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

Data Organization: Reap what you Sow

Every maturing company hits a point where the processes and systems that once worked no longer scale. Don’t let this happen to your data organization.
Join Juan, Tim, and this week’s guest, Meetesh Karia from The Zebra, while they live brainstorm about scaling data teams and processes. They’ll look at how the small seeds you plant along the journey can pay huge dividends later on. It all stems from the seeds you plant along the way; the people on your team, the balance of efficiency vs. resilience and the processes...
Other topics include:
How to strike a balance between efficiency and resilience
Structuring teams for continued success
If you could grow a garden of anything, what would it be? (Not the obvious; money)

What’s the Secret Recipe for DataOps?

Accelerating the delivery of data applications is at the top of the to-do list for many enterprises. That requires overcoming friction in your data architecture, managing complex requirements, and handling a variety of semantics challenges.
Join Tim, Juan, and Chris Bergh from Data Kitchen for a conversation about DataOps. The trio will look at how this emerging data management methodology can improve the flow of knowledge within data teams.
Other topics include:
How to create the right DataOps culture
Measuring the value of your DataOps strategy
Worst reason you’ve been called into work on your day off

Why it’s time to mesh with your data architecture

Mesh is everywhere. It’s in our clothes, our fishing nets, our Wifi networks, and now our data architecture. But what is a data mesh? Do you need one? And if so, how do you start?
Zhamak Dehghani is the director of Emerging Technologies at Thoughtworks and the leading expert on data mesh. We’ll chat about the emergence of the data mesh as a concept, why the approach works for eliminating architectural silos, and how it's producing more data-driven cultures.
Other topics include:
Key tools, technologies, and skills for adopting a data mesh
What to do if you’re data mesh curious
Inspiring architectural designs outside the data space

What do they teach at your Data Science U?

The data science discipline is in a constant state of evolution with new techniques and applications being introduced almost daily. And this journey often begins at institutions of higher learning around the world. Many universities offer bachelors and masters degrees in data science, but are these programs adequately preparing the data professionals of tomorrow?
In this episode, Juan, Tim, and Prof George Fletcher of Eindhoven University of Technology will discuss the state of data science education and explore how these programs can be extended to satisfy industry needs.
Other topics include:
What universities get right and wrong about data science education
What skills we should be teaching that we aren’t today
What should be the mascot at Data Science University?

Power to the Data!

Companies spend an obscene amount of money every year on data and analytics initiatives. And almost all of that spend goes toward applications that employ vastly different data models. Normalizing data structures is a painstaking process that most IT teams are used to by now. But should we normalize normalization?
In this episode, Juan, Tim, and Dave McComb, President of Semantic Arts and author of Software Wasteland and The Data-Centric Revolution, discuss what it takes to shift from an application-centric to a data-centric mindset.
Other topics include:
What it means to be data-centric
How to undo decades of questionable data management practices
Debate: French Revolution vs. American Revolution vs. Beatles Revolution

Building a great data team: Mission (Im)possible

Here’s your mission, should you choose to accept it: Your company is making poor decisions about how to bring its latest product to market. Time is running out, and the company risks missing a unique and lucrative opportunity. You must convince your exec team to stop using gut instinct and start trusting in data.
Step one is building a strong data and analytics team with the right mix of people, process, and technical know-how. In this episode, Juan, Tim, and Patrick Barry, VP of Data and Analytics at SPM Marketing and Communications, discuss what it takes to assemble a team from scratch.
Other topics include:
What roles and skills to prioritize
Why diversity is critical
What things do we wish would self-destruct

Does your data have a ‘born on’ date?

Where does this data come from? Who created it? How has it been used? Like the origins of the universe, there can be quite a mystery surrounding the genesis of your company’s datasets. Understanding data provenance is the first step in answering those critical questions.
Join Tim, Juan, and Professor Deborah McGuiness of Rensselaer Polytechnic Institute, renowned AI scientist and pioneer in provenance research to discuss data provenance and why it matters to you.
In this episode, we discuss:
The origin story and evolution of data provenance
Provenance standards every data person should know
Which fictional character has the best origin story

Identity graph: the new customer 360

What’s the best way to get to know your customers? For most companies the solution is creating a 360 profile using data integration, data warehouse, master data management, and a slew of marketing tools. But there is another option: the Identity Graph.
Join Tim, Juan, and guests Michael Murray and Bret Harper of Wunderman Thompson Data for a look at how and why Identity Graphs are disrupting the company-customer relationship.
In this episode, we’ll discuss what an identity graph is and why you need one, why graph technology is a game changer for understanding customers, and the true identity of St. Patrick and what he might buy if he were alive today.

A modern approach to data transformation

Data warehouses have been around for decades, and we’ve relied on data integration processes like ETL (Extract-Transform-Load) to get the data in. While data warehouses evolved to data lakes and data lakehouses, and ETL became ELT, little else has changed.
This week’s special guest is Drew Banin, co-founder of Fishtown Analytics. They’re the team behind the open source tool Data Build Tool (better known as dbt), and for disrupting the data transformation process (the T in ETL).
Discussion topics include how to build a modern tech stack for your data-driven business, what actually started the dbt revolution, and (of course, obviously) if you could transform into any animal on the planet, what would it be and why?

Do you have data trust issues?

When data is powering your business, you expect that data to be trustworthy in real time, all the time, but that’s easier said than done. That’s where data quality comes in. Join special guest Lior Gavish, co-founder of Monte Carlo Data, for a conversation about data quality, reliability, and trust. Discussion topics include quantity vs. quality in data science and analytics, the downsides of applying band-aid fixes versus repeatable solutions, and whether quality wines are really worth the extra expense.