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

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?

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

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

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

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.

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?

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.

If data is the new oil, does that mean the Chief Data Officer is the new baron? Not exactly, but it is one of the fastest growing and most critical executive roles in the enterprise. So... what exactly does a CDO do? In this episode, Tim and Juan welcome special guest Mohammed Aaser, CDO at McKinsey & Company, the world’s largest management consulting firm. We’ll discuss the unique responsibilities and challenges for a CDO in an increasingly data-driven economy. Plus, we’ll learn how the firm builds and maintains its own thriving data culture. Discussion topics include how a CDO drives critical value for the business, future trends and potential disruptions in the data space, and a clear verdict on whether Tim or Juan would make the better CDO.

We test our food. We test our cars. We test our code. But do we test our data? Join Tim, Juan, and special guest Sam Bail from Superconductive, the company behind open source data testing tool Great Expectations. We’ll chat about how to incorporate data testing into your workflow and who should be involved. We’ll also discuss why data quality is not just a tool, but a state of mind and a commitment. This episode will feature tools for test-driven data development, best practices to incorporate data testing, and which of our hosts received the highest SAT score.

The insurance industry thrives on robust, diverse, accurate, and innovative data. In fact, it’s one of the most data rich verticals around, dealing in claims, ratings, coverage, pricing, geographic, and people data and so much more. But let’s be honest, working with the disparate data sources and applications is incredibly messy and inefficient! In this episode, Juan and Tim will be joined by insurance veteran, John Lucker to find out what we can all learn from the data and analytics challenges in insurance. This episode will include an honest look at the ugly side of insurance data, opportunities to address insurance data challenges, and an important deep dive: State Farm vs. Progressive. Which has better commercials?