliable insights and make smarter decisions for improved outcomes.