Raw Data with Rob Collie

Everyone’s talking like AI is coming for your job. Rob’s here to tell you it’s coming for your skill set. If you already think in tables, models, and relationships, congratulations—you’re built for what’s next. This episode is part pep talk, part reality check, and all proof that data people aren’t getting replaced. We’re getting promoted. Rob takes you inside Microsoft’s campus and out into the real world, where big firms are burning millions on AI theater while mid-market teams quietly pull ahead. The twist? You don’t need to change the AI to win with it. You just need to feed it the right data. That means the same instincts that made you good at Power BI now make you dangerous—in the best way. This one’s for the data geners, the ones who never flinched at a gnarly spreadsheet and always saw potential where others saw pain. AI isn’t a threat. It’s your next playground. Listen in and meet the only cavalry that’s actually showing up: you. Check out the first episode of Raw Data with Rob Collie and get ready to lead the AI charge.

Nobody loves requirements docs. They’re the corporate equivalent of writing a novel just so someone can skim the back cover. The real question is whether you can ditch all that and go straight from “here’s what I need” to a working Power BI model. In this episode, Rob and Justin push AI into that role and see what breaks, what builds, and what actually saves you time. Turns out, the magic isn’t in making AI look impressive on a demo slide. It’s in whether it can wire up tables, relationships, and measures fast enough that your team can skip the plumbing and jump right to the good part: asking “does this answer the question?” instead of “why won’t this table join?” That’s the test, and it’s the only one that matters. From tools that feel like friendly appliances to those that lean full hacker-mode, Rob and Justin run the gauntlet. They even crack open Copilot’s inner workings to see how answers really get formed. It’s a gritty look at whether AI can finally cut the “first-hour tax” every project pays and give leaders a faster path to value.

Large language models aren’t magic. They’re pivot tables for words. That’s the real breakthrough — not a crystal ball, not a robot overlord, but a new way to roll up all the noise in your business into something you can actually use. And that’s why AI belongs in the middle layer. Just like BI gave you visibility across systems, AI is becoming the connective tissue for all the unstructured stuff that never fit neatly in a database. Sure, every product is rushing to bolt on “AI features,” but those sidebars and pop-ups can only see the data inside their own walls. The real power shows up when you wire AI across the mess — the emails, the docs, the meeting notes, the structured and unstructured side by side. That’s where pivot-table-for-words meets pivot-table-for-numbers, and suddenly you’re not staring at silos. You’re staring at the whole picture. Rob and Justin cut past the hype to show why AI isn’t the star of the show, it’s the glue in the middle. And that’s good news, because the middle is where business actually gets done. Hit play and hear why the future of AI is less wizardry, more wiring — and why that’s exactly what makes it work.

This isn’t another AI think-piece, it’s a full-on data brawl. Copilot is out here plagiarizing Rob’s pivot table crusade while the self-appointed nerd police try to lock down the definition of agentic AI. Meanwhile, thirty years of fantasy football become the unexpected proof that tuning beats buzzwords every single time. What starts as a slip from Copilot turns into a bigger story about how AI really works. Off-the-shelf tools can sound impressive, but they collapse into clichés when they’re not tuned to the person using them. The difference isn’t just in efficiency, it’s in credibility. Get it right, and AI amplifies your voice. Get it wrong, and you sound like everyone else mailing it in. Don’t settle for AI that sounds like everyone else. Listen in and hear what happens when tuned workflows collide with real-world stakes.

Pivot tables finally auto-refresh. Humanity wins, but our host Rob Collie is still annoyed. Why? Because he asked for it back in 2007 and got shot down. That little gripe kicks off a bigger conversation: what else in data is overdue for a shake-up? Copilot might be next. Forget filters. Forget dashboards. Rob put it to the test on his beer-league hockey stats and found himself asking questions faster than any report could keep up. It felt natural, almost too natural. Then came the twist. Copilot served up an answer that looked perfect… and was completely wrong. If it can fool the guy who built the model, what chance does anyone else have? Listen in for the laughs, the lessons, and the curveballs of a future where filters may finally be obsolete.

