『M365 Show with Mirko Peters - Microsoft 365 Digital Workplace Daily』のカバーアート

M365 Show with Mirko Peters - Microsoft 365 Digital Workplace Daily

M365 Show with Mirko Peters - Microsoft 365 Digital Workplace Daily

著者： Mirko Peters - Microsoft 365 Expert Podcast
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 The M365 Show – Microsoft 365, Azure, Power Platform & Cloud Innovation Stay ahead in the world of Microsoft 365, Azure, and the Microsoft Cloud. The M365 Show brings you expert insights, real-world use cases, and the latest updates across Power BI, Power Platform, Microsoft Teams, Viva, Fabric, Purview, Security, AI, and more. Hosted by industry experts, each episode features actionable tips, best practices, and interviews with Microsoft MVPs, product leaders, and technology innovators. Whether you’re an IT pro, business leader, developer, or data enthusiast, you’ll discover the strategies, trends, and tools you need to boost productivity, secure your environment, and drive digital transformation. Your go-to Microsoft 365 podcast for cloud collaboration, data analytics, and workplace innovation. Tune in, level up, and make the most of everything Microsoft has to offer. Visit M365.show.

m365.showMirko Peters 政治・政府
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  • Stop Cleaning Data: The Copilot Fix You Need

    Stop Cleaning Data: The Copilot Fix You Need
    2025/10/31
    The Data Cleanup TrapYou think your job is analysis. It isn’t. It’s janitorial work with better branding. Every spreadsheet in your life begins the same way—tabular chaos pretending to be data. Dates in six formats, currencies missing symbols, column headers that read like riddles. You call that analysis? That’s housekeeping with formulas.Let’s be honest—half your “reports” are just therapy for the punishment Excel inflicts. You open the file, stare into the abyss of merged cells, sigh, and start another round of “Find and Replace.” Hours vanish. The terrible part? You already know how pointless it is. Because by the time you finish, the source data changes again, and you’re back to scrubbing.Every minute formatting cells is a minute not spent extracting insights. The company pays you to understand performance, forecast trends, and drive strategy. Yet most days you’re just fighting the effects of institutional laziness—people exporting garbage CSVs and calling it “data.”Here’s the twist: Excel Copilot isn’t a cute chatbot for formulas. It’s the AI janitor you’ve been pretending to be. It reads your mess, understands the structure, and cleans it before you can reach for the “Trim” function.By the end of this, you’ll stop scrubbing like an intern and start orchestrating intelligent automation. Oh—and we’ll eventually reach the single prompt that fixes eighty percent of cleanup tasks… if you survive the upcoming CSV horror story.Section 1: Why Excel Is a Chaos FactoryExcel was never meant to be the world’s data hub. It was built for grids, not governance—a sandbox for accountants that somehow became the backbone of global analytics. Small wonder every enterprise treats spreadsheets like a duct-taped database. Functional? Yes. Sustainable? About as much as storing medical records on sticky notes.The flaw starts with human nature. Give an average user a column and they’ll type whatever they like into it. December 3 becomes 03-12, 12/3, or “Dec third.” Some countries reverse day and month; others write it longhand. Excel shrugs, pretends everything’s fine, and your visuals later show financial spikes that never happened.Those invisible trailing spaces—oh yes, the ghosts of data entry—break lookups, implode joins, and silently poison automations. You think your Power Automate flow failed randomly? No. It met a rogue space at the end of “Product Name” and gave up.Then there’s the notorious mixed-type column. Numbers acting like text. Text pretending to be numbers. A polite way of saying: formulas stop working without warning. One cell says “42,” the next says “forty-two.” You can’t sum that; you can only suffer.Every inconsistency metastasizes as your spreadsheet ages. Excel tries to please everyone, so it lets chaos breed. That flexibility—the ability to type anything anywhere—is both its genius and its curse.Now, extend the problem downstream. Those inconsistencies aren’t isolated; they’re contagious. A Power BI dashboard connected to bad data doesn’t display trends—it manufactures fiction. Power Automate flows crumble when a column header changes by one character. Fabric pipelines stall because one table used “CA” and another wrote “California.”I once saw a manager spend three days reconciling regional sales. She was convinced her west-coast numbers were incomplete. They were fine; they were just labeled differently. “California,” “Calif.,” and “CA” politely refused to unify because Excel doesn’t assume they’re the same thing. By the time she found it, the reporting deadline had passed and the leadership team had already made a decision based on incomplete figures. Congratulations—you’ve automated misinformation.Excel’s architecture encourages this disaster. It has no schema enforcement, no input validation, no relational discipline. You can design a formula to calculate orbital mechanics but still accidentally delete a quarter’s worth of invoices by sorting one column independently. It’s like giving a toddler algebra tools and then acting surprised when the living room explodes.These flaws wouldn’t matter if Excel stayed personal—one analyst, one sheet. But it became collaborative, shared via OneDrive, circulated through Teams, copied endlessly across departments. Each copy accumulates its own micro‑mutations until no one remembers the original truth. The spreadsheet becomes a family heirloom of errors.And then, in desperation, we export the mess into Power Platform, expecting automation to transcend lunacy. Spoiler alert—it doesn’t. Flows break, connectors fail, dashboards lie, and you blame the platform instead of the real culprit: the spreadsheet habit.That’s the swamp Copilot was trained to drain. It doesn’t judge your column naming skills or your inconsistent capitalization; it just reads the chaos, classifies the problems, and offers to fix them. Excel remains ...
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    23 分
  • Fix Power Apps Data Entry: Use THIS AI Agent

