The Joe Reis Show

Matt Housley joins me for our monthly round-up of topics. This time, there's danger everywhere - The AI Bubble, how vibe coding is evolving, AI slop, and more.

After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it. We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.

In this conversation, Dr. Cecilia Dones and I discuss the social skills we're losing as AI becomes more integrated into our lives. We explore the erosion of social norms, from AI companions joining Zoom calls without consent, endless enshitified content, to my son's generation calling AI girlfriends "clankers".Is there hope? We break down the "rage currency" that dominates media and the positive AI stories that go unheard. The biggest takeaway: as the world becomes more synthetic, "showing up" in person will become the ultimate "premium value."

In conversations I've been having with leaders and practitioners, there's some open-ended questions about the impact of AI on vendors and open-source projects. If you don't have a moat, you need to start thinking about how AI coding tools will erode the edges of your product. And what about getting users and traction? I cover this and much more in this episode. Enjoy!

Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.

Matt Turck (VC at FirstMark) joins the show to break down the most controversial MAD (Machine Learning, AI, and Data) Landscape yet. This year, the team "declared bankruptcy" and cut over 1,000 logos to better reflect the market reality: a "Cambrian explosion" of AI companies and a fierce "struggle and tension between the very large companies and the startups".Matt discusses why incumbents are "absolutely not lazy" , which categories have "largely just gone away" (like Customer Data Platforms and Reverse ETL) , and what new categories (like AI Agents and Local AI) are emerging. We also cover his investment thesis in a world dominated by foundation models, the "very underestimated" European AI scene , and whether an AI could win a Nobel Prize by 2027.https://www.mattturck.com/mad2025

I travel a TON, and the most frequent questions I get relate to traveling: Why I do it and any tips I have for traveling. Here, I answer those questions and more.

Jeremiah Lowin, founder of Prefect , returns to the show to discuss the seismic shift in the data and AI landscape since our last conversation a few years ago. He shares the wild origin story of FastMCP, a project he started to create a more "Pythonic" wrapper for Anthropic's Model Context Protocol (MCP).Jeremiah explains how this side project was incorporated into Anthropic's official SDK and then exploded to over a million downloads a day after MCP gained support from OpenAI and Google.He clarifies why this is an complementary expansion for Prefect, not a pivot , and provides a simple analogy for MCP as the "USB-C for AI agents". Most surprisingly, Jeremiah reveals that the primary adoption of MCP isn't for external products, but internally by data teams who are using it to finally fulfill the promise of the self-serve semantic layer and create a governable, "LLM-free zone" for AI tools.

I'm back, and give some notes from the road, thoughts on choosing tools and vendors, having a plan B for tools, and more.

There's no shortage of technical content for data engineers, but a massive gap exists when it comes to the non-technical skills required to advance beyond a senior role. I sit down with Yordan Ivanov, Head of Data Engineering and writer of "Data Gibberish," to talk about this disconnect.We dive into his personal journey of failing as a manager the first time, learning the crucial "people" skills, and his current mission to help data engineers learn how to speak the language of business.Key areas we explore:The Senior-Level Content Gap: Yordan explains why his non-technical content on career strategy and stakeholder communication gets "terrible" engagement compared to technical posts, even though it's what's needed to advance.The Managerial Trap: Yordan's candid story about his first attempt at management, where he failed because he cared only about code and wasn't equipped for the people-centric aspects and politics of the role.The Danger of AI Over-reliance: A deep discussion on how leaning too heavily on AI can prevent the development of fundamental thinking and problem-solving skills, both in coding and in life.The Maturing Data Landscape: We reflect on the end of the "modern data stack euphoria" and what the wave of acquisitions means for innovation and the future of data tooling.AI Adoption in Europe vs. the US: A look at how AI adoption is perceived as massive and mandatory in Europe, while US census data shows surprisingly low enterprise adoption rates