Every campaign has it: that shiny "more reach, lower cost" lever that looks like marketing gold but really just siphons your budget into digital quicksand. We pulled it. The metrics looked fantastic. And that's exactly how we knew we'd been had. In this episode, Rob breaks down a real-world lesson in false signals, phantom clicks, and why data discipline isn't just consultant-speak—it's your financial survival strategy. We're talking about the bots that sneak in through "partner sites," how they corrupt your retargeting, and the ripple effect that turns good data bad across your entire funnel. Here's the thing: The real damage isn't the wasted spend. It's what corrupted data does to your decision-making. When you're building your strategy on phantom signals, every "optimization" takes you further from real results. If you're protecting a budget or leading a team that depends on clean data to drive real business decisions, this episode cuts straight to what matters: spotting the traps before they drain your resources and rebuilding trust in the numbers that actually count.

AI is rewriting the rules of analytics. Copilot can pull answers straight from your semantic model and bypass the dashboard entirely. But for all the tech fireworks, the same old truth holds: communication is still the hardest part. Stakeholders don’t always know what they want, builders don’t always know how to translate it, and requirements docs have never fixed that gap. Copilot just puts the tension in sharper focus. Rob and Justin dig into why vanishing chat histories aren’t just inconvenient, they erase the most honest record of what stakeholders actually care about. Screenshots and Word docs are a band-aid, not a solution. Persistent, shareable conversations could change the way model developers and business users collaborate, but only if governance and security evolve fast enough to keep up. Along the way, they show why usage data from Copilot queries is miles ahead of click stats on a dashboard and why the story of your data has always hinged on the same thing: people understanding each other. Dashboards may have set the stage, but conversation is where the real action is. Listen now and see what happens when the chat itself becomes the deliverable.

Two hundred episodes in, and we're done with the warm-up! Episode 200 finds Rob flying solo and pulling zero punches on the question everyone's quietly asking: what's the future of data work? No anniversary nostalgia here, just uncomfortable truths about AI bootcamps at Starbucks, semantic models going naked, and why being "pretty good" at anything is about to get very complicated. If you think you know where this is all headed, think again. Rob spent yesterday turning a non-techie real estate agent into an AI power user, and what he saw exceeded anything from the early Power BI days. But here's what nobody's talking about: the invisible barriers, the shifting skill requirements, and why the middle ground might be disappearing faster than anyone realizes. The lines between data work and software work are blurring, structured versus unstructured data is becoming meaningless, and the comfortable assumptions about who does what are about to get stress-tested. Two hundred episodes of calling it straight, and this one tackles the questions that keep data professionals up at night. Some answers might surprise you. Others might make you uncomfortable. [But you'll know exactly where you stand when the dust settles]

Most of us have been in the trenches long enough to know when something's about to flip the script. And brother, we're standing at the edge of a cliff most data folks don't even see coming. Rob Collie thought he had Power BI figured out. Then Copilot did something impossible; it cracked a question that should've left it scratching its digital head. But it didn't just answer. It nailed it. That's what we're calling the Dobie Moment—when AI stops being a fancy calculator and starts being genuinely scary-smart. Here's the thing nobody's talking about, your semantic models aren't just sitting there anymore. They're waking up. And when Rob and Justin break down what happened in this episode, you'll see exactly why that should make you sweat a little. They're not here to blow smoke. They'll show you the magic, sure, but more importantly, they'll show you where the landmines are buried. Because when AI starts connecting dots you didn't even know existed, confidence and correctness become two very different animals. Bottom line: The future of data just knocked on your door. You can pretend you didn't hear it, or you can listen to this episode and actually be ready when your models have their own moment of reckoning. Your call. But don't say we didn't warn you.

AI looks unstoppable… until you hand it a hundred pages of meeting notes. Rob and Justin dig into why context windows and token limits quietly run the show. That “million-token” brag from Google? More like weighing the Titanic in bananas. From Shakespeare to SharePoint, this episode shows why AI remembers the Roman Empire better than your company history—and why that’s not a bad thing. Rob also introduces Griff, a digital colleague that fires off P3-flavored ideas like it’s had three espressos. It’s practical AI that’s actually fun to use. Hit play to find out where AI is brilliant, where it falls flat, and how to make it work for you without the hype. Also on this episode: Million Token Context Windows? Myth Busted—Limits & Fixes

