Stop The Power Apps Lie

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Excel is not a database, stop pretending it is.

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When you wire power apps to a spreadsheet and call it production,

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you’re building a glass elevator and acting shocked when it shatters under traffic.

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Here’s what’s actually at stake.

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Data loss that nobody notices until quarter-end concurrency chaos that rewrites history

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and a governance black hole auditors can drive a truck through.

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The truth, you’re confusing a calculator with the control system.

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I’ll tear down the myth, show the correct path, and hand you a migration plan.

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And there’s one decision that prevents 80% of failures will get to that.

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First, why this matters before your next outage?

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Why this matters?

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Cost of confusion.

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OK, so basically the cost of confusion starts with pretending flexibility equals reliability.

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Excel is flexible because it lets anyone do anything.

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Data versus reliable because it refuses to.

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That refusal saves you money.

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Why this matters?

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Every time a spreadsheet stands in for a database, you import risk.

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Hidden risk, missed decimals, copied headers, rogue formulas, small errors that propagate like kuzu.

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You don’t see the cost until you reconcile reports and discover two versions of truth.

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The average user shrugs, finance does not.

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The confusion tag shows up three ways.

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Data loss, concurrency, and governance.

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Data loss isn’t always dramatic.

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It’s often boring.

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Someone pests values over formulas to speed it up.

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Truncates a column because the view is narrow, or saves a filtered subset and emails it as

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the latest silent corruption.

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The system doesn’t scream because Excel isn’t a system.

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It’s a grid with math.

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Concurrency is where the comedy turns expensive.

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Shared workbook, multiple editors, one drive, sync, jitter, last, save wins.

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Two people fix the same customer row.

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Credit terms updated here address corrected there.

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And the final file, blesses whichever save landed last.

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That’s not a transaction that’s roulette.

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And your power app connected to that file, it serves stale data, then overrides new data,

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then acts surprised.

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Acid transactions aren’t optional in multi-user apps.

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They’re the guard rails that keep reality coherent.

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Governance is the black hole.

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No schema enforcement means columns morph from text to number at the speed of a paste.

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No required fields means blanks stroll right into your pipeline.

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No relationships means you retype foreign keys and hope typos behave.

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No audit trail means you can’t answer the simplest question who changed what, when, and

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why.

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Regulators love that.

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So do internal fraudsters and well-meaning chaos agents.

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Here’s what most people miss.

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The tool you love for analysis is terrible as a backbone.

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Excel is the ancestor.

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Fast, familiar, forgiving.

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Dataverse is the spine, structured, relational, transactional.

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It’s not just a different storage option.

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It’s a different philosophy.

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Excel assumes you’re a careful human.

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Dataverse assumes you’re busy, distracted and occasionally wrong.

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And it builds fences accordingly.

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The costs scale fast.

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Reporting integrity collapses when column types drift.

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Compliance risks spikes.

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When sensitive data lives in a desktop file, then in an email, then in a shared folder with

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everyone access.

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One time isn’t just an outage.

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It’s the hours your team spends reconciling duplicated lists, patching broken formulas, and

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explaining to leadership why the dashboard changed by itself.

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It didn’t.

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You did that.

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Everything clicked when I realized Excel is a calculator with a grid, you model, you explore,

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you prototype.

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Fantastic.

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But the moment multiple users enter, edit and rely on that data operationally, you need

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a system that enforces rules even when you’re tired.

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In other words, dataverse.

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The aha moment, a database doesn’t trust you.

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It validates constraints and logs.

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It forces relationships instead of trusting lookup.

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It gives you role-based access control so finance can update credit limits but can’t

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see HR notes.

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It writes an audit trail because memory is not a control.

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Excel gives you none of that by design.

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It’s why you love it.

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It’s why it fails.

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The stakes, real money.

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Data loss forces rework.

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Concurrency creates contradictory records that erode customer trust.

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Governance gaps turn into fines, breach notifications, and board level meetings you do not want.

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The counter-intuitive part is that moving to dataverse feels slower at first.

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You define tables, relationships, choices, business rules, you set security roles in auditing,

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you think all I wanted was a simple app.

