You clicked “Create app from Excel,” felt clever, and accidentally scaled your chaos. It’s not your fault—Power Apps makes it look easy. But Excel isn’t a database; it’s a calculator wearing a database costume. The moment you try to operationalize spreadsheet data in Power Apps, Dataverse exposes every hidden flaw: missing keys, mixed types, ambiguous relationships, duplicate entities, orphaned rows, and silent corruption spreading behind the scenes. This episode tears down the five failure patterns that silently destroy Power Apps built on Excel data—and then rebuilds your data model correctly. You’ll learn how to fix primary keys, enforce types, replace VLOOKUPs with proper relationships, eliminate multi-purpose columns, and prevent orphaned records so your app stops breaking under its own weight. If you’re tired of inconsistent behavior, failing imports, broken lookups, and unpredictable automations, this episode is your blueprint. What You Will Learn The Real Reason Excel Data Fails in Power Apps We start by breaking down why Excel feels “fine” for small tasks but collapses in Dataverse:No enforced identityNo enforced typesNo referential integrityNo audit trailNo concurrency modelUnlimited ambiguityHidden inconsistencies from copy-paste culturePower Apps expects structure. Excel hides the lack of structure until it’s too late.You’ll discover why your spreadsheet worked yesterday but fails catastrophically when imported into an actual data platform. Failure Pattern #1 — No Primary Keys: The Silent Destroyer Most Excel “tables” are just rows. No identity. No contractual uniqueness. No stable way to know whether a row is the same record as last week. This episode explains:Why surrogate GUIDs must be your primary keysWhy natural keys drift and break historyHow alternate keys allow clean upsertsHow Excel’s “uniqueish” text values lie to youHow missing keys cause duplicates, overwrites, and broken automationsHow to generate stable IDs inside Excel before an importWhy Dataverse’s “Primary Name” column is NOT the primary keyYou’ll learn how to build a correct key strategy and fix your source data so Power Apps stops merging the wrong records or duplicating everything. Failure Pattern #2 — Mixed Data Types: The Spreadsheet Horror Show Excel allows one column to contain:✔ numbers✔ text✔ dates✔ leftover Outlook pastes✔ blanks that aren’t real blanks✔ currency symbols mixed into strings Dataverse does not. It enforces meaning. You’ll learn how to model your data correctly with:Whole Number vs Decimal vs CurrencyBoolean vs ambiguous textDateOnly vs DateTimeText fields with normalizationChoice fields for finite statesLookup fields for referencesWe cover how Power Query can clean, normalize, and coerce types before they ever reach Dataverse, and why ignoring types causes broken formulas, inconsistent logic, and unreliable reports. Failure Pattern #3 — VLOOKUP as “Joins”: The Spreadsheet Illusion Excel users simulate relationships by repeating text values and using VLOOKUP.Dataverse does not: it uses actual relationships. We show why:VLOOKUP duplicates text and drifts easilyOne rename breaks hundreds of dependent rowsLookups bind child records to parent IDs instead of labelsA single change to a parent updates everywhere automaticallyYou should model Suppliers, Locations, Categories as tables—not textYou’ll learn how to replace VLOOKUP with Dataverse lookup columns that prevent duplication, preserve history, and eliminate fragile dependencies. Failure Pattern #4 — Multi-Purpose Columns: Where Spreadsheets Go to Die Excel encourages stuffing anything into one column:StatusNotesCommentsTemporary statesMixed enumerationsFlagsConditions“Just this once” exceptionsPower Apps cannot operate on that ambiguity. This episode explains:Why “Status” should be a ChoiceWhy “Location” should be a LookupWhy notes need their own text columnHow to split overloaded fields into governed valuesHow to map free-text to clean, consistent optionsHow this impacts validation, logic, views, and automationsYou’ll learn how to make your model predictable and eliminate the silent drift that makes reports lie. Failure Pattern #5 — Orphaned Rows: The Most Dangerous Spreadsheet Habit In Excel, you can delete a supplier row without realizing thousands of product rows still reference it.In Dataverse, this creates orphans—records with no parent—which break everything. You’ll learn:Why relationships must be requiredHow to enforce parental integrityWhy delete behavior should usually be “Restrict”How to preload parent entities and resolve lookupsHow to detect & repair orphaned recordsWhy “Unknown Supplier” should be a real row, not a blank valueOrphans ruin reporting, destroy accuracy, and break flows. Dataverse can prevent them—if you model it correctly. Excel vs Dataverse — Stress Test Breakdown We compare the two environments across:Row volumeConcurrencyAuditabilityAPI throughputReferential integrityLookup consistencyBehavior under updatesSecurity boundariesData lineage and governanceYou’ll learn exactly why Excel buckles under scale and why Dataverse enforces constraints that feel strict but protect the integrity of your system. Your Minimal Remediation Path (The Practical Fix) You’ll leave with a step-by-step plan to repair your Excel model and bring it into Dataverse cleanly:Model core entities (Products, Suppliers, Categories, Locations)Use surrogate GUID keysDefine alternate keys for importsNormalize data typesSplit overloaded columnsReplace text with lookupsPreprocess data in Power QueryImport parents → then childrenEnforce required relationshipsMove formulas into DataverseEnable auditing & field securityMonitor for duplicates/orphansFollow this, and your Power Apps stop corrupting data and start acting like actual systems instead of spreadsheet reenactments. Who This Episode Is For This episode is ideal for:Power Apps makersCitizen developersPower Platform adminsData analysts transitioning to DataverseTeams migrating from Excel to Power AppsOrganizations scaling low-code appsBecome a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.Follow us on:LInkedInSubstack

Ver todos los episodios