It's a common misconception that simply feeding AI systems with data will automatically yield amazing results. While this might work initially, data scientists often find themselves spending more and more time cleaning and preparing data as projects progress.
High-quality data is essential for AI applications. This means manually cleaning and organizing data to ensure it's accurate, complete, and understandable by machines. Additionally, providing extra context like definitions and labels helps AI systems learn more effectively and perform tasks better.
Preparing data early on in the process can save time and effort. Imagine giving a chef pre-prepared ingredients instead of raw groceries; it's much more efficient and ensures a faster meal. The diagram below outlines six key principles for ensuring data is ready for AI use. This whitepaper will discuss each principle in detail.
