We live in the golden age of data tools, yet we still lack a grasp on the basics of working with data itself - data literacy, modeling, solving problems people care about, etc. It's time to get back to basics, which I think will make data professionals way more effective at their jobs.

Scott Taylor (aka the Data Whisperer) is an OG in data content, speaking, and storytelling. He's been keynoting data events since the 1990s and keeps sharpening his game. Scott is someone I look up to, and I always enjoy our chats.

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

Website: https://www.metametaconsulting.com/

YouTube: https://www.youtube.com/channel/UCVQ1YhjNqc77GVsb3Xs4tvw

(Note - there's a very slight interruption with our internet connection at the 35 minute mark)

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While on a hike today with a friend, we were engrossed in a chat about something that is often overlooked by technical practitioners - the politics of data. This takes many shapes and forms, and ultimately impacts the type of work you do. The main takeaway - learn to pay attention to the broader political landscape of your company.

Kai Zenner has been working on the EI AI Act for a while, and we chat about his perspective on its evolution, challenges, and potential. Along the way, we discuss why the EU AI Act differs from GDPR, why regulating a quasi-global piece of legislation is very difficult, and much more.

I admit, politics and regulation are way outside my wheelhouse, and I learned a ton in this discussion. Given the impact the EU AI Act will affect the work of everyone involved with data, I think you'll learn a thing or two about not just the act itself, but also how the "sausage is made", so to speak. Enjoy!

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

Twitter: https://twitter.com/ZennerBXL

Site: https://www.kaizenner.eu

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Ryan Boyd and I chat about the evolution and future of databases, the pendulum between single-server and distributed computing, DuckDB and Motherduck, and much more.

We also talk about developer relations, which I consider Ryan as one of the OG's in the field.

Note - this was recorded the week of Databricks Summit 2023.

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Is Kimball still relevant? Or should we just throw columnar storage and unlimited compute to solve our analytical needs?

Because I like to live on the edge, I respond to a comment online that I think highlights the rot in our industry as it relates to how we view data modeling today.

Data Modeling With Joe Reis - Understanding What Data Modeling Is And Where It's Going (Seattle Data Guy): https://www.youtube.com/watch?v=NKo02ThtAto

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Joshua Bowles is a linguist and data scientist turned software engineer. This is a wide-ranging chat between two old-school data scientists/ML practitioners about the past, present, and future of ML and AI.

LinkedIn: https://www.linkedin.com/in/joshua-bowles-ailgroup/

Mastadon: https://infosec.exchange/explore

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Benny and I chat about whether data is a profession (in the traditional sense), moving from CDO at a large company to solo consulting, building an audience and staying consistent with content, and much more.

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

Elevating Data to a Profession (link): https://www.datent.com/p/elevating-data-to-a-profession-why

Blog: https://www.datent.com

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Imagine two extremes. On one end, data modeling is done perfectly and harmoniously across the data lifecycle. On the other end, data modeling is ignored and thrown into the dustbin of history. Along this spectrum, where do you think we are as a data industry?

I'm leaving this question open-ended right for now and would appreciate your thoughts.

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Ranjith Raghunath is a customer experience (CX) wizard, and we chat about why CX is important, modeling the customer journey process, the impact of AI on CX, and much more.

Ranjith's LinkedIn: https://www.linkedin.com/in/ranjith-raghunath/

CX Data Labs: https://www.cxdatalabs.com/

Note - there were some technical difficulties around the 44:00 minute mark. Nothing major though.

Peter Hanssens is the founder of DataEngBytes, the forward-thinking conference on all things data engineering. In this chat, we talk about what to expect at the 2023 edition of DataEngBytes, the tech scene in Australia, his views on the current and future field of data engineering, and much more.

data #dataengineering #dataengbytes

It's no secret that AI is useful, but it's super hard to cut through the BS.

Maya Mikhailov and I chat about the very confusing ML/AI landscape. This is a great discussion that offers many pointers if you want to leverage ML and AI, but you're unsure how to do so.

Maya's LinkedIn: https://www.linkedin.com/in/mayam/

Savvi AI: https://www.savviai.com/

ai #ml #data #datascience

I'm starting to see more and more discussions about soft skills and people skills. In this episode, I talk about why tech skills are table stakes, and soft skills are where you need to level up if you want to boost your career.