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And yes, that’s the point.

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Simple is a user experience, not a data strategy.

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So before your next outage, accept the premise.

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Excel can track.

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Dataverse must govern.

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Hold that thought.

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Now we’ll step on the three failures you keep stepping on and then replace them with

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something that actually holds under pressure.

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The three fatal failures.

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Data loss, concurrency, governance.

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This is what most people miss.

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Data loss in Excel rarely looks like a dramatic explosion.

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It looks like a polite quiet subtraction.

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The decimal that vanishes because the cell was formatted as text.

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The import that truncates are 255 characters because someone picked the wrong data type

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in a wizard.

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The column name that changes from customer ID to customer ID because someone hates underscores.

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And suddenly every formula points at air.

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Excel doesn’t validate your reality.

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It nods, smiles, and proceeds.

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The simple version is this.

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There’s no enforced schema, no constraints, no referential integrity.

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Think of a database as a bouncer checking IDs.

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Excel is the side door propped open with a chair.

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You can paste nine digits into a field that’s supposed to hold 10.

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You can delete a parent row and strand children that still reference it.

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You can import a CSV with more rows than your clever range noticed and your app stops

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seeing 12% of the world.

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No alarms, no errors.

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Just the slow, invisible drift of truth.

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Here’s the weird part.

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Even when you’re careful, Excel’s convenience is your enemy.

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Copypaste values only to speed things up.

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You just froze a living calculation, rename a sheet tab, your power query steps break three

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layers downstream.

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Use Find and Replace to fix a code pattern.

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Congratulations.

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You also change the middle of every part number containing that sequence.

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That’s not Mellis.

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That’s the absence of a contract between your intent and the data.

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Let’s make it painfully concrete.

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Two users fix the same row.

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One updates the credit limit from 10,000 to 15,000 after a phone call with sales.

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The other corrects the shipping address to include the missing suite number.

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Save, last save wins blesses whichever edit landed last.

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The other edit obliterated.

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In your app, the record now has the new address and the old credit limit or vice versa depending

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on the sync lottery.

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There’s no merge, no row level locking, no transaction that bundles both edits as a single

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truth.

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But we turned on co-authoring in one drive.

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Cute.

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That’s text level reconciliation, not acid guarantees, then yes, acid matters.

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Atomicity means both edits commit or neither does.

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Consistency means the data obeys rules before and after.

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Consistency means simultaneous edits don’t bleed into each other.

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Durability means once it’s saved, it’s day saved, Excel offers none of that, it offers

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good luck.

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Ba Saa Cene, he Sa Saa Somasasal.

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The power app connected to that file inherits the chaos, stale reads, conflicting rights,

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and override races that turn history into a choose your own adventure.

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Now governance, the black hole where accountability goes to die.

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No schema means columns can morph by accident.

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No relationships means there’s nothing forcing a valid customer on an order.

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No required fields means phone can be empty until you need to call.

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No audit means you can’t answer who changed this without walking around asking humans

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to remember.

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No role-based access means the intern can see salary notes because the file has one permission.

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Whoever got the link.

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And do not waive version history at me like it’s an audit log.

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A blob level version is not a field level change record.

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Compliance exposure is not theoretical.

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Sensitive data in a spreadsheet travels by email, gets saved to desktop, reappears

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in a personal one drive.

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DLP doesn’t see it until it’s already everywhere.

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Try convincing an auditor that your customer list with birth dates in a tab called old

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was adequately protected because the folder had a read only sign.

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Excel doesn’t implement policy, it politely ignores it.

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Nobody explains this right but the governance failure creates a culture of personal copies,

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just in case.

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So now you have the mothership file, three personal forks, a CSV used for a monthly import

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and a sharepoint copy for reporting, which one is real, all of them until something breaks,

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then none of them.

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This is how reporting integrity erodes, even if each person was careful.

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The truth treating Excel like a database is not frugality, it’s deferred pain.

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Data loss is a feature of the medium, concurrency is a coin flip.

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Governance is theater, enter, that averse, not because Microsoft like selling you new nouns,

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but because you need a spine that enforces contracts, mediates edits and remembers everything.

