Explicit Measures Podcast

Mike & Tommy tackle PADU — Preferred, Accepted, Discouraged, and Unacceptable — and map the four-bucket framework onto Microsoft Fabric, exploring how teams can bring real governance and process architecture to a platform that gives you five ways to do everything.

They weigh in on where Lakehouse, Git, Dataflows, Copilot, and self-service Power BI land on the scale, and why defining what your org will no longer tolerate might matter more than any architecture decision you make this year.

Mike & Tommy tackle the question of whether Power BI should stay an analytics layer or evolve into an operational one, joined by Mike Zack — former CEO of Acterys, now part of Vena — who has built his career around closing the gap between insight and action. They weigh in on when write-back genuinely belongs in a report, who owns the data once users can change it, and what governance has to exist before you flip that switch.

With AI surfacing signals faster than ever, the real bottleneck may not be the dashboard — it's the disconnected workflow that follows it.

Mike & Tommy dive into the biggest announcements from FabCon Barcelona, breaking down how Fabric IQ, agentic app creation, ontology authoring, and a wave of platform updates are reshaping what Power BI and Fabric mean for everyday BI teams. They tackle whether the semantic model has officially become the center of the Microsoft AI universe, and what orgs need to do right now to be ready.

From write-back apps built with natural language to zero-provisioned Fabric and on-demand billing, they weigh in on which features deserve a spot on your 90-day roadmap and which ones need more governance guardrails before anyone touches them.

Mike & Tommy dive into why choosing the right AI model is one of the most consequential decisions a Power BI or Fabric team can make—and why most teams aren't making it deliberately at all. They explore the tension between cost, capability, and governance: when a frontier model is worth it, when a cheaper one is more than enough, and why letting a model review its own DAX is a governance risk hiding in plain sight.

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Mike & Tommy are joined one last time by Kurt Buhler as they dive into the agentic development of Power BI reports, wrestling with why generating a governed dashboard is so much harder than spinning up a web app or 3D model, and whether that friction is a platform problem, a data problem, or a skills problem.

They tackle the biggest misconceptions teams have when pitching agentic BI internally, debate whether dashboards are headed toward becoming ephemeral artifacts, and make their most realistic predictions for what the BI landscape looks like at the edge and in the enterprise core one to two years from now.

Mike & Tommy are joined again by Kurt Buhler to tackle whether calling yourself a "Power BI person" is quietly capping your career in the age of AI, exploring how an AI-powered workflow expands your lens beyond a single tool to the full BI and application space, and why that shift should feel like liberation, not a threat.

They dig into who gets empowered versus who feels replaced, and what it really means to add value when AI is handling more of the build.

Mike & Tommy are joined again by Kurt Buhler and tackle the intersection of data literacy and AI literacy, exploring how self-awareness — or the lack of it — shapes the way teams build and consume Power BI and Microsoft Fabric solutions.

They dig into whether AI raises the floor while lowering the ceiling, why poor data literacy can become more dangerous in an AI-assisted world, and how to keep critical, creative, and curious thinking alive when Copilot is in every seat.

Mike & Tommy are joined by Kurt Buhler as they dive into Fabric Apps and where they actually fit in the Power BI world, weighing whether they replace traditional reports, who owns them in a real org, and whether low code is getting cornered by AI agents.

They break down the pre-requirements for success, the unlocks Fabric Apps offer that reports never will, and why ambition, visual literacy, and planning friction matter more than the tool itself.

Mike & Tommy tackle the Fabric handoff — exploring what it really takes to transition a Microsoft Fabric environment to an organization with no prior Fabric experience, and whether the real challenge is the platform or the change management behind it.

They break down who needs to own Fabric after the consultants leave, what breaks first when a Power BI shop inherits a tenant they didn't build, and how to set a new team up to actually run it well six months later.

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Mike & Tommy dive into how AI is actually changing data visualization work, exploring whether the real unlock is iteration speed rather than AI-built visuals, and why the "no enterprise license required" reality matters more than keynote promises.

They tackle the governance risk of analysts using personal AI tools with real data, debate the conversational analyst vs. architect analyst divide, and land on practical habits Power BI teams can adopt today.

Mike & Tommy break down the latest upgrades to Fabric Data Agents—MCP integration, OneLake Catalog discovery, multi-agent support, and Foundry Observability—and explore whether these changes finally move Data Agents from demo to production-ready.

