The 3 Data Principles for Successful AI Implementation

Hear from our VP of Data Engineering Daniel Zagales, as he shares the 3 data principles for successful AI implementation

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Video transcript

The three biggest trends I've been seeing as people go to implement all these new and great AI models and implementations is really the core principles of data. One, security.

Two, quality. And three, availability.

So we've been seeing a lot of people who are already ahead of the game, who have been really relying on those principles from day one, really either be ready to propel into the future with AIML, or people who are really just getting into that space, highly focusing on those three areas.

If your data is not secure, then really your LLMs or people interacting with their data can get to data that you don't want. If it's not quality data, kind of the same thing. Right? You your data can come out, in a way that you may not mean for it to come out. And then lastly, if your data is not available, a lot of these things just won't really ring true in regards to the LLMs. You You may not be able to get the quality that you want or, be able to surface the type of models that you want if it's not readily available for you.

When I think about the future of work for our customers, I get really excited for what that means for them, both on the engineering side, where they may be using co pilots and things that really drive forward their productivity and really enhance the way, they produce work back for their business. And then the other side, on the business, it's really exciting to see how quickly people can start using AI models if the data underneath is there to be able to move faster in their day to day, engage better with their end customers, and really drive forward their their business based on those assumptions.

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