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We’ll build that next.

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Enter data verse what it is, why it wins.

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Enter data verse, not a nicer spreadsheet.

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A data platform that behaves like an adult is the spine your apps attached to so the body

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stands up even when users lean on it.

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This governance first because that’s where Excel politely bowed out.

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In data verse you define tables, columns, data types, relationships and validation once

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and the platform enforces them every time.

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You want an account table and a contact table with one to many relationship?

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You don’t beg users to keep IDs consistent, you create a look up.

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You want a choice field so status can only be new, active or closed?

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You define it and lock it.

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You want business rules like credit limit must be greater than zero if status is active.

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You don’t write a memo, you write a rule.

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The difference is stark.

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Data verse is a contract, not a suggestion.

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Security isn’t who has the file link.

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It’s a role-based access control that maps to actual responsibilities, build security roles,

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sales finance support with table level permissions, row ownership and field level visibility.

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Make salary notes readable by HR only while everyone else sees a neat blank column.

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Layer teams over roles so access follows organizational structure instead of improvisation.

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Add column level security for truly sensitive attributes and yes enable audit logs that

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capture who changed what when from what to what.

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That’s not a retroactive version history.

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That’s accountability baked in data quality becomes proactive.

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Use required fields, reject style constraints and calculated columns to keep data consistent

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at entry, not in a cleanup day, six months later.

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The duplication rule stop you from creating contoso LLC, contoso LLC and contoso as three

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different customers because someone was in a hurry.

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Power automate brings discipline, run validation flows, send approvals, kick off notifications,

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and apply the same rule a thousand times without relying on, remember to do it.

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Scalability excel gasps at volume and concurrency, data verse expects it.

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It’s built for multi-user high velocity rights with server side logic, indexes and query optimization.

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Your canvas apps can still paint the custom UI you crave, but the engine underneath is model-driven.

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Relationships enforced, forms generated, processes orchestrated.

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And yes, model-driven apps are responsive out of the box so you don’t spend a week

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in nudging pixel-perfect layouts that collapse on mobile.

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Facilability changes the culture, flip on auditing at the table or column level and you get a time-stamped

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ledger of changes.

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Need to know who changed credit limit from 15,000 to 5,000 at 312 pm?

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It’s there.

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Need change history for compliance?

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Also there.

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Enable change tracking to power incremental loads and integrations without repulling the

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universe and when inevitably someone asks for rollback, point to backups and point in

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time restore.

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Memory isn’t a control, data verse logs are.

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Minimizing downtime during migration is not magic.

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It’s planning supported by platform features.

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You can run faced migrations, stage the schema, load historical data, dual run for a period,

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then cut over with a timed freeze.

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If the cutover goes sideways, have rollback criteria defined and tested.

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You’re not hoping the spreadsheet is close enough.

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You’re executing a plan with guardrails, environments for dev, test and prod, solutions

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for transport and a clear promotion path that doesn’t rely on right-click copy.

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Here’s the truth, the marketing slides almost say out loud.

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Virtual driven strength plus canvas flexibility is the sweet spot.

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Use model driven for the core data interactions that demand consistency.

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Standard forms, related records, business process flows.

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Layer canvas pages for the oddball UI where speed and specificity matter.

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A barcode scanner, a dashboard, a kiosk style intake screen.

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One data brain, data verse.

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Feeding both experiences so you never play, which version is real.

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Again.

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Security and compliance certifications exist for a reason.

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You get encryption, address and in transit, data loss prevention policies across tenants

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and environments, and integration with Microsoft Entra for identity.

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That’s not just comfort, it’s audit evidence.

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Map that to your governance policy and stop pretending a shared folder with do not touch counts

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as control.

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Integration is adult too.

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Use virtual tables to surface external data without duplicating it or use data flows and

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power query to shape and import on a schedule.

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Trigger power automate flows on create, update, delete, so downstream systems stay in sync

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without a ritual of CSV exports and when analytics scale questions arrive data verse doesn’t demand

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monastic purity.

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It extends export to fabric lake house for big analytics.