They weigh in on the real tension: whether easier connectivity creates governance sprawl, how "one agent per domain" reshapes the one-version-of-truth debate, and what BI teams need to have in place before agent chaos takes hold.

Mike & Tommy dive into Liquid Clustering in Microsoft Fabric, breaking down why Runtime 2.0 changes it from an experimental feature into the default table layout strategy for Lakehouse workloads.

They explore how incremental clustering solves the write amplification problems that plagued Runtime 1.3, why file skipping matters for Direct Lake and Power BI report performance, and how teams should think about choosing clustering columns, running OPTIMIZE, and trusting Auto Reclustering without blowing their F-SKU capacity.

Mike & Tommy dive into whether medallion architecture has truly evolved in the Fabric era or whether teams are just re-labeling Bronze, Silver, and Gold with new icons, using this Microsoft Fabric community article as a jumping-off point.

They break down when an all-in-one Warehouse pattern makes sense versus a hybrid Lakehouse approach, and walk away with a practical take on how much Spark you actually need before complicating your design.

Mike & Tommy dive into what data visualization can teach us about critically evaluating LLMs, exploring why fluent, polished AI output can be just as misleading as a beautiful but inaccurate chart. They break down the tension between AI speed and data quality, tackling how BI teams can build a practical framework around accuracy, security, and sovereignty to use these tools without lowering their standards.

Mike & Tommy dive into Power BI Desktop Bridge, breaking down what it actually solves, where it fits in your stack alongside Desktop, Service, Fabric, Git, and deployment pipelines, and what it really takes to make it run like a Ferrari for your team.

They tackle the hard questions — whether Desktop Bridge exposes weak development habits faster than it fixes them, how to keep speed from turning into chaos, and what governance guardrails every BI lead should have in place before rolling it out broadly.

Mike & Tommy dive into anonymous data access in Fabric Apps, exploring how the new capability lets builders deliver public-facing BI experiences without requiring users to sign in — and whether that's a governance breakthrough or a disaster waiting to happen.

They break down which items support it, what tenant admins need to know, and how Rayfin fits into the story of Fabric becoming a true application platform.

Mike & Tommy dive into the emerging pattern of Fabric as a Backend (FaaB), exploring whether Fabric is becoming the default way teams think about their entire data ecosystem or just a front-facing adoption tool.

They weigh in on whether this pattern is a natural evolution of the platform or a case of forcing an analytics engine into an operational role — and what guardrails teams need before going all in.

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Mike & Tommy dive into what agentic development actually means for Power BI teams, breaking down the crawl, walk, run framework for safely adopting AI agents across BI assets. They explore why the shift goes far beyond Copilot prompts, how semantic models become the center of the conversation, and whether most orgs are ready to trust agents with DAX, RLS, and report generation. Tune in for a practical roadmap on where to start, what guardrails are non-negotiable, and how experienced pros stay relevant as agents take on more of the build.

Mike & Tommy dive into the Power BI Desktop Bridge preview, exploring how this new local API surface lets external tools and AI agents interact directly with a running Desktop session — and whether that changes report authoring forever.

They break down the Report Authoring Skill showcase, weigh in on the governance risks of agent-driven edits outside of source control, and discuss what experienced teams should actually test before trusting an edit–verify loop with their models.

Mike & Tommy tackle the hub-and-spoke composite model pattern, weighing in on whether splitting a bloated golden semantic model into a core hub with department-specific spokes delivers real simplification or just trades one problem for another. Prompted by Austin's question about avoiding a 100+ table behemoth, they break down what belongs in the hub, where DirectQuery connections start to hurt, and what governance guardrails teams need before scaling this architecture.

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Mike & Tommy dive into the 2026 Gartner Magic Quadrant for Analytics and BI, breaking down what Microsoft's 19th consecutive Leader recognition actually means in an era where every vendor claims to be AI-first. They explore whether the real story is Power BI, Fabric, or the broader Microsoft ecosystem, and tackle the central tension of whether AI is making BI more powerful or just more confusing. Tune in for a practical take on what data teams should do differently before letting agents answer business questions.

