Karli Burghoff

5 Questions with Nick Schrock

The modern data stack is often defined by the type of technologies that exist within it. Cloud-based, open source, low/no code tools, ELT, and reverse ETL. But surely there’s more to it… isn’t there?

5 Questions with 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 manage data forever. The question is, how do you tell the difference?

5 Questions with Zhamak Deghani

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.

5 Questions with Kelly Wright

In a relatively short time, we’ve gone from collecting and neglecting data to managing, enriching, and learning from data. We are in the age of collective data empowerment, where user-friendly apps and data ubiquity mean almost anyone can answer complex business questions. So why does the “data-driven” enterprise still sound like a pipe dream?

5 Questions with Jans Aasman

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?

5 Questions with Lars Albertsson

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?

5 Questions with 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?

5 Questions with 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?

5 Questions with Kirk Borne

We all want to get more value from our data, right? So should we wrangle it, prep it, classify it, mine it, or model it? Then once we’ve got our data squared away, should we do prescriptive or predictive analytics? And does that require real-time or just-in-time data? If we had a nickel for every buzzword we hear, we could afford far better podcasting equipment.

5 Questions with Denise Gosnell

This week, Juan and Tim are joined by Denise Gosnell, CDO of Datastax, to talk about the business of open source and how community-centric data applications are reshaping the enterprise.

5 Questions with Emil Eifrem

Catalog and Cocktails hosts, Juan Sequeda and Tim Gasper sat down with CEO of Neo4j, Emil Eifrem, to cover the evolution of the data landscape so far, and to speculate where it’s heading.

5 Questions with DJ Patil

The episode touched on a variety of topics including the U.S. pandemic response, using data to sow mistrust, and the ethical use of data. The following five questions are excerpted from the podcast. You can check out the entire recording here:

5 Questions with Mike Ferguson

You can’t get from your home to the grocery simply by owning a car. You have to actually drive the vehicle to get to a place that delivers value. Sounds obvious right? But we don’t instinctively think this way when it comes to data. We focus so much on tools, processes, and architectures, but we don’t talk enough about actually using the data.