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Keep operational rights in data verse and let power be I speak to both without duct tape.

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Everything here exists to fix the failures we outlined.

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Data verse enforces contracts, mediates edits with transactions and remembers changes with

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an audit trail.

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It’s not faster to set up than dragging a file into a connector.

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It’s faster to live with.

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It’s the difference between a hobby project and an operational system and because I can

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feel you about to ask yes, when your current world lives in excel, there’s a path out

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that doesn’t torture week will take it next.

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The correct migration strategy from excel to data verse.

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Here’s the path out structured predictable and blessed boring will keep your users working

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while we move the data from clever spreadsheet to actual system preparation first inventory

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the chaos list every excel file feeding your app location owner consumers refresh cadence

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and how it’s used reference entry or reporting classify sensitivity public internal confidential

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restricted note row counts and tabs that are actually separate entities masquerading as

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sheets identify calculated columns you’ll replace with data verse calculated fields or business

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rules.

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Then pick a scope you can win one business process not your entire company modeling next translate

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sheets into tables columns and relationships one entity per real world now account contact

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order product.

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Use proper data types numbers as numbers dates estates choices for controlled vocabularies

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lookups for relationships avoid over normalization two or three levels deep is fine building

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an academic ERD museum is not ad required fields where reality demands them and define alternate

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keys for natural identifiers you’ll use during imports now choose your import path if you

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need transformations splitting columns mapping codes fixing types use data flows with power

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query it’s repeatable scheduleable and keeps your logic in one place if the spreadsheet

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is already clean and flat direct import works for a first load if users insist on working

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in excel during a transition the data verse add in let’s them push changes under rules

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instead of emailing attachments the simple version pick the path that moves logic out of

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human hands and into a pipeline before you touch production validate profile the data

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blanks were required invalid formats duplicates by key often that reference nonexistent parents

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build the duplic logic standardized casing trim white space normalize punctuation then

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define match rules that won’t eat cousins for twins load into a dev environment first

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reconcile counts and check sums spot check critical records then break it on purpose try to insert

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a bad value violate a relationship or bypass a required field the system should say no

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loudly security setup is not optional homework it’s the core deliverable define roles for

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who can read create write a pen and delete by table layer teams so access follows org structure

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apply column level security to sensitive attributes salary ss and payment tokens use owner and business

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units go to corral role level visibility and yes default to least privilege the give everyone

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everything shortcut is how you re import risk turn on the memory enable auditing at the

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table and column level for the fields that matter status amounts identities dates enable

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change tracking to support downstream syncs without full reloads configure automatic backups

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and validate point in time restore if you can’t prove you can go back you didn’t finish the job

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plan your cutover like an operation with instruments counted face migration beats heroics

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step one deploy the schema to dev test and prod using solutions step two pre-load historical

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data into prod while your excel apps still runs step three schedule a freeze window communicated

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during which excel is view only step four delta load recent changes step five switch your

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apps data source to data verse and dual run for a defined period with read only access to all data

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step six declare success or rollback based on objective criteria data reconciliation thresholds

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performance in error rates user sign offs if you don’t predefined rollback triggers you’ll

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invent them mid panic communication cadence matters tell people what will change when and

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how they’ll work during the freeze provider one page what moved wear guide and a hotline

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for the first week train the few who need to adjust habits like entering a required field

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that used to be optional people can adapt surprises cannot to quick tactics to avoid pain first

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keep identifier stable introduce a durable surrogate key in data verse and map excels natural

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key to an alternate key for upsets that prevents duplicates during staged loads second retire

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the spreadsheet deliberately archive it to a secure location lock it and publish a clear

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statement data verse is the source of truth excel extracts are read only an ephemeral the

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pay off your app now writes to a platform that enforces rules mediates edits and remembers

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changes you didn’t move a file you replace the habit with a system and if you’re about to

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ask how this fits the broader data landscape operational verses analytical will expand the

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architecture next when data verse isn’t enough fabric lake house and sickle the truth data

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verse is your operational backbone not your planetary brain when the questions shift from

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update this record safely to model two years of click stream against revenue by region