The world of data is being reset by AI, and the infrastructure needs to evolve with it. I sit down with streaming legend Tyler Akidau to discuss how the principles of stream processing are forming the foundation for the next generation of "agentic AI" systems.Tyler, who was an AI cynic until recently, explains why he's now convinced that AI agents will fundamentally change how businesses operate and what problems we need to solve to deploy them safely.Key topics we explore:From Human Analytics to Agentic Systems: How data architectures built for human analysis must be re-imagined for a world with thousands of AI agents operating at machine speed.Auditing Everything: Why managing AI requires a new level of governance where we must record all data an agent touches, not just metadata, to diagnose its complex and opaque behaviorThe End of Windowing's Dominance: Tyler reflects on the influential Dataflow paper he co-authored and explains why he now sees a table-based abstraction as a more powerful and user-friendly model than focusing on windowing.The D&D Alignment of AI: Tyler's brilliant analogy for why enterprises are struggling to adopt AI: we're trying to integrate "chaotic" agents into systems built for "lawful good" employees.A Reset for the Industry: Why the rise of AI feels like the early 2010s of streaming, where the problems are unsolved and everyone is trying to figure out the answers.

I still see some companies acting sheepish with AI, too scared to even try it out. That's a massive mistake. Now is the time to play offense with incorporating AI into your company and reimagining what it can become.

Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents.We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.Join us as we cover:The Shift from Dashboards to Agents: We discuss why dashboards can create a high cognitive load and fail users in complex scenarios , and how conversational agents in the flow of work (like Slack) provide targeted, actionable insights and boost adoption.What is Product Telemetry?: Irina explains how telemetry is evolving from a simple engineering observability use case to a critical data source for AI, machine learning, and recommendation systems.Why Standard RAG Fails in the Enterprise: Irina shares why typical RAG approaches break down on dense, entity-rich corporate data (like Salesforce's help docs) where semantic similarity isn't enough, leading to the rise of Graph RAG.The New, Measurable ROI of Data: How moving from BI to agents allows data teams to precisely measure impact, track downstream actions, and finally have a concrete answer to the ROI question that was previously impossible to justify.Data Teams as Enterprise Leaders: Why data teams are uniquely positioned to lead AI transformation, as they hold the enterprise "ontology" and have experience building products under uncertainty.

It's all about acquisitions, acquisitions, acquisitions! Matt Housley joins me to tackle the biggest rumor in the data world this week: the potential acquisition of dbt Labs by Fivetran. This news sparks a wide-ranging discussion on the inevitable consolidation of the Modern Data Stack, a trend we predicted as the era of zero-interest-rate policy ended.We also talk about financial pressures, vendor exposure to the rise of AI, the future of data tooling, and more.

In this episode, I sit down with Saket Saurabh (CEO of Nexla) to discuss the fundamental shift happening in the AI landscape. The conversation is moving beyond the race to build the biggest foundational models and towards a new battleground: context. We explore what it means to be a "model company" versus a "context company" and how this changes everything for data strategy and enterprise AI. Join us as we cover:Model vs. Context Companies: The emerging divide between companies building models (like OpenAI) and those whose advantage lies in their unique data and integrations.The Limits of Current Models: Why we might be hitting an asymptote with the current transformer architecture for solving complex, reliable business processes. "Context Engineering": What this term really means, from RAG to stitching together tools, data, and memory to feed AI systems. The Resurgence of Knowledge Graphs: Why graph databases are becoming critical for providing deterministic, reliable information to probabilistic AI models, moving beyond simple vector similarity. AI's Impact on Tooling: How tools like Lovable and Cursor are changing workflows for prototyping and coding, and the risk of creating the "-10x engineer." The Future of Data Engineering: How the field is expanding as AI becomes the primary consumer of data, requiring a new focus on architecture, semantics, and managing complexity at scale.

In this episode, I sit down with Ole to discuss his new book, "Fundamentals of Metadata Management." We move past the simple definition of "data about data" to a more nuanced view of metadata as something that exists in two places at once , serving as a pointer to find information elsewhere.Ole introduces his core concept of the "MetaGrid"—the interconnected, yet siloed, web of metadata repositories that already exists within every large organization across various teams and technologies. He argues that the key to better metadata management is not to build a new monolithic system but to recognize, document, and integrate the MetaGrid that's already there, hiding in plain sight.The conversation also covers the impact of the AI hype cycle , the lessons learned from the Data Mesh movement , the sociological incentives that help or hinder metadata projects , and the cultural clash between the worlds of data engineering and library science.