Office Space clip: https://www.youtube.com/watch?v=hNuu9CpdjIo

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Tristan Handy and I chat about balancing competing tensions, both personally and leading dbt Labs. We also discuss the power of organizational behavior, naming problems to solve, and home remodeling.

This is different from the normal interviews you'll hear with Tristan, and I hope you enjoy it!

dbtlabs #data #analyticsengineering

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After a whirlwind of conferences with the two "big players in the data space," I share some thoughts on how we can improve the conferences targeted at the data engineering community.

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Solomon Kahn has led data teams at startups and big companies. We talk about the advantages of being a data person in a big company, what makes a good data team, why he thinks embedded analytics suck, his new startup Delivery Layer, and much more.

Delivery Layer: https://www.deliverylayer.com/

Solomon's LinkedIn: https://www.linkedin.com/in/solomonkahn/

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Whenever Kris and I chat, there's an agenda, which is totally useless. Every single time we've talked, the conversation goes into different (I'll argue better) directions. In this episode, Kris and I delve into the art and craft of programming, finding your tribe as a developer advocate, and so much more. I hope you enjoy this great and meandering conversation.

Developer Voices podcast: https://open.spotify.com/show/2gXhwz0AQRv2cvw61kobE5

Kris's LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris's Twitter: https://twitter.com/krisajenkins

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Colleen Fotsch is one of the highest performing (and kindest/nicest) people I know. She's a former NCAA swimmer at UC Berkley, where she was a two-time national champ, American record holder, and Olympic qualifier. She's also a multiple-time Crossfit games athlete and competed on the US National Bobsled Team, where she was supposed to go to Olympic qualifiers, but got sidelined by a tragic injury which she discusses.

On top of all of this, she's a data analyst! In this show, we discuss her evolution from athlete to analyst, the gory details of being a top-level competitive athlete, and much more. If you're interested in what it takes to compete at the highest levels, you'll definitely like this episode.

I'm proud to call Colleen my coach, and I'm also fortunate to mentor her in her data journey. Enjoy!

Instagram: https://www.instagram.com/colleenfotsch/?hl=en YouTube: https://www.youtube.com/c/ColleenFotsch LinkedIn: https://www.linkedin.com/in/colleenfotsch/

I've spoken earlier about why I think data modeling is on life support, and if not dead, definitely a zombie. In this rant, I explore how we got here, the consequences, and what we can do to resurrect data modeling.

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Veronika Durgin joins me to chat about learning and adapting in a fast-changing world, handling vendors, and much more.

Veronika's LinkedIn: https://www.linkedin.com/in/vdurgin/

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Paul Blankley and Ryan Janssen are the co-founders of Zenlytic. They started a BI company with an LLM-first approach (back before LLM's were insanely cool). We talk about the future of BI, and how LLM's will change the face of data and analytics.

Zenlytic: https://www.zenlytic.com/

Paul's LinkedIn: https://www.linkedin.com/in/paulblankley/

Ryan's LinkedIn: https://www.linkedin.com/in/janssenryan/

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Karl "Ivo" Sokolov and I are good friends, and we chat about how he views innovation in Europe, regulations, Western and Eastern Europe, moving from services to a product, and much more. Ivo is a straight shooter, and I always enjoy chatting with him. Enjoy!

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The word "model" is used a lot by data professionals. There are dbt models, machine learning models, relational models, and conceptual, logical, and physical models. My concern is we're missing the bigger picture of what data modeling was initially supposed to accomplish, which was to represent reality and structure it as data. The bigger implication is that our various "models" will become too myopic and miss the larger broader context of the reality of how we use data to serve our organizations.

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George Park is a Czechia-based data engineer. We chat about automation, change management and culture, data modeling (Data Vault in particular), and much more. George lives on the front lines of helping customers, so this is a good discussion if you like to hear from a real practitioner.

George's LinkedIn: https://www.linkedin.com/in/george-park-599846136/

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There's a rush from many countries to regulate AI. Murielle Popa-Fabre is an NLP and ML expert currently working for the Council of Europe, building an international Framework Convention on AI that will touch a wider number of countries (46) than the EU AI Act (only for EU countries). We chat about her path from academia to working in regulation, and the upcoming EU, the Council of Europe, and G7 regulations on AI. These regulations will have a historical impact on what happens next with AI, and it will be very interesting to see where things go from here.

Murielle's LinkedIn: https://www.linkedin.com/in/murielle-popa-fabre-b563187b/

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