Mike & Tommy tackle a question every data team manager is wrestling with: should junior staff learn DAX fundamentals before touching agentic AI tools, or can they dive straight in? Drawing from a listener in the insurance industry who manages a data team, they explore the tension between building real technical depth and embracing AI-assisted efficiency, and whether letting juniors "vibe DAX" creates dangerous blind spots or a faster path to productivity.

Mike & Tommy dive into Microsoft's RayFin, weighing in on whether this new code-first app experience inside Fabric is a genuine shift in how teams deliver data insights — or just another layer of hype. They explore the tension between RayFin and Power BI reports, tackle who actually owns these apps in the enterprise, and break down what BI professionals should do right now before the next wave hits.

Mike & Tommy tackle whether AI agents can solve what the Power BI admin portal can't—giving you a true tenant-level, workspace-free view of who can access what in Fabric. They break down Taylor B.'s real-world agent that stitches together audit logs, model APIs, grants, direct links, and app audiences into a central governance store, and weigh in on whether Microsoft's native governance story is ready or still a DIY puzzle.

From Purview's role to the risks of letting agents reason over permissions, they explore what it would take to make data governance in Fabric actually feel complete—and what builders like Taylor should ship now versus wait for.

Mike & Tommy tackle the often-overlooked "outer context" problem in agentic Fabric development — exploring why request tracking systems like Notion, GitHub Issues, or even ADO matter more than most teams realize, and how building a proper project harness could be the difference between an agent that delivers and one that drifts.

They break down how to map these workflows across corporate tool stacks — Loop, OneNote, SharePoint — and what a minimum viable harness actually looks like in practice.

Mike & Tommy tackle the surprisingly tricky problem of tracking Power BI app usage at scale, exploring why app-level telemetry is harder to surface than report-level metrics and how teams can map usage back to districts using Entra ID, Admin APIs, and audit logs.

They break down which telemetry sources are actually viable, how to avoid common pitfalls like audience filters hiding true reach and shared devices skewing counts, and lay out a scalable architecture for ~9,000 users across 70 districts built around a centralized semantic model with incremental refresh.

Mike & Tommy tackle why organizations are still dragging their feet on AI adoption even with Microsoft Fabric sitting right in front of them — weighing in on whether the real blockers are skills, data quality, governance, or just unclear ROI.

They break down what "AI-ready" actually looks like in a Fabric and Power BI environment, how to tell AI strategy from AI theater, and what a realistic 30/60/90-day plan looks like to move from experimentation to production.

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Mike & Tommy dive into the second half of the AI self-service analytics story, tackling governance, skills, and validation — and why giving everyone the ability to ask questions of data raises the stakes, not just the speed.

They break down what a "Skill" actually means in Power BI terms, who owns the wrong answer when Claude writes the query, and how BI teams can build validation checkpoints that create trust without grinding the business to a halt.

Mike & Tommy dive into self-service analytics with AI, exploring how tools like Claude are changing the way business users interact with data — and whether speed and access come at the cost of trust and governance.

They break down what semantic models need to look like when chat is the primary interface, who owns accountability when AI gives a wrong answer, and what practical guardrails BI teams should put in place before scaling AI-assisted analytics.

Mike & Tommy tackle the growing tension between individual and team-owned Microsoft Fabric Agent Skills, exploring how BI teams should organize, govern, and promote skills without killing innovation or creating shadow BI.

They break down the full skill lifecycle — from personal experiments to certified team assets — and land on practical governance guardrails that are lightweight enough to actually stick.

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Mike & Tommy tackle whether Power BI developers are quietly becoming professional QA testers in the age of Microsoft MCP, weighing in on what separates a true senior developer from a junior when AI writes the first draft.

They explore how the developer role is shifting, who owns accountability when AI-generated measures ship broken, and what skills still matter when the tool can do the typing.

Mike & Tommy dive into RayFin, Microsoft's new AI-first product for building, deploying, and governing agents inside Microsoft Fabric, exploring whether this is a true shift in how BI teams work or just another layer of hype on day one.

They break down what RayFin actually changes for semantic models, reports, and pipelines, tackle the real governance risks when AI can build and deploy at platform speed, and land on the practical first steps a Fabric team should take before turning it loose in their tenant.

Mike & Tommy dive into Microsoft Build 2026, breaking down what the "agentic apps" announcement actually means for Power BI and Fabric teams—and whether this is a real architecture shift or just rebranded copilots.