The way we work is changing right in front of us. In this rant, I talk about how I'm seeing AI reshape how technical and non-technical people do their work. The bottom line - there's a lot of room to innovate and evolve your job.

In this discussion, I sit down with data veterans Remco Broekmans and Marco Wobben to explore why so many data projects fail. They argue that the problem isn't the technology, but a fundamental misunderstanding of communication, culture, and long-term strategy.The conversation goes deep into the critical shift from being a "hardcore techie" to focusing on translating business needs into data models. They use the classic "involved party" data modeling pattern as a prime example of how abstract IT jargon creates a massive disconnect with the business.Marco shares a fascinating (and surprising) case study of the Dutch Railroad organization, which has been engaged in an 18-year information modeling "program" - not a project - to manage its immense complexity. This sparks a deep dive into the cultural and work-ethic differences between the US and Europe, contrasting the American short-term, ROI-driven "project" mindset with the European capacity for long-term, foundational "programs".Finally, they tackle the role of AI. Is it a silver bullet or just the latest shiny object? They conclude that AI's best use is as an "intern" or "assistant", a tool to brainstorm, ask questions, and handle initial prototyping, but never as a replacement for the deep, human-centric work of understanding a business.Timestamps:00:00 - Introduction01:09 - Marco Wobben introduces his 25-year journey in information modeling.01:56 - Remco Broekmans reintroduces himself and his focus on the communication aspect of data.03:22 - The progression from hardcore techie to focusing on communication over technology.08:16 - Why is communication in data and IT projects so difficult? 09:49 - The "Involved Party" Problem: A perfect example of where IT communication goes wrong with the business.13:35 - The essence of IT is automating the communication that happens on the business side.18:39 - Discussing a client with 20,000 distinct business terms in their information model.21:55 - The story of the Dutch Railroad's 18-year information modeling program that reduced incident response from 4 hours to 2 seconds.27:25 - Project vs. Program: A key mindset difference between the US and Europe.34:18 - The danger of chasing shiny new tools like AI without getting the fundamentals right first.39:55 - Where does AI fit into the world of data modeling? 43:34 - Why you can't trust AI to be the expert, especially with specialized business jargon.47:18 - The role of risk in trusting AI, using a self-driving car analogy.53:27 - Cultural differences in work pressure and ethics between the US and the Netherlands.59:29 - Why personality and communication skills are more important than a PhD for data modelers.01:03:38 - What is the purpose of an AI-run company with no human benefit? 01:11:21 - Using AI as an instructive tool to improve your own skills, not just to get an answer.01:14:12 - How AI can be used as a "sidekick" to ask dumb questions and help you think.01:18:00 - Where to find Marco and Remco online

Are you a giver or a taker?It seems like every few months, I have to put out a PSA about how I get annoyed at unsolicited pitches from people who are one-sided and transactional. These people are takers. I've got no time for takers.Instead, pay it forward. Give away your ideas in free articles and videos. Mentor people. Create an open source project. Be a friend and a human without asking anything in return. Be a giver.

In this episode, I sit down with Wendy Turner-Williams, a distinguished tech leader and executive with a deep history at companies like Microsoft and Salesforce. She's of the original minds behind what became Azure Data Factory, among other foundational tech. In this wide-ranging conversation, Wendy charts the trajectory from the early days of the Internet to the current AI-driven hype cycle and looming crisis. She explains how these tools of innovation are now being turned against the workforce and why this technological revolution is fundamentally more disruptive than anything that has come before. This episode is a candid, unfiltered discussion about the real-world impact of AI on jobs, the economy, and our collective future, and a call for leaders to act before it's too late.Timestamps:00:22 - Catching up: The tough job market and writing new books. 05:49 - Wendy's impressive career history at Microsoft, Salesforce, and Tableau. 06:17 - The origin story of Azure Data Factory and other foundational projects at Microsoft. 09:18 - A personal story about the challenges of being a woman in Big Tech in the early days. 13:02 - A look back at a favorite early-career project: Digitizing physical maps with nascent GPS technology in 2001. 18:11 - The state of the tech industry: "Tech is cannibalizing itself because of AI." 20:31 - The massive, impending shock to the job market and why AI is different from previous industrial revolutions.27:26 - Why the "human in the loop" is a temporary and misleading solution. 29:55 - Breaking down the numbers: The staggering quantity of white-collar jobs projected to be eliminated. 36:37 - Why leaders are failing to act and conversations are happening behind closed doors without solutions. 38:25 - Discussing potential solutions: Should companies have quotas for their human workforce? 45:21 - The need for "truth tellers" and leaders who are willing to question the current path and drive human-centric transformation. 53:15 - The grim reality for recent graduates with computer science degrees who can't find jobs. 56:22 - The risk of IP hoarding and engineers deliberately crippling systems to protect their jobs.01:00:20 - Final thoughts: Are we waiting for a "let them eat cake" moment before we see real change?

