As enterprises race to implement AI strategies, the crucial step of preparing data could be creating a major hurdle.
During many major tech conferences and events in 2024, talk of implementing artificial intelligence was a common theme as IT leaders are tasked with creating new GenAI tools for business. But a common refrain was the need to prepare data for machine learning.
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“Data fabric and data mesh are like the Montagues and Capulets, or the Hatfields and McCoys,” says Kendall Clark, co-founder and CEO of data firm Stardog. “It’s like a frenemy rivalry … they are so similar that nobody can tell them apart, but it’s the small differences.”
Because data fabric is so similar, Clark says clients will request data fabric but what they are really describing is data mesh architecture. So, it’s more important to have a strong understanding of your businesses unique data needs. “The labels really aren’t that important.”
“You don’t have to get the decision right, you just have to choose,” Clark says of picking a new data architecture for GenAI implementation. “I would start by picking a super critical, important problem that will make a real difference for your organization. Something that will make your business save more money, manage risk, make more money, make people more productive – those are the keys to driving the business forward. You need to pick one as your rallying point.”
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