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you’ve crossed into analytics land different physics stop trying to bench press a warehouse

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with an app database start with a simple decision tree if the workload is operational

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crud workflows approvals near real time views for teams data verse is home if the workload

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is analytical big joins historical snapshots large aggregates across millions or billions

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of rows enter Microsoft fabric and the lake house if the workload is specialized or LTP

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with extreme transaction rates or legacy app dependencies SQL server or Azure SQL belongs

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with data verse optionally as the app facing layer data verse plus fabric lake house is

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the clean pattern keep master data and operational truth in data verse use one lake to land curated

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governed exports for analytics at scale power be I sits natively on the lake house chewing

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through history without throttling your app compare that to overworking data verse with

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giant reports you’ll throttle API limits annoy users and still deliver sluggish dashboards

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operational data serves today analytical data serves trends integration patterns matter

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virtual tables let data verse read from external stores without duplicating data use them to

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surface reference data or legacy system records inside your app while keeping the source authoritative

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for analytics avoid round tripping push data out on a schedule via data flows synapse link

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for data verse or fabric pipelines into one lake then model semantic layers in power be I

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that’s push for analytics virtual eyes for operational context pulling massive facts back into

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data verse is how you recreate excel just Claudia SQL has a seat but not the throne use Azure

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school when you need stored procedures doing tight transactional work deterministic performance

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for line of business systems or when an existing apps schema is immovable you can front

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it with model driven apps via virtual tables or connect canvas apps directly the key is architectural

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intent data verse for unified security auditing and low code app ergonomics SQL for specialized

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transactional logic fabric for scale out analytics three tools one playbook hybrid architecture is

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adult supervision master data in data verse with relationships validation and are back facts

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streamed or batch loaded into fabric lake house for history enrichment and machine learning

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certain subsystems pricing engines MES trading run on SQL for deterministic transactional throughput

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glue it with power automate and fabric pipelines then document ownership and lineage so you

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don’t grow shadow excel empire on the side who owns customer data verse who owns 12 months

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of sales detail fabric who owns pricing algorithm state SQL write it down and force it and stop

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arguing in meetings guardrails or excel doesn’t reemerge wearing a power be I badge first

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no ad hoc cs v’s as data sources in production they can be staging at best short lived auditable

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and deleted second centralized semantic models in power be I with certified data sets outlaw

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personal data models feeding executive dashboards third lineage or it didn’t happen every data set

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maps back to data verse tables pipelines or SQL sources with documented transforms finally governance

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policies dlp for environments one lake access via security groups and c i cd for pipelines and models

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if you’re still emailing extracts just for a quick check you’ve already backslid everything here

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keeps the promise data verse governs operations fabric scales inside SQL handles specialized transactions

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use the spine were a spine belongs and the warehouse where gravity is different your 10 step migration

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plan you can draft today you’ve got the y and the what now we make it unavoidable with a plan you

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can start drafting before your next meeting ends 10 steps no heroics just adult supervision step one

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map current excel assets owners consumers prioritized by risk inventory is not glamorous it’s

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necessary list every spreadsheet that feeds or pretends to feed production where it lives who owns it

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who uses it and what decisions depend on it no row counts tabs that are really entities refresh

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cadence and whether it’s reference data entry or reporting tax sensitivity public internal

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confidential restricted then ranked by blast radius which file if wrong or unavailable hurts you

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the most that’s your starting q step two define entities keys relationships decide choices versus

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lookups translate the business nouns into tables accounts contacts orders products spare me the

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we are unique myth for each table define a primary key use guides as surrogate keys in data verse

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and identify natural keys as alternate keys for upsets like account number map relationships

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explicitly one too many from account to contact many too many were reality demands it use choices

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for control vocabularies status region category use lookups for relationships no a text field that

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usually matches is not a relationship step three security model sketch roads teams column security

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for sensitive fields design access like an adult create roles aligned to functions sales finance

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ops hr with create read write append append to and delete rights scope per table use teams to mirror

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org structure so people inherit access by membership not by hope apply column level security to fields

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like salary ssn credit card tokens or confidential notes decide ownership model user owned