Matt Housley joins me for our monthly chat. This time, we discuss my article The Pedantic Layer, fraud, and more.

There are very few people like Stephen Brobst, a legendary tech CTO and "certified data geek," Stephen shares his incredible journey, from his early days in computational physics and building real-time trading systems on Wall Street to becoming the CTO for Teradata and now Ab Initio Software. Stephen provides a masterclass on the evolution of data architecture, tracing the macro trends from early decision support systems to "active data warehousing" and the rise of AI/ML (formerly known as data mining). He dives deep into why metadata-driven architecture is critical for the future and how AI, large language models, and real-time sensor technology will fundamentally reshape industries and eliminate the dashboard as we know it. We also chat about something way cooler, as Stephen discusses his three passions: travel, music, and teaching. He reveals his personal rule of never staying in the same city for more than five consecutive days since 1993 and how he manages a life of constant motion. From his early days DJing punk rock and seeing the Sex Pistols' last concert to his minimalist travel philosophy and ever-growing bucket list, Stephen offers a unique perspective on living a life rich with experience over material possessions. Finally, he offers invaluable advice for the next generation on navigating careers in an AI-driven world and living life to the fullest.

The AI hype cycle seems to be calming down a bit, and this is awesome. Humanity always craves another hype cycle, so I give some ideas on what I think is next after AI.

Ever wonder how companies in the crowded data and AI space build powerful alliances to drive revenue and growth? In this episode, I sit down with Eleanor Thompson, a partnerships expert based in London and founder of a successful partnerships consultancy. Drawing from her experience running the partner program at Fivetran during its hyper-growth phase, Eleanor shares the essential strategies for building a successful partnership ecosystem from the ground up. We also also discuss the mental fortitude required for entrepreneurship, drawing surprising parallels between running a business and competing in high-intensity fitness events like Hyrox. Tune in to learn:The fundamental reasons why partnerships are critical for expanding your reach and generating revenue. When is the right time for a startup to focus on partnerships (hint: it's not day one). Eleanor's "4A" framework (Alignment, Ability, Audience, Accountability) for identifying the perfect partner. The key roles, from Partner Sales Engineers to Partner Ops, needed to build a successful partnerships team. Red flags to watch for when a potential partner is more focused on margin than customer value. How AI can be used to identify ideal partners and even predict their future success. Find Eleanor Thompson online:LinkedIn: Eleanor Thompson Website: https://branchworks.io #BusinessPartnerships #DataEngineering #AI #Entrepreneurship #Tech #Startup #GoToMarketTimestamps00:49 - Who is Eleanor Thompson? 02:25 - Why Do Business Partnerships Exist? 05:26 - When is the Right Time for a Company to Start Building Partnerships? 06:40 - The 4A Framework for Defining Your Ideal Partner Profile 08:20 - Joe's Experience Partnering with Big Tech 12:33 - How to Structure a Partnerships Team in a Growing Tech Company 20:49 - What is Partner Operations and Why is It a Critical Hire? 22:30 - The Importance of Trust and Referral Fees 25:15 - Eleanor's Journey as an "Accidental CEO" 30:10 - The Mental Fitness and Resilience Required to Be a Founder 41:20 - How to Use AI in Your Partnership Strategy 45:00 - How to Spot a Good Partner on the Very First Call 46:33 - Red Flags to Watch For in Potential Partners 51:35 - How Fitness and Hyrox Competitions Fuel Business Success 59:45 - Where to Find Eleanor Thompson

Is reality setting in for the AI bubble? Who the hell knows. This is definitely the most bipolar bubble I've ever seen, making the dotcom bubble look downright tame. In this rant, I discuss why AI is in its televangelist moment, and why a reality check is necessary to keep real progress on AI on track.

