AI-ready data is much more than the old adage “garbage in, garbage out.” Of course, no one wants
garbage, but as another saying goes, “One man’s trash is another man’s treasure.” The key is knowing
what you need for a specific initiative but also knowing what you’ve got. Data must be evaluated,
managed and governed, including detailed labeling and publishing. Those last two are the key to
reuse, the holy grail of effective and efficient AI.
To use a cooking analogy, making data AI ready is more than just putting together a tossed salad. In
the kitchen, raw ingredients need to be prepared for specific recipes. Potatoes might need to be
sliced, diced or grated depending on what you’re making. But before you even get to that step, you
need to find the potatoes. You’ll also likely need to clean them. And, you’ll need to find the other
ingredients that go with them. Ingredients must also be labeled: You’d not like to mistake the sugar
for the salt or the smoky paprika for the spicy cayenne pepper.
AI-ready data is like those prepared ingredients, ready to be baked into the AI model. At the recent
Snowflake Summit, we announced features of the AI Data Cloud that will address the key
characteristics of AI-ready data.