They weigh in on how Fabric Data Factory orchestration, Microsoft Databases, and Agent Skills change the day-to-day for BI developers and data engineers, and what governance guardrails teams need before anything touches production data.

Mike & Tommy dive into CI/CD automation with agents in Microsoft Fabric, exploring how agentic workflows are reshaping deployment pipelines, whether AI-driven deployments introduce more speed or more risk, and what guardrails teams need before letting agents touch production workspaces.

Mike & Tommy dive into Claude Design meets Power BI Embedded, exploring whether AI-generated UX is a shortcut or a quality risk, how semantic models stay the source of truth when LLMs scaffold embedded apps, and what guardrails belong on every AI-assisted analytics project.

Mike & Tommy tackle Microsoft Fabric hints to work faster, exploring whether teams are wasting time due to self-inflicted bad workflows, poor reuse, and confusing busy work with shipping value. They break down which repeatable tasks should be standardized first, when "just build it" becomes technical debt, and what practical Fabric habits listeners can adopt this week to stop the waste and start delivering faster.

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Mike & Tommy explore agentic skill and report design, diving into the "Five Minutes to Wow" framework and whether AI agents accelerate great design or amplify bad habits. They confront questions about who owns design decisions when the AI makes the call, how to balance speed with craftsmanship, and what guardrails prevent self-service clutter—ultimately wrapping up with practical takeaways for anyone opening Power BI Desktop tomorrow.

Mike & Tommy dive into whether agentic tooling should be driving your Knowledge Center, exploring if docs, support, and governance content can—or should—be fully automated by AI, and what that means for the future relevance of your CoE.

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Mike & Tommy explore the importance of agentic skills for the Fabric developer, weighing whether Skills are a productivity breakthrough or another governance risk, and discussing how to build, version, and share Skills without sacrificing engineering discipline.

Mike & Tommy dive into Databricks Genie and the growing hype around data agents, exploring whether the real challenge is natural language chat or the semantic layer underneath—and what Power BI teams must fix before any AI agent can deliver trusted, governed answers at scale.

Mike & Tommy weigh in on whether bookmarks in Power BI are still worth the maintenance burden, exploring how field parameters, slicers, and modern features have changed the game, and when you should still reach for bookmarks versus simplifying your data model instead.

Mike & Tommy dive into Building Agentic Tools for Microsoft Fabric With Alex Powers, exploring Microsoft's Alex Powers' new tool 'Taskflow Assistant' for Microsoft Fabric and how AI is transforming the way professionals work with Fabric workflows, discussing whether we're helping users master the platform or avoid learning it altogether.

Mike and Tommy review the most underrated Fabric features based on buzz and client usage

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Mike & Tommy tackle the overfed golden dataset problem—when a single "source of truth" becomes a 100-table, 400-measure monster that takes four minutes to open. They explore why teams fall into the trap of cramming everything into one model, how fear of discrepant numbers drives over-centralization, and what it takes to split a bloated semantic model without breaking executive trust or reverting to data chaos.

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This week on Explicit Measures, we tackle a critical "last mile" problem in Business Intelligence: how do you add narrative context to Power BI reports when the storytellers aren't Power BI users? Our listener has built monthly snapshot reports delivered to multiple business units, but marketing leads need to annotate these reports with context before they reach their final audience. We explore the gap between data delivery and storytelling, challenge assumptions about tool selection, and discuss practical patterns for integrating qualitative commentary with quantitative metrics—without breaking your data governance or forcing non-technical users into unfamiliar platforms.

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Mike & Tommy weigh in on whether Databricks and Microsoft Fabric are converging into direct competitors, exploring how Databricks' push into BI with Genie and AI capabilities is closing the gap on Power BI's presentation layer. They question whether "end-to-end" platforms are the future or just feature bloat, discuss where semantic models should live in a modern data stack, and help teams decide when to bet on one platform versus embracing the dual-stack reality.

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Mike & Tommy dive into improving AI skills for Fabric developers, exploring whether to start with prompt engineering or semantic modeling fundamentals, how to build real agents without organizational buy-in, and when AI tools actually add value beyond well-built reports. They tackle the tension between learning cutting-edge capabilities and mastering core BI craft, offering a practical 90-day roadmap for developers stuck in slow-adoption environments.

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