"The team still exists, but it's not five humans, it's one human and four agents." Scott Breitenother on the Data Renegades Podcast on why Kilo's operating model ditches the traditional team structure. At Kilo, each person owns a feature end-to-end. Pedro, the sole data person, runs the full data stack using Kilo and the dbt MCP server. Suresh owns code reviews from conception to production. The collaboration happens with agents, not mandatory check-ins with peers. Scott credits PostHog for articulating something similar, but takes it further: "Too often, humans use collaboration as a safety blanket." The unit of productivity has changed. One experienced person plus a fleet of agents ships more than a team of specialists waiting on each other. Listen to wherever you get your favorite podcasts. #DataEngineering #AIAgents #DataTeams #Analytics

At Kilo, new engineers ship code on their first day. A working MVP feature by end of week. In production the next week. Scott Breitenother on Data Renegades Podcast on what happens when writing code is no longer the bottleneck. "We've tried to ruthlessly hunt down every unnecessary decision gate, every kind of comfort blanket that organizations put in to make people feel comfortable." The pattern he sees with every new hire: intimidation on day one, validation when given real ownership, then flight. Nobody wants to spend their time asking for permission. Once the gates come down, people move at what Kilo internally calls "kilo speed." The developers aren't working 24-hour days. They're just using agents and nobody is asking them to check in every five minutes. Listen to the full episode with Scott wherever you get your favorite podcasts. #AIAgents #Engineering #DataTeams #Analytics

“I’m a very big data guy. I like to drive decisions with data, but it has to be the right data.” In case you missed it, on Ep. 2 of the Measured Impact Podcast, we sat down with Alex A Pilkington, CEO of the IM ABLE Foundation. Alex shares how his team focuses on the metrics that actually matter—building supportive communities, removing barriers to participation, and asking the right questions behind the data. 🎧 Listen to the full episode to hear more: https://lnkd.in/eYBB85Xg

More Metrics = Worse Decision Making. In a data driven world, it’s easy to think that more information means being more informed and equipped to make better decisions. But the reality is, the opposite of that is true. Watching business leaders say they’re “data driven” or “making decisions through data” is 9/10 times corporate jargon for: “our spreadsheet looks cool & colorful” Truthfully, decisions are made with *accurate* & *precise* analysis. The numbers you were looking for were tested by a specific hypothesis. Want better decision making? Start with your mission & vision. Make sure those are well defined. Then when you’re looking for insight, ask questions and form a hypothesis. Then set up metric systems to inform you on your decision. Only then, will you be truly data driven. If you liked these insights, I share these kinds of thoughts on the Modern Ventures Podcast, Mondays 6AM EST. Check it out! #dataanalytics, #data, #dashboards, #podcast, #projectmanagement

Nobody should own customer data. Everyone should. At LOVEFiLM, Simon Calver pinned customer complaints in the reception. Every Friday, staff received the top five positive and negative responses. By five o'clock, the negatives were fixed. On our latest episode of Enter The Boardroom, Simon argues that as businesses scale, data gets siloed. Divisional heads claim it. Functional heads guard it. And the customer gets lost. 👉 Episode link in comments below For more insights like this, search "Enter the Boardroom" wherever you listen to podcasts. #EnterTheBoardroom #CustomerCentricity #BoardEffectiveness #CorporateGovernance #ScaleUp

Everyone celebrates winners. That’s the least interesting part. Real progress comes from being wrong. Repeatedly. What still catches teams off guard: Even with strong data and clear customer insights, you arrive at the wrong solution. Not because the data is flawed. Because the interpretation is. If you’re not forcing yourself to explain why something failed, you’re not experimenting. You’re guessing without progress. I unpacked this and more with Florian Möller on his podcast → https://lnkd.in/dHG8aakg

Every CIO and CDO has been there: You present a dashboard to the board, and the first question isn’t about strategy—it’s "Are these numbers actually correct?" In Episode #2 of the Just Do-it Right podcast, we sit down with our guest Doug Martin, MBA, Client Executive to bridge The Data Trust Gap. We aren't just talking about the problem; we're giving you the blueprint to fix it. Inside the episode, we discuss: 🛠️ Why expensive data stacks are still failing to produce "the facts." 🔐 The intersection of Data Trust and Data Protection. 📈 A CIO’s guide to ensuring data integrity from source to screen. Listen now on Spotify. Preview of Episode #2 - The Data Trust Gap: https://lnkd.in/efm_5EFF #CIO #CDO #DataTrust #JustDoItRight #DataGovernance

When forecasts get too complex, clarity disappears. On an episode of Run the Numbers, New Era CFO Kevin Drost makes the case for ditching black-box forecasting models in favor of something far more effective: simple, transparent assumptions you can actually pressure-test. Kevin explains why over-engineered models often create false confidence – and how straightforward forecasting helps leaders make better decisions when conditions change (as they inevitably do). In the conversation, he also covers: 📈 How forecasting discipline should evolve as companies scale 🧠 Why clarity matters more than “perfect” precision 💬 How CFOs can manage investor expectations without losing realism For founders and finance leaders navigating growth, it’s a timely reminder: the best forecasts aren’t flashy – they’re usable. 🎧 Watch or listen to Run the Numbers wherever you get your podcasts. Links in comments. 

When a customer says, “We don’t use sprinters or box trucks”, it usually doesn’t mean the demand isn’t there — it means no one has mapped where that demand is hiding.🗺️ That’s where a consultative STL approach changes the conversation: look at your data, spot the smaller shipments getting forced into bigger equipment, and identify the loads that would move faster (and often more economically) with a dedicated small truck. ----------- 🎧 Find to the full episode on your favorite platform: ▪️YouTube: https://lnkd.in/dD25G38w    ▪️Spotify: https://lnkd.in/du7ytAgE    ▪️Apple Podcasts: https://lnkd.in/d6caVTDx    ▪️Amazon Music: https://lnkd.in/dcJ_HmpU   #SmallTruckload #STL #ExpeditedFreight #FreightFraud #LogisticsPodcast

Often the opportunity isn’t whether STL capacity exists, it’s whether anyone has taken the time to analyze the data and uncover it. A consultative approach can reveal shipments that move faster and more efficiently with the right equipment. 💥💥💥