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

Is self-service BI the answer? w/ Cindi Howson

Self-service is great for listening to music, pumping gas, and figuring out what to binge watch on TV. But is self-service really what we want from our analytics? What happens to the business world when everyone is a data analyst?
Tim, Juan, and special guest Cindi Howson, chief data strategy officer at ThoughtSpot and host of The Data Chief Podcast explore the positives and potential downsides of data democratization and how to drive data cultures that bias for good.
This episode will feature:
How BI in the enterprise evolved from the domain of the few to a tool for the many
Why change management is critical for improving data cultures
What’s something that’s not self-service today but probably should be?

Data tools: the good, the bad, and the ugly w/ Erik Bernhardsson

New data tools drop daily, but are they worth the hype? Some launch with overinflated expectations, while others solve a problem that doesn’t really exist. On the flip side, there are tools that transform the enterprise and have the potential to change the way we do data forever. The question is, how can you tell the difference?
Special guest Erik Bernhardsson, formerly with Better.com and Spotify, joins Tim and Juan for a conversation on data tools; the good, bad and the ugly.
This episode will feature:
An overview of data tool types and who they serve
How to define “good, bad and ugly”
What’s your favorite spaghetti western movie?

Data is the new... Herman Miller Aeron Chair? w/ Doug Laney

If data, and information more broadly, is a company's most important asset, then why is it so often treated with less respect than office furniture? It's in various states of disarray, often unaccounted for, and employees bring it home against corporate policy.
This week, we're joined by Doug Laney, author of the best-selling book, "Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage.” We'll talk about taking better care of your data, so it can take better care of you.
This episode will feature:
Tips for how to truly measure the value of all your information assets
Steps for getting your information house in order
Is data the new oil, bacon, gold, oxygen, currency, or sand?

The Data Mesh Debate w/ hosts Juan and Tim

“What is a data mesh?” It’s one of the most frequently asked questions on this podcast, and is almost a microcosm for data and analytics as a whole. It may be the next big thing. It may be yet another passing fad in architectural design. We compared and contrasted it with data fabric in Episode 32 and spoke with Zhamak Dehghani, the founder of the data mesh paradigm in Episode 44. We even hosted a special panel discussion dedicated to data mesh.
After all these discussions, we are still trying to figure out what it is. So, we’re settling this the only way we know how: a good old-fashioned debate. Only one side can win.
This episode will feature:
The definitive(ish) definition of data mesh
A look at why organizations struggle to understand and implement it
What is your data “hot take” that everyone else disagrees with?

Sure, you’re responsible, but are you ethical? w/ Partha Srinivasa

When we think about good, responsible data stewardship, we often think in terms of compliance and governance. We talk about access, trust, and consumption. But there’s another dimension we must consider if we’re to be truly responsible with data. We simply can’t ignore the role of ethics in data use.
Join Tim, Juan and special guest Partha Srinivasa, award-winning CIO/CTO/CDO and current CDO of Verisk, for a conversation about data responsibility and ethics.
This episode will feature:
Common misperceptions about data and ethics
How to build ethics into your data practice
Can you be both ethical and a fan of the morally dubious, Seinfeld?

A data analyst, scientist, and engineer walk into a bar...w/ Danielle Oberdier

Quick, what’s the difference between a data engineer and a data analyst? One preps the data, the other analyzes the data, right? A data scientist, meanwhile, analyzes more complex and disorganized data. The truth is all three of these roles perform overlapping functions leading to an incredible amount of confusion in the job market.
Join Tim, Juan and special guest Danielle Oberdier, founder of DiKayo Data and host of the popular DataFemme podcast, to talk about the state of data jobs. How do we make sense of the roles that are out there today, who should companies be looking for in their hiring process, and what new and exciting data positions are starting to emerge?
This episode will feature:
Certifications that make you more marketable
How to stand among your peers in a crowded job market
What was the very first job you ever had?

Do we have a data infrastructure crisis? w/ Mammad Zadeh

Is your data infrastructure designed for the next decade of data work? Probably not, but are you at least heading in the right direction? It’s an existential question that often reveals fundamental flaws in the system.
Join Tim, Juan and special guest Mammad Zadeh, former VP of Engineering at Intuit, for a deep dive on data tools, leadership, and change management.
This episode will feature:
A look at the rocky road ahead for CDOs
How to effectively scale data teams
What's your crisis comfort food?

Fashion Week...but for data w/ Jans Aasman

Fall summit 2021 is right around the corner!!!! Join speakers from Nationwide, ThoughtSpot and MANY MORE to discuss hot topics like Data Mesh, DataOps, and the future of data management on Sep. 29: data.world/events/summit
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With the hype of graph databases and knowledge graphs, a common (mis)practice is to quickly migrate your existing siloed data into a graph database. But be careful! You may just be bringing the complexity of your silos into the graph.
Join Tim, Juan and guest Jans Aasman from Franz Inc, the makers of AllegroGraph, for a conversation on why your graph-based machine learning and 360 projects should start with data modeling.
This episode will feature:
Data modeling approaches you should consider
Tips to avoid data modeling pitfalls
Juan and Tim striking their best model pose

How to think about data value w/ Lars Albertsson

Don't forget to register for the data.world Fall summit 2021: data.world/events
We all strive to be data-driven. And yet we all instinctively know that we’re not very good at it. In fact, if you believe any number of recent surveys, it seems we may actually be getting worse at data. One reason for this is a misalignment on what success looks like. How do you define and measure it? What is the actual value of your data?
This week, Tim and Juan join Lars Albertsson, founder of Scling, to talk about data productivity and balancing a technology vs product-driven approach to data.
This episode will feature:
Companies that have truly unlocked data value and how they did it
Common missteps along the data maturity journey
What have you been practicing... but not actually getting any better at?

What’s the deal with Reverse ETL? w/ Tejas Manohar

ETL (Extract Transform and Load) was the SOP for data integration for 25+ years. A decade ago the introduction of data lakes pushed transformation to the end of the process and into tools like Snowflake, BigQuery, and Redshift. Now the latest chatter in the data management industry is Reverse ETL. Shouldn’t we call this LTE?
Join Tim, Juan and special guest Tejas Manohar, CEO of Hightouch for a conversation about Reverse ETL and why it matters now.
This episode will feature:
The evolution of data integration pipelines
Use cases for Reverse ETL
What other acronyms make you smh?