Back in 2010, Tableau beat smarter tools with a better demo. No brain, all charm and the market loved it. Fast-forward to now: same playbook, new costume. The AI dashboard crowd is selling “natural language BI” with zero semantic model, zero memory, and a whole lot of LinkedIn swagger. In this episode, Rob and Justin revisit why Tableau’s empty-calorie approach won the first round, and how that same mistake is about to flood the AI + BI space all over again. Turns out, you can still sell snake oil if you call it GenAI. Rob breaks down how an elite MIT course managed to skip LLMs entirely, how a flashy Tableau blog post went viral for connecting a CSV, and why “AI-ready” vendors keep duct-taping chat interfaces onto raw SQL and hoping no one looks under the hood. But the real story? Microsoft is sitting on the most powerful data brain in the game, and if they land the front end, it’s game over. This isn’t just a history lesson. It’s a blueprint for seeing through the hype and betting on what actually works. If you’re building, buying, or betting on AI tools, listen in before you get dazzled by the demo. Also on this episode: Early Experiments in Tableau’s New MCP Service

Let’s say your business runs on sun, sweat, and schedule precision. You’ve got crews in the field, materials that don’t wait, and about 90 minutes to get it right before the product turns into a thousand-pound paperweight. That’s the world Joseph Graziano lives in. He’s the CFO of Quadrant Concrete and also the guy keeping the trucks moving, the forecasts dialed in, and the safety records spotless. Because in the mid-market, you don’t get extra people. You get extra resourceful. Joseph shares how he helped transform a boots-on-the-ground concrete business into a data-forward operation without fancy titles, inflated budgets, or a fleet of consultants. From field-collected data to real-time cashflow forecasting, he’s found the sweet spot where better reporting leads to smarter, calmer decisions. You’ll also hear why operational transparency isn’t just about ROI, it’s about reducing chaos, building trust, and creating a culture where everyone sees what matters. If you’re trying to lead your business through complexity without adding complexity, this episode can be your blueprint. Data doesn’t have to be fancy to be powerful. It just has to work. Listen now and see what it looks like when grit meets insight.

It started as a side project. Rob Collie built a Power BI model for his rec league hockey team. Just for fun. Just to see what the data could say. But something weird happened. The dashboards were solid. The data model was solid. But, the users still had questions. And lots of them. And that’s when it clicked: people don’t think in slicers. They think in questions. Natural ones. The kind dashboards rarely anticipate. In this episode, Rob and Justin Mannhardt didn’t just talk about Microsoft’s Copilot for Power BI. They put it to the test. No tuning. No prep. Just a raw semantic model paired with real questions from actual humans. The result? A glimpse at what happens when the tech finally meets the moment. Copilot isn’t just a gimmick. It understands nuance, handles filters, and points people to the answer without making them dig. And it’s getting better by the day. This isn’t a future-state conversation. You’ve already done the hard part. Now you can build on it. And if you’ve been wondering when AI will start delivering real value, this is a pretty good place to start. Also in this episode: Indy Inline Hockey Dashboards Inline Analytics Doesn’t Mean What You Suspect it Means, w/Ryan Spahr Copilot for Power BI Rethinking the ROI of Dashboards

AI wants your data, but it can't handle the truth . . . “We’ll just plug our data into ChatGPT.” Sure. Sounds easy. Rob thought so too, right up until he hit token limits, had to install Python, and discovered that even two megabytes of transcripts was too much for the world’s smartest models to handle. In this episode, Rob and Justin break down what happens when you try to use AI on your company’s internal data. Spoiler: it’s a lot more complicated than the vendors make it sound. From semantic search to retrieval augmented generation (RAG), they unpack why the dream of “ask AI anything about your business” keeps falling short. You’ll hear why your Power BI model isn’t going anywhere, why structured data still needs old-school engines, and what it really takes to get value from your own information. Somewhere between a cautionary tale and a tech detective story, this one tells it like it is . . . unapologetically. Run into the same situation? We’d love to hear about it! Give us a shout on LinkedIn and tell us how you overcame the limitations.

For a while, you could pretend AI was still a someday problem. Not anymore. Rob Collie and Justin Mannhardt are back, and this time they are tackling Model-Context-Protocol (MCP) and Multi-Agent Systems,  two shifts that could finally put an end to the human copy-paste Olympics. This isn’t about shinier tools. It is about AI that plugs in without the duct tape and starts doing the work without making you babysit. Rob and Justin dig into what’s real, what’s coming, and why "waiting to see" is no longer a strategy. MCP is being called the USB-C of AI. Multi-Agent Systems are making AI check its own work, so you do not have to. Translation? The gap between "early" and "too late" is closing fast, and the status quo isn’t going to cut it much longer. If you are tired of the hype but know you can’t sit this one out, this episode is the advice you have been waiting for. So, tune in and enjoy! If you like what you hear, don’t forget to leave us a review on your favorite podcast platform. Your feedback helps new listeners discover the show! Also in this episode: The Cybernetic Teammate Déjà vu Cat | The Matrix

