Sports analytics requires video, scouting textual reports, streaming data, numerical results of games. How can these types of analytics be accomplished? Where does Generative AI fit? Enter the data lakehouse. Ari Kaplan, the real money ball guy, will share his experience with Tim and Juan.

Sports analytics requires video, scouting textual reports, streaming data, numerical results of games. How can these types of analytics be accomplished? Where does Generative AI fit? Enter the data lakehouse. Ari Kaplan, the real money ball guy, will share his experience with Tim and Juan.

Catalog and Cocktails is the honest, No-BS, non-sales-y podcast about data and analytics. This is your unfiltered chat about everything interesting in data and analytics. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from thought leaders, practitioners and executives across the data and analytics space.

Catalog and Cocktails is the honest, No-BS, non-sales-y podcast about data and analytics. This is your unfiltered chat about everything interesting in data and analytics. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from thought leaders, practitioners and executives across the data and analytics space.

Catalog & Cocktails: The Honest, No-BS Data Podcast

What does Data Lakehouse, Sports and Generative AI have in common? with Ari Kaplan

Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic joins Juan and Tim to discuss how to tie data and AI work to business strategy.

The first basic question: what does the organization want to look like in three to five years, and what has to happen to get there? Platforms and models only matter if they serve that answer. And how do you measure? Setting a baseline is hard, slow work, and teams often skip it to start building. Without one, it is hard to tell whether the work is worth doing, and harder to ask that question once the build is underway.

We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced. 

Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic joins Juan and Tim to discuss how to tie data and AI work to business strategy.
The first basic question: what does the organization want to look like in three to five years, and what has to happen to get there? Platforms and models only matter if they serve that answer. And how do you measure? Setting a baseline is hard, slow work, and teams often skip it to start building. Without one, it is hard to tell whether the work is worth doing, and harder to ask that question once the build is underway.
We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced.&nbsp;

Start With the Business Metric, Not the AI with Richard Masters

This is the takeaway episode with Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic where he joins Juan and Tim to discuss how to tie data and AI work to business strategy.

We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced, and so much more. 

This is the takeaway episode with Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic where he joins Juan and Tim to discuss how to tie data and AI work to business strategy.
We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced, and so much more.&nbsp;

TAKEAWAY Start With the Business Metric, Not the AI with Richard Masters

In this special rant episode, Sonny Rivera joins Juan and Tim during a live recording and rant about AI-generated dashboards, semantic layers, and what it takes to trust an answer. The situation: dashboards slop is easy to build which makes foundation matter more, not less. We discussed: 
Building a dashboard was never the hard part. Being able to explain where a number came from is.
Speed without a governed foundation produces wrong answers faster. As Sonny put it, “fast and wrong is still wrong.”
Modeling data is really modeling the business. The job has not changed. How it gets done is changing.
A single source of truth did not hold up. Favors an interoperable set of semantic layers and context, with shared definitions where people actually care.
Logic written in the moment, in a dashboard or a pipeline, often never makes it back into the semantic layer. Closing that loop is the gap to fix.
Friction between teams is a sign that a definition matters. Make the differences visible rather than forbidding them.
Trust is what data leaders can least afford to lose, which is why testing belongs in dashboards too.
Writing code is no longer the slow part. Product management and testing are becoming the bottlenecks.

In this special rant episode, Sonny Rivera joins Juan and Tim during a live recording and rant about AI-generated dashboards, semantic layers, and what it takes to trust an answer. The situation: dashboards slop is easy to build which makes foundation matter more, not less. We discussed:&nbsp;
<ul>
<li aria-level="1">Building a dashboard was never the hard part. Being able to explain where a number came from is.</li>
<li aria-level="1">Speed without a governed foundation produces wrong answers faster. As Sonny put it, &ldquo;fast and wrong is still wrong.&rdquo;</li>
<li aria-level="1">Modeling data is really modeling the business. The job has not changed. How it gets done is changing.</li>
<li aria-level="1">A single source of truth did not hold up. Favors an interoperable set of semantic layers and context, with shared definitions where people actually care.</li>
<li aria-level="1">Logic written in the moment, in a dashboard or a pipeline, often never makes it back into the semantic layer. Closing that loop is the gap to fix.</li>
<li aria-level="1">Friction between teams is a sign that a definition matters. Make the differences visible rather than forbidding them.</li>
<li aria-level="1">Trust is what data leaders can least afford to lose, which is why testing belongs in dashboards too.</li>
<li aria-level="1">Writing code is no longer the slow part. Product management and testing are becoming the bottlenecks.</li>
</ul>

What does Data Lakehouse, Sports and Generative AI have in common? with Ari Kaplan

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