What are the hidden dangers lurking beneath the surface of vibe coded apps and hyped-up CEO promises? And what is Influence Ops?I'm joined by Susanna Cox (Disesdi), an AI security architect, researcher, and red teamer who has been working at the intersection of AI and security for over a decade. She provides a masterclass on the current state of AI security, from explaining the "color teams" (red, blue, purple) to breaking down the fundamental vulnerabilities that make GenAI so risky.We dive into the recent wave of AI-driven disasters, from the Tea dating app that exposed its users' sensitive data to the massive Catholic Health breach. We also discuss why the trend of blindly vibe coding is an irresponsible and unethical shortcut that will create endless liabilities in the near term.Susanna also shares her perspective on AI policy, the myth of separating "responsible" from "secure" AI, and the one threat that truly keeps her up at night: the terrifying potential of weaponized globally scaled Influence Ops to manipulate public opinion and democracy itself.Find Disesdi Susanna Cox:Substack: https://disesdi.substack.com/Socials (LinkedIn, X, etc.): @DisesdiKEY MOMENTS:00:26 - Who is Disesdi Susanna Cox?03:52 - What are Red, Blue, and Purple Teams in Security?07:29 - Probabilistic vs. Deterministic Thinking: Why Data & Security Teams Clash12:32 - How GenAI Security is Different (and Worse) than Classical ML14:39 - Recent AI Disasters: Catholic Health, Agent Smith & the "T" Dating App18:34 - The Unethical Problem with "Vibe Coding"24:32 - "Vibe Companies": The Gaslighting from CEOs About AI30:51 - Why "Responsible AI" and "Secure AI" Are the Same Thing33:13 - Deconstructing the "Woke AI" Panic44:39 - What Keeps an AI Security Expert Up at Night? Influence Ops52:30 - The Vacuous, Haiku-Style Hellscape of LinkedIn

What's the nature of skills and competency when we're all just AI enabled button pushers? I rant about why we need to retain our sense of competence and uniqueness in an age where everything is rapidly becoming a sea of sameness and lameness.

Is AI the silver bullet for modernizing our aging software systems, or is it a fast track to creating the next generation of unmaintainable "slopware"?In this episode, I sit down with Marianne Bellotti, author of the amazing book "Kill It With Fire," to discuss the complex reality of legacy system modernization in the age of AI. We explore why understanding the cultural and human history of a codebase is critical, and how the current AI hype cycle isn't a silver bullet for legacy IT modernization efforts.Marianne breaks down a recent disastrous "vibe coding" experiment, the risk of replacing simple human errors with catastrophic automated ones, and the massive disconnect between the promises of AI agents and the daily reality of a practitioner just trying to get a service account from IT.Join us for a pragmatic and no-BS conversation about the real challenges in software, the practical ways to leverage LLMs as an expert partner, and why good old-fashioned systems thinking is more important than ever.Find Marianne Bellotti:Socials: @BellmarWebsite: https://belladotte.tech/Book, "Kill It With Fire": https://nostarch.com/kill-it-fire

Matt Housley joins me to chat about whether it matters that AI is PhD level, clanker content (the new term for AI slop), a retrospective on Fundamentals of Data Engineering, and much more.

Shane Gibson just published a book - The Information Product Canvas. We discuss his journey as an author and publisher, why shared language is critical in data projects, the iterative processes that can enhance data team efficiency, and the role of canvases in business strategy. We also get into how AI might evolve the landscape of data and publishing.

