Waters Wrap: The path to generative AI is paved with solid data practices
While large language models are likely to proliferate, those that can develop a solid data infrastructure of taxonomies, ontologies, data sourcing, mapping and lineage will be the ultimate winners, Anthony says.
In the highly regulated capital markets, there’s a dichotomy between things you “want to do” and things you “need to do”. Banks and asset managers want to deploy high-powered, machine-learning-driven analytics tools, but they need to solve ever-greater regulatory reporting needs. But does it have to be this way? Is there a way to bridge the gap?
(I hope you’ll follow along with me as this is a bit of a think piece. And I’d love feedback to see how I can improve this theory.)
First, let’s look
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