Catalog & Cocktails: The Honest, No-BS Data Podcast
data.world
317 episodes
5 months ago
<p>Catalog and Cocktails is an honest, no-BS, non-sales-y conversation about data and analytics. This is your unfiltered chat about everything interesting in data and metadata management, DataOps, architecture, and beyond. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from visionary leaders across the data space. </p>
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<p>Catalog and Cocktails is an honest, no-BS, non-sales-y conversation about data and analytics. This is your unfiltered chat about everything interesting in data and metadata management, DataOps, architecture, and beyond. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from visionary leaders across the data space. </p>
TAKEAWAYS - Building Trust in Your Data with Frances O'Rafferty
Catalog & Cocktails: The Honest, No-BS Data Podcast
6 minutes 4 seconds
7 months ago
TAKEAWAYS - Building Trust in Your Data with Frances O'Rafferty
Frances O'Rafferty, Senior Manager at Baringa, explore sthe critical gap between data-driven aspirations and decision-making reality. While many organizations claim to be "data-driven," leaders often revert to gut instinct when making crucial decisions. We'll examine how fragmented data experiences erode confidence and how a user-centric approach that brings together data, metadata, quality metrics, and lineage in one place can build the foundation of trust necessary for truly data-driven decision making. Frances shares practical insights on creating holistic data experiences that empower everyone across the organization to trust in their data and make confident, fact-based decisions.
Catalog & Cocktails: The Honest, No-BS Data Podcast
<p>Catalog and Cocktails is an honest, no-BS, non-sales-y conversation about data and analytics. This is your unfiltered chat about everything interesting in data and metadata management, DataOps, architecture, and beyond. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from visionary leaders across the data space. </p>