Rehgan Bleile joins me to chat about the challenges and importance of AI governance and adoption. We also discuss the lack of representation of women in conferences, and efforts to create genuine opportunities for women speakers. 
 AlignAI: getalignai.com LinkedIn: https://www.linkedin.com/in/rehganavon/ Women in Analytics: https://www.womeninanalytics.com/

Most people approach their careers via 1:1 - 1 employee: 1 employer, 1 consultant: 1 client, etc.

In this rant, I discuss why you should consider 1:many as a way to level up your career and visibility.

Christian Steinart and I chat about data consulting in unsexy and niche industries (like roofing in Christian's case). We go through starting a consultancy, the mental game of running a business, getting leads, and much more.

LinkedIn: https://www.linkedin.com/in/christiansteinert96/

I've been head's down finishing my upcoming Data Engineering course on Coursera, and working on the new book. In this episode, I chat about the differences between courses and books, why high quality content matters more than ever in the age of AI, and much more.

Enroll in the new DeepLearning.AI Data Engineering Professional Certificate course here!

https://www.coursera.org/professional-certificates/data-engineering

Chris Bergh joins me to chat about all things DataOps. We also discuss lean, removing waste from data processes and teams, and much more.

DataKitchen: https://datakitchen.io/

DataOps Manifesto: https://dataopsmanifesto.org/en/

Hanging out in Berlin right now. Re-read Peopleware (originally released in 1987), and it got me thinking about what hasn't changed in tech and data. Namely, we tend to rush through things in the name of productivity instead of focusing on quality. Will AI help this? Maybe and maybe not.

Lexi Pasi and I chat about symbolic logic in AI, building and managing data science teams, math, and the shapes of ML/AI problems.

Lexi is one of my favorites to talk to because she's so left-field yet so effectively reasonable and logical (she does have a PhD in logic...).

LinkedIn: https://www.linkedin.com/in/alexandrapasi/

I've been told that I "say the quiet parts out loud." It might be calling out "data science" and AI as overhyped, or anything else I've ranted about online over the years.

In this episode, I unpack what that means and why I do it.

Joseph Machado and I chat about teaching data engineering, trends in data engineering, and the state of data content and influencers.

LinkedIn: https://www.linkedin.com/in/josephmachado1991/

Website: https://www.startdataengineering.com/

Data can be a rough industry. Data professionals often feel alone and in need of someone to talk to. I've been hosting "data therapy" sessions for the last couple months. Here are my thoughts on them, and why I think they're critical to helping professionals feel heard and learn from others.

Join here: https://practicaldatamodeling.substack.com/

Paco Nathan and I chat about early chatbots, and all things AI, especially since the 1980s. We also riff on how we intersected in the early days of the Internet.

Paco is one of my faves, so expect him back for another interview soon.

X: https://twitter.com/pacoid

LinkedIn: https://www.linkedin.com/in/ceteri/

Shachar Meir joins me for a chat about the three missing ingredients in data, going out on your own, the state of the data industry, and much more.

LinkedIn: https://www.linkedin.com/in/shacharmeir/

Matthew Mullins joins me to chat about a recent "get off my lawn" post along the lines that a lot of things we're trying to reinvent in data have already been done, and we're generally overcomplicating the hell out of things. I agree with him. In this episode, we rant about a few of the topics in his post.

The post: https://www.linkedin.com/feed/update/urn:li:activity:7217116669188485121/

Lindsay Murphy joins me to chat about whether companies are ready for AI in their data and analytics workflows. The verdict - listen and find out more :)

Lindsay's LinkedIn: https://www.linkedin.com/in/lindsaymurphy4/

It's not enough to know or peddle one data modeling technique these days. That's like fighting in the UFC knowing only thumb-wrestling. The world is very complicated with respect to data. To be a data practitioner, you need to be awesome in not just one, but MANY data modeling techniques. This is what I call Mixed Model Arts, which will be discussed further soon. Anyway, don't be 1-dimensional. Know a lot about a lot.

What's up with Finland and data? I think Finland might have among the strongest contingencies of data practitioners in the world. Pound for pound, Finland might rule the planet for data competencies.

I chat with the The Finnish Data Mafia, jokingly my friends who are responsible for the upcoming Helskini Data Week.

Just wrapped up a course with Sol Rashidi on transitioning your career from practitioner to leader. The notion of "success" kept recurring, so I spend this podcast unpacking it. What is success and why should you figure out what it means for you?

This wasn't the interview I expected to do. I thought I'd interview Nick Freund about his startup, Workstream. Between the time we scheduled our podcast and when we hit the record button, he shut down his company. That's a pretty major shift, to say the least.

What's it like to shut down a company? Nick discusses the various pivots of his startup, trying to raise capital in a brutal funding environment, the data tooling landscape, the process of shutting down a company, and much more.

This is an emotional episode, and I'm glad we got the opportunity to make it happen. I feel like stories like Nick's are all too common, yet rarely vocalized in the brutally honest way that Nick describes his story.

Nick's LinkedIn: https://www.linkedin.com/in/nick-from-workstream/

I've had plenty of discussions over the last couple of weeks about data teams - what are they, and how do I measure their success? I dive into a distinction I make about data teams - enterprise vs product - and some key ways to gauge them.

Doug Needham is an OG DBA and data architect who built DataOps workflows back in Desert Storm (!) and has managed to stay very current with data to today. We talk about data architecture war stories, the hard work to do generative AI in the enterprise, and much more. Enjoy!

Some things happened over the last day that I need to call out. Women and other underrepresented groups need to be treated better in tech and data. Whether it's all-male panels at conferences or mansplaining on social media, I'm pretty embarrassed and irritated by how women are treated in our industry. My message for this episode - stop being a d*ck.

Juha Korpela is a world-renowned expert in conceptual data modeling. He joins me to discuss the power of conceptual data modeling, why the data modeling world is broken today, data products, and much more.

LinkedIn: https://www.linkedin.com/in/jkorpela/

In this episode, I talk about why history matters for technology professionals. When you understand the history of technology, techniques, and approaches, you have the context to understand where they fit into your situation. Ignore history at your peril.

Yulia Pavlova (Director of Technical Innovation at Thomson Reuters) joins me to chat about the role of AI in disinformation/misinformation in the media, communicating complex topics to nontechnical people, and much more.

I personally consider the current state of the media as one of the central challenges today, and I learned a lot chatting with Yulia, who's innovating in this space.

LinkedIn: https://www.linkedin.com/in/yuliapavlovaphd/

Is data modeling a waste of time? I meet a number of people who say it is. In this episode, I dissect some of the arguments against data modeling, and give reasons why it matters more than ever today.