Does today's use of AI coding agents remind you of a drunken high school or college party? Just like people discovering drugs and alcohol for the first time, I feel like the tech and data industry is in a similar place. "Just vibe..." is the mantra now.But when I talk with developers and data practitioners in private, I get different vibes. There's definitely a concern that we're collectively building lots of slopware and are setting ourselves up for trouble as an industry.

Kostas Pardalis (Co-Founder @ Typedef) joins me to chat about the rapid evolution of AI and data infrastructure, next generation architectures, and much more

In this episode, I unpack how Big Tech is using massive AI investment to justify mass layoffs. All of this AI projects keep failing for the same reasons every other tech wave and IT project has: bad data, org problems, etc. Will AI be different?

Shane Murray (Field CTO Monte Carlo, Former Head of Data NY Times) joins me to chat about the impact of AI on data teams and business strategies, data observability on unstructured data, and more.

I'm very type-A and goal-oriented to a very unhealthy degree. In this rant, I explain how I'm slowly letting go of this and instead focusing on long-term sustainability.

Madison Schott joins me to chat about about her journey from working as an analytics engineer to creating content about dbt, SQL, data modeling, and more.

Are you vibe coding? Using Claude Code, Cursor, etc? I really don't care. I also talk about the power of no, Spartan Races, and more.

Peter Hanssens is an Australia-based data engineer, business owner, and community pillar. He runs Cloud Shuttle, a data engineering consultancy and organizes DataEngBytes, a series of meetups and conferences throughout Australia and New Zealand. We chat about building data engineering communities, running conferences, and much more.

Aaron Neiderhiser (CEO of Tuva Health) has an audacious goal of improving US healthcare analytics. It all starts with the data. Aaron discusses his journey and obsession with making sense of the gigantic mess of US healthcare data.

There have been lots of social media posts declaring things to be dead - SQL, R, data engineering, BI, etc.I give my thoughts on these proclamations, why it's a wrong way to think about our space, and more.

Jeroen Janssens and Thijs Nieuwdo join me to chat about all things Polars. We discuss the evolution of the Polars library, its advantages over pandas, and their journey of writing 'Python Polars: The Definitive Guide.'

In this episode, I rant and speculate about why small teams and companies are the future, AI native data architectures, why the future is awesome, and more.This episode is brought to you by Keboola. Check out their new MCP server at https://www.keboola.com/mcp

Regular guest Gordon Wong joins me to chat about the impact of AI on attention and expertise, product management, and much more.

In this episode, I ramble and speculate about what the next-generation data architectures look like. Thanks for Keboola and GoodData for sponsoring this episode.Keboola: https://www.keboola.com/mcpGoodData: GoodData.com/resources

Zhamak Dehghani (creator of Data Mesh, CEO of NextData) joins me to chat about what she thinks is next in data - autonomous data products, decentralized data and AI, and much more. Zhamak is one of the people I most respect in our industry. She's a once-in-a generation phenomenon who will change the trajectory of our industry.

Matthew Scullion (CEO, Co-Founder of Matillion) joins me to chat about the future of data engineering, namely agentic data engineering teams. What does this new world look like? Matthew shares some ideas of what he's building at Matillion, and the broader context of what agentic AI means for the data ecosystem, teams, and workflows.

Wrapping up the week at Snowflake Summit. As always, the big platform ate away at their partners. If you're a partner, what can you do to shield yourself from platform cannibalization? In this episode, I give some advice from what I've seen in the data ecosystem over the years.

Svetlana Tarnagurskaja (CEO of the Dot Collective) joins me to chat about her entrepreneurial journey in the data space, building a boutique data consultancy, competing with Big Consulting, the challenges of building a team, and the importance of creating a human-centric company culture.The Dot Collective: https://www.thedotcollective.co.uk

Hannes Mühleisen shows off DuckLake and answers live questions.DuckLake: https://duckdb.org/2025/05/27/ducklake.html

Ash Smith is a data engineer based in Perth, Australia (the most remote city in the world). We chat about data products, interoperability, and the importance of collaboration and incentive structures within data teams.

Regular guest Gordon Wong and I chat about what you should think about if you want to work in data. Spoiler - it's probably not what you think.Shoutout to GoodData and MonteCarlo for sponsoring this episode.