    Fix Power Apps Data Entry: Use THIS AI Agent
    2025/10/30
    The Data Entry NightmareLet’s start with something familiar — the Power Apps form. Every organization has one. Rows of text boxes pretending to be productivity. You click “New Record,” a form opens, and suddenly you’re not an analyst or a manager. You’re a typist. Copying names, phone numbers, addresses, maybe from an email that someone forwarded, maybe from a PDF invoice that refuses to let you copy cleanly. This isn’t digital transformation. It’s clerical labor with branding.Now multiply that by hundreds of records. Each one entered manually, each one a potential typo waiting to ruin your reports later. The average user calls it “filling forms.” Professionals, however, know the truth — it’s slow, error-prone data decay, and it happens daily across every Power App ever built.And yet, Power Apps insists on those same rigid fields because, well, someone has to enter the data… right? Wrong. Enter the AI Data Entry Agent — and suddenly, the whole miserable ritual collapses.Why Traditional Power Apps Forms FailTraditional Power Apps forms are a triumph of structure over sanity. They promise governance, validation, and consistency, but what they actually deliver is the illusion of control wrapped in user frustration. Every form designer knows the pain: text inputs aligned like soldiers, drop-down menus cloned from Dataverse tables, and those “required” asterisks that audibly sigh when someone forgets them.You build a “customer onboarding” form. Ten fields should be easy. But then finance wants two additional fields, sales wants three optional notes, and compliance insists every address follow a specific format. Suddenly your minimalist form looks like it was designed by a committee of auditors. Users stop reading; they tab blindly through fields like they’re trying to finish an exam they didn’t study for.And accuracy? Forget it. Data doesn’t start clean — it arrives as emails, chat logs, scanned documents, screenshots, half-finished Excel sheets. Each requires manual interpretation before those neat form fields ever see a keystroke. The result is garbage in, garbage out — only slower. Even when you paste in text, you still have to carve it apart. Name in one box, phone in another, and heaven help you if there’s a middle initial because now validation fails.Power Apps forms were never built for unstructured input. They’re databases disguised as paperwork. And that matters because the modern business world runs on unstructured content. The average customer record might originate in an Outlook thread, a Teams chat, or a photo of a business card someone snapped in a meeting. Expecting humans to manually normalize all that feels like asking accountants to do math on napkins.The consequence isn’t just inefficiency — it’s inaccuracy. The longer a human touches the data, the more opportunity for deviation creeps in. Typos, inconsistent abbreviations, blank fields. The cost cascades through reports, dashboards, and automated flows. “Why do our customer counts never match Power BI?” Because Susan misspelled Contoso twice. The system didn’t catch it because it was syntactically correct, just semantically wrong.And yet, this failure perpetuates. Admins add more validation rules. Makers add more labels explaining what to type. Trainers create tutorials teaching people how to copy information correctly — as if accuracy were a skill problem instead of a design flaw. What Power Apps needed wasn’t a better form. It needed a smarter interpreter — one that could read context, understand meaning, and populate fields without making the user think. That, at last, is what the AI Data Entry Agent delivers.Meet the AI Agent: Overview and CapabilitiesThe AI Data Entry Agent isn’t a gimmick; it’s a demotion notice for manual data entry. Think of it as a bilingual translator living inside your form. It reads messy human text and speaks perfect Dataverse. When users open a record and activate the agent, they don’t have to interact with every field. They simply paste what they have — an email from a colleague, a paragraph of onboarding info, even raw notes copied from Teams — and the agent parses, interprets, and maps each piece to the correct column.Microsoft calls this Smart Paste, but that label undersells the brilliance. The model behind it recognizes entities like names, addresses, and phone numbers, but also learns from context within your specific table schema. If your table includes “Preferred Contact Method,” it understands that “email” in the text likely belongs there. It doesn’t hallucinate; it aligns with your metadata. In effect, the AI agent behaves like a form’s internal analyst — it reads unstructured input, determines intent, and builds structured data faster than any human could.But Smart Paste is only half the trick. The other is File Upload, a feature that feels slightly supernatural. Instead of text, you can drag in an image ...
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    23 分
  • Stop Migrating: Use Lists as Copilot Knowledge

