Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.

Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.

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

ServiceNow Podcast Program is a collection of podcast shows hosted by various ServiceNow experts and professionals covering different focus areas and a broad variety of topics. Come listen to our experts talk about what's going on at ServiceNow. Join the conversation!

Visit the Now Community forums

ServiceNow Podcast Program is a collection of podcast shows hosted by various ServiceNow experts and professionals covering different focus areas and a broad variety of topics. Come listen to our experts talk about what's going on at ServiceNow. Join the conversation!
<a href="https://community.servicenow.com/community">Visit the Now Community forums</a>

ServiceNow Podcasts

TAKEAWAY - Think Like a Librarian: Why the Reference Interview Is the Framework Data Teams Are Missing with Jenna Jordan and Amalia Child 

Most companies slapping the "data product" label on existing data assets are fooling themselves. Bethany Sehon, Enterprise Data Leader at Capital One, has been building data products and ontologies in production long before they became buzzwords. Bethany joins Juan and Tim to share lessons learned: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more. 

Topics discussed: 
Data products as a discipline, not a label
Governance process as the forcing function 
Dedicated roles, not side-of-desk work 
Ontology as a strategic function 
Standardization is necessary but not sufficient
Less is more on scope 
Trust is consumer-specific
AI expands what counts as a data product

Most companies slapping the "data product" label on existing data assets are fooling themselves. Bethany Sehon, Enterprise Data Leader at Capital One, has been building data products and ontologies in production long before they became buzzwords. Bethany joins Juan and Tim to share lessons learned: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more.&nbsp;
Topics discussed:&nbsp;
<ul>
<li aria-level="1">Data products as a discipline, not a label</li>
<li aria-level="1">Governance process as the forcing function&nbsp;</li>
<li aria-level="1">Dedicated roles, not side-of-desk work&nbsp;</li>
<li aria-level="1">Ontology as a strategic function&nbsp;</li>
<li aria-level="1">Standardization is necessary but not sufficient</li>
<li aria-level="1">Less is more on scope&nbsp;</li>
<li aria-level="1">Trust is consumer-specific</li>
<li aria-level="1">AI expands what counts as a data product</li>
</ul>

Data Products Done Right: Lessons from Capital One's Journey at Scale with Bethany Sehon

This is the takeaway episode with Bethany Sehon, Enterprise Data Leader at Capital One where she share lessons learned of building data products and ontologies in production long before they became buzzwords: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more. 

This is the takeaway episode with Bethany Sehon, Enterprise Data Leader at Capital One where she share lessons learned of building data products and ontologies in production long before they became buzzwords: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more.&nbsp;

TAKEAWAY - Data Products Done Right: Lessons from Capital One's Journey at Scale with Bethany Sehon

This is the start of our 7th year of doing the podcast. Juan and Tim kick off the 12th season sharing who are the upcoming guests and what's the latest on their mind about this crazy data and AI world:
The new website (honestnobsdata.com) and discussed building a knowledge graph of the podcast itself 
Two types of semantics: for AI context vs. for system interoperability 
The vendor hype gap vs. practitioner reality
AI agent proliferation and the governance surface area explosion
AI cybersecurity: models escaping their sandboxes 
The economics of AI: accuracy, latency, and token cost as the new CAP theorem
The great convergence, "triangle of consolidation" and “postmodern data stack”
OSI and Apache Nessie
Work as the center of gravity
Bridging the operational and analytics worlds

This is the start of our 7th year of doing the podcast. Juan and Tim kick off the 12th season sharing who are the upcoming guests and what's the latest on their mind about this crazy data and AI world:
<ul>
<li aria-level="1">The new website (<a href="http://honestnobsdata.com">honestnobsdata.com</a>) and discussed building a knowledge graph of the podcast itself&nbsp;</li>
<li aria-level="1">Two types of semantics: for AI context vs. for system interoperability&nbsp;</li>
<li aria-level="1">The vendor hype gap vs. practitioner reality</li>
<li aria-level="1">AI agent proliferation and the governance surface area explosion</li>
<li aria-level="1">AI cybersecurity: models escaping their sandboxes&nbsp;</li>
<li aria-level="1">The economics of AI: accuracy, latency, and token cost as the new CAP theorem</li>
<li aria-level="1">The great convergence, "triangle of consolidation" and &ldquo;postmodern data stack&rdquo;</li>
<li aria-level="1">OSI and Apache Nessie</li>
<li aria-level="1">Work as the center of gravity</li>
<li aria-level="1">Bridging the operational and analytics worlds</li>
</ul>

TAKEAWAY - Think Like a Librarian: Why the Reference Interview Is the Framework Data Teams Are Missing with Jenna Jordan and Amalia Child

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