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This week in data: What the heck is data observability?

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Generative AI and the path to predictive analytics


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What is data observability (really)? And how are you supposed to plan your generative AI budget? This week, we learned that just a small number of CIOs spend a significant amount on gen AI and that Morgan Stanley predicts 15 to 20% enterprise adoption within 3 years. 

What does this mean for your 2024 gen AI budget? Many of you have already chimed in with comments and voted in the generative AI LinkedIN poll. If you’re going to TED AI this week, Bruno is happy to debate this live there!

This week’s carcast tackles:

1) Generative AI in the enterprise: Identifying use cases for enterprise AI and why trust and data quality are your competitive moat are debated by Michael Krigsman of CXO Talk

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An exclusive invite-only evening of insights and networking, designed for senior enterprise executives overseeing data stacks and strategies.

 


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2) The State of AI Report 2023: Air Street Capital published its most recent research on AI investment.  Among the insights, gen AI apps have had a breakout year across image, video, coding, voice or copilots for everyone, driving $18 billion of VC and corporate investments. Also, 70% of the most-cited AI papers in the last 3 years have authors from U.S.-based institutions and organizations.  

3) What the heck is data observability? Gen AI needs sound data infrastructure to work. Our friend Sanjeev Mohan explains that the industry needs DataBizOps, a way to “optimize” cloud in the context of value creation. I’m a big believer in that concept. In fact, just a year ago I wrote about data mesh and why you should care.

This week’s CarCast also includes the best interview question by Peter Thiel and a quick take on Silicon Valley legend and former Stripe COO Claire Hughes Johnson’s book Scaling People.

Bruno Aziza is a technology entrepreneur and partner at CapitalG, Alphabet’s independent growth fund.

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