    Stop Migrating: Use Lists as Copilot Knowledge
    2025/10/30
    The Myth of Mandatory MigrationWhy is it that in every digital transformation meeting, someone insists the first step is to migrate everything? As if physical relocation somehow increases intelligence. A file sits peacefully in SharePoint, minding its own business, and then a consultant declares it must be “upgraded” to Dataverse for “future compatibility.” Translation: they’d like another project. You’re told that modernization equals movement, even though nothing’s broken—except, perhaps, your budget.For years, the myth persisted: that Copilot, Power BI, or any shiny AI assistant needed data that lived elsewhere—somewhere fancier, more “enterprise-class.” SharePoint Lists were treated like embarrassing relatives at a corporate reunion: useful once, but not to be seen in public. The assumption? Too old, too simple, too unworthy of conversational AI.And yet, quietly—without fanfare—Microsoft flipped that assumption. Copilot Studio now talks directly to SharePoint Lists. No ETL pipelines, no schema redesign, no recreating permissions you already spent months configuring. The connector authenticates in real time, retrieving live data without duplication. Suddenly, the “legacy” tool outsmarts the migration budget.So today we’re breaking a commandment the IT priesthood refuses to question: thou shalt not move data for no reason. You can keep your lists where they are and still have Copilot read them fluently. Let’s dismantle the migration mirage.Section 1: The Migration MirageEvery enterprise has a reflex. Something important appears? Move it to Dataverse. Something large? Fabric, obviously. Something nonstandard? Export it anyway; we’ll clean it later. It’s muscle memory disguised as strategy. Migration has become a ritual, not a necessity—a productivity tax masquerading as modernization.Consider the sales pipeline that already lives in a SharePoint list. It’s updated daily, integrated with Teams alerts, and feeds a dozen dashboards. But once Copilot entered the picture, someone panicked: “AI can’t use Lists; we’ll have to rebuild it in Dataverse.” Weeks later, the same data exists twice, with half the triggers broken, a few licensing costs multiplied, and no measurable improvement in functionality. Congratulations—you’ve achieved digital motion without progress.Modernization is supposed to make work easier. Instead, we build data ferries. Information leaves SharePoint, visits Power Automate for translation, docks at Fabric for modeling, and then returns to Teams pretending to be insight. It’s the world’s least efficient round trip.Let’s count the costs. First, licensing—because Dataverse isn’t free. Every migrated record incurs an invisible tax that someone in finance eventually notices with horror. Next, schema redesign—those column types in Lists never quite map one-to-one. Something breaks, which triggers meetings, which trigger Power Automate rebuilds. The end result: thousands of dollars spent achieving what you already had—a structured table accessible in Microsoft 365.And the absurdity compounds. Each year brings a new “recommended” platform, shinier than the last, so data hops again: Lists to Dataverse, Dataverse to Fabric, Fabric to some eventual “Unified Lake Platform.” The name changes, the bills persist, the value doesn’t. Users just want their information to answer questions; they never asked for serialized migration.The truth is brutal in its simplicity: Copilot never needed your data copied—it needed permission to see it. Authentication, not replication. All those hours spent writing connectors and dataflows? They existed to make up for an access gap that no longer exists. The new SharePoint List connector removes the gap entirely.For the first time, AI in Microsoft’s ecosystem understands the data where it naturally lives. No detours, no middleware acrobatics. It queries your list directly under the same user context you already trust. If you can open a row, so can Copilot. If you can’t, neither can it. Governance remains intact; logic remains simple.Think about what that means. The endless migration carousel—the expensive dance between platforms—wasn’t driven by technology limits. It was driven by institutional habit. Data migration became a corporate superstition, performed “just in case,” like carrying an umbrella indoors. The enterprise mind equated movement with progress, complexity with sophistication. It never occurred to anyone that simplicity might finally work.And now, without any ceremony, Microsoft just invalidated all that ritual. No new architecture diagram. No whitepaper claiming “revolution.” Just a quiet update: “SharePoint Lists can now be added as knowledge in Copilot Studio.” That’s it. Five seconds of configuration wiped away entire categories of budget justification.Governance teams who lived off “data modernization initiatives” now face an existential crisis. ...
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    21 分
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