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versus organization owned based on whether records should be private by default least privilege is

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not optional it’s the baseline step four data quality rules required fields patterns the

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logic referential checks move the rules out of human memory and into the platform mark must have

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fields as required at the table level at format constraints for IDs emails phone numbers if it

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should match a pattern say so define the duplication rules for accounts and contacts that consider

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normalized names emails and phone numbers trim spaces standardized casing remove punctuation

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before comparing in force referential integrity with relationship rules so you cannot create an

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order without a valid account the system should reject nonsense at entry not after a quarterly cleanse

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step five build dataverse schema in dev seed with sample data create your tables columns

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relationships choices business rules and calculated fields in a development environment don’t do

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this live seed with a small representative sample dozens or hundreds not thousands so you can

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see how forms render how look ups behave and where your rules bite this is where you find out that

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notes needs 1000 characters not 200 and that status should be a choice not free text adjust now

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not doing cut over step six choose import path configure data flows power query transformations

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pick the pipe that matches the mess if your excel needs transformation split columns fix types map

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codes use data flows with power query make the steps explicit and repeatable scheduled if needed if

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the sheet is already clean and flat use direct import for the initial load if users must keep

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entering data in excel during transition use the dataverse add in so their changes flow through

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the rules not around them the adult rule logic belongs in a pipeline not in someone’s quick tips

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posted step seven validate loads reconciliation reports spot check audit and permissions load into

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dev reconcile counts per table and per key run exception reports for duplicates or offense and invalid

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formats then fix the source or the transform promote to a test environment and validate again

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produce a reconciliation report that a non-technical stakeholder can read we expected 18,942 accounts

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we loaded 18,941 one duplicates suppressed by rule spot check audit logs to confirm changes

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attract test security by logging in as each role see only what you should and fail where you must

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step eight pilot with a model driven skeleton canvas where you ex demands it stand up a model driven

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app against your dataverse schema you’ll get forms views and related records out of the box that

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respect your relationships and rules use that for core operations add canvas pages only where you

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ex specificity matters scanning dashboards kiosk intake resist the urge to redesign everything pixel by

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pixel on day one function first fancy later the pilot proves that the data model is right because the

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app behaves even under people who don’t read instructions step nine cut over plan freeze window dual run

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rollback communication script write the playbook before you play announce a freeze window where excel

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becomes read only pre-load historical data to product ahead of time during the freeze delta load

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new changes switch the apps data source to dataverse dual run for defined period with read only

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access to the old file for comparison define objective rollback criteria data variance thresholds

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error rates performance baselines user sign offs if you hit a red line rollback no heroics no

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sunk cost pride also script the combs what’s changing when what users do differently and where to

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get help surprises are failures of planning not fate step 10 post migration monitoring backups audit

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review excel access deprecation you’re not done at cut over you’re done when operations are boring

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monitor key metrics record creation rates error logs api consumption and performance confirm automatic

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backups are running test point in time restore in a sandbox so you can sleep review audit logs weekly

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at first spot outliers confirm sensitive fields aren’t being edited by the wrong roles deprecate

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excel access intentionally archive the old files to a secure locked location publish an access

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policy that states the source of truth is that averse exports are temporary and read only remove

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old connections from apps and flows to nobody silently backslides close loop the one decision that

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prevents 80% of failures centralized the data model in dataverse first not in a canvas app not in

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power query not in a well-meaning spreadsheet the model tables relationships rules and security

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lives in dataverse everything else plugs into that reality make that decision and most of the chaos

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you’ve normalized simply can’t occur you know what to do next draft the inventory draw the model

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pick the pipeline and schedule the freeze you’re replacing a habit with a system that’s how grown-ups

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do data conclusion the spine not the spreadsheet the takeaway is simple excel tracks dataverse governs

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and governance is what keeps truth intact when humans get busy if this saved you from another quick fix

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disaster lock in your upgrade path subscribe turn on alerts and watch the dataverse modeling walk

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through next draft your migration plan today inventory model pipeline cutover and execute before

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the next outage makes the decision for you.

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Entropy wins by default. Choose Structure.