AI and Analytics Success is All About Architecture
Is Your Architecture a Marvel or a Disaster?
Two of my favorite streaming shows are Engineering Marvels and Engineering Disasters.
Engineering Marvels “reveals the extraordinary feats of engineering inside the world’s most spectacular man-made constructions.” Each episode identifies significant challenges and showcases how innovative engineering design and execution overcame them.
Engineering Disasters takes the opposite tack, with each episode examining something that went seriously wrong, such as a bridge failure, a dam rupture, or a building collapse. Generally, these were due to errors of omission caused by unforeseen factors that the engineers failed to consider or errors of commission where missed requirements, incorrect assumptions, mistaken calculations, or faulty materials led to a catastrophic outcome.
Like Engineering Marvels, building an architecture that supports today’s and tomorrow’s AI and Analytics requirements requires extraordinary feats of engineering. And like Engineering Disasters, there are many ways to fail.
How to Build Marvels and Avoid Disasters
A successful AI and analytics architecture is critical to today’s technocentric business models. Failure is not an option.
To help you engineer your way to AI and Analytics Architecture success, we’ve brought together a panel of experts in The Jam Session: AI and Analytics Success is All About Architecture on July 12, 2024, at noon ET.
Join me, fellow Insight Jam Expert John Santaferraro, CEO and Analyst of Ferraro Consulting, Roy Hasson, VP of Product and Marketing at Upsolver, and Andrew Madison, Developer Advocate and Evangelist at Dremio, for a lively interactive session to explore five critical questions on this topic.
AI and Analytics Architecture: Five Things You Need to Know
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What business requirements stress AI and Analytics architectures the most today?
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What do “Engineering Marvel” AI and Analytics architectures do extraordinarily well?
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Where do “Engineering Disaster” AI and Analytics architectures typically fail?
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How can recent technology innovations improve your odds of engineering a successful AI and Analytics architecture?
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Which reference architectures and other best practices provide jumpstarts that can accelerate progress?
