Raw Data By P3 Adaptive

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!

Ever find yourself overthinking a problem until you’ve thought yourself right into a corner? Yeah, we’ve been there too. But what if there was a way to cut through the noise and just know when something is a good idea? Enter Miller’s Theorem, a simple but sneaky-effective thought experiment that’s been kicking around in our conversations for years. It’s the kind of thing that sounds like nonsense at first until it doesn’t. In this episode, we unpack how a casual visit to a bougie home décor store in Seattle turned into an existential crisis over tariffs, pricing psychology, and whether customers actually care if their overpriced alpaca throw just got 25% more expensive. But it’s not just about economics. It’s about how we make decisions, avoid self-inflicted complexity, and maybe stop outsmarting ourselves into bad choices. Also on the table: the contrapositive, the dangers of taking political soundbites at face value, and why abolishing the IRS is an idea so catastrophically bad it might actually make the Great Depression look like a mild inconvenience. It’s a wild ride through logic, business, and just enough existential dread to keep things interesting. Listen now, then hope over to LinkedIn and tell us what you think! Got a topic you would like to have Rob and Justin cover? Join our Raw Data by P3 Adaptive Steering Committee and let us know!   Also in this episode: Pokerbots, Adware, and Burning Man, w/ Brad Miller & Kai Hankinson

For years, dashboards have been the go-to for business intelligence. Built with the best intentions, loaded with visuals, and meant to empower decision-makers. But are they actually delivering? A recent Reddit thread raised a tough question: "Has anyone here moved away from dashboards as their main source of insights?" It’s not the usual “Dashboards are dead” hype. It’s a real challenge from BI pros who build them. The problem? Analysts say dashboards can’t keep up with how they really explore data. Executives and business users aren’t using them as intended, leading to more ad hoc requests, not fewer. The result? Dashboards end up underused, misused, or worse, ignored. In this episode of Raw Data, Rob and Justin cut through the noise and get to the heart of the issue. Are dashboards the problem, or is it how we’re designing and using them? And more importantly, what’s the right way forward? Also in this episode: Reddit discussion