Sakiko Stickley joins Raw Data to share a story that is part inspiration, part revelation, and a whole lot of truth-telling. From the first moment she discovered Power Pivot, Sakiko did not just learn data. She lived it. She rewired reporting systems, survived micromanagers, and navigated the strange realities of big consulting firms, all while quietly proving that one person, one model, and a little bit of DAX can change everything. In this episode, we get an inside look at how a passion for smarter systems can collide with corporate inertia, what it really feels like to challenge the status quo, and why Sakiko believes AI could someday outperform human leadership, not just in data crunching but in ethics and decision-making too. (Spoiler: She might be right.) If you have ever felt like a lone voice in a world that clings to inefficient processes, Sakiko’s journey will feel like a kindred spirit calling from across the data universe. Listen in for a conversation filled with hard truths, breakthrough moments, and a reminder that true data people do not just build models. They build better futures.

What’s the value of a long-awaited feature? Well, that depends. Have you ever tried explaining a fiscal calendar that doesn’t believe in months? This week, Rob Collie and Justin Mannhardt dive into the latest Microsoft Fabric updates, including the long-rumored, almost-mythical custom calendar support in DAX, the highly requested user-defined functions, and the Copilot expansion that might finally be worth the hype. These updates aren’t just bells and whistles; they’re fixes to problems that have been quietly driving your team up the wall for a decade. And while the tech is cool, the real story is what it unlocks for the people trying to build reliable reporting, reuse their logic across models, and stop wrestling with edge cases in Excel at 10 p.m. If you’ve ever been told, “that’s just how it works,” this episode is a breath of fresh air, and a reminder that progress doesn’t always come with a parade. Sometimes it shows up in patch notes.

What happens when you hand off your Power BI output to ChatGPT and ask it to make sense of your world? You might be surprised. This week, Rob shares a deeply personal use case. One that ties together two major themes we've been exploring: Gen AI is reshaping the way we think about dashboards. To get real value out of AI, you need more than just data. You need metadata. And yes, that kind of metadata—the kind you create in Power BI when you translate raw data into something meaningful. Along the way, we revisit the old guard of data warehousing. The mighty (and now dusty?) ETL priesthood. And we uncover a delicious little irony about how the future of data looks a lot like its past, just with better tools and smarter questions. The big twist? We're all ETL now. But the "T" might not mean what you think it does anymore. Listen now to find out how a few rows of carefully modeled data, a table visual, and one really good AI assistant changed the game. For Rob and, just possibly, for all of us. Also in this episode: Blind Melon – Change (YouTube) The Data Warehouse Toolkit Raw Data Episode - The Human Side of Data: Using Analytics for Personal Well-Being

AI agents are making big waves, but are they the future of business or just another passing trend? In this episode, Rob Collie and Justin Mannhardt explore the rise of AI agents, what they actually do, and why the excitement might be a little premature. They unpack the risks, the rewards, and how leaders can navigate the hype with a bit of caution and a lot of curiosity. Rob and Justin discuss the fine balance between automation and human oversight, tackling questions about when it's smart to embrace AI and when it might be better to pause. They also share thoughts on the current SaaS landscape, where new AI tools are popping up fast and why it pays to be thoughtful before jumping in. Ultimately, this episode is about finding clarity in a fast-moving space. It's about understanding where AI agents can add real value, where they might introduce unnecessary risk, and why critical thinking still matters. Rob and Justin reflect on the challenges of trusting AI, the dangers of locking into technology too early, and how the best decisions come from balancing curiosity with skepticism. If you're wondering how to separate the genuine innovations from the passing fads and how to be strategic about adopting AI in your business, this conversation is for you.

Listener John sent in a classic question: should you build one all-encompassing data model, or should each part of your business have its own? If you think the answer is black and white, well, welcome to the world of data modeling, where nuance reigns supreme. Rob and Justin dive deep into the pros, cons, and inevitable mistakes of both approaches. Along the way, they call out the biggest modeling traps, expose the myths that make projects stall, and introduce a revolutionary new acronym: JGS (Just Get Started). Because, spoiler alert, the best data model is the one that actually gets built. This is an episode you won’t want to miss!