It started with a fish. Or more accurately, a fish pic guy. You know the one: holding up a trout like it’s a personality. And when I saw Sara Lagerquist compare him to local choices in Dataverse, I couldn’t unsee it. (She really did.)

Suddenly, the entire dating app experience started looking like a metaphor for Power Platform architecture. The red flags, the weird flexes, the mysterious disappearances, it’s all there. So here it is: my lovingly curated list of toxic Tinder traits and their Power Platform equivalents. And most importantly: what we really really want instead 💅.

🚩 “Just here for fun”

Tinder version: No intention of building anything serious.

Dataverse version: Apps are built in personal environments and shared informally, often with no use of solutions at all.

Why it’s bad: Personal environments are tied to an individual user. If that user’s license changes or they leave the organization, apps and flows can become inaccessible or break. There is no backup mechanism, no structured deployment, and no visibility for admins.

What we really really want: Use Managed solutions in real environments, supported by application lifecycle management. Adopt a Dev → Test → Prod structure using solution export/import, pipelines, and service accounts for ownership continuity.

🚩 “5'9 because apparently that matters”

Tinder version: Insecure flexing
Dataverse version: Spends hours fine-tuning view layouts: adjusting column widths, alignment, and font size, while ignoring form usability or data model design.

Why it’s bad: Aesthetic tweaks at view level don’t improve the user experience if forms are cluttered, poorly grouped, or not optimized for mobile. This leads to low adoption, frustrated users, and avoidable support tickets. Prioritizing cosmetics over structure signals a lack of UX maturity.

What we really really want: Design for usability. Use tabbed forms, card sections, responsive layouts, and define view columns based on meaningful business fields — not visual symmetry. Test across devices and screen sizes.

🚩 “Sapiosexual”

Tinder version: Claims to love intelligence, but mostly just wants to sound smart
Dataverse version: Mentions GPT, AI Builder, vector embeddings, or Copilot Studio without having clean data, a clear prompt strategy, or a licensing model that supports usage.

Why it’s bad: Deploying generative AI on top of unstructured or incomplete data (e.g., notes columns, plaintext metadata, inconsistent tags) results in unreliable output. Using OpenAI connectors without cost control leads to unpredictable billing. Overengineering AI features can also distract from core user needs.

What we really really want: Use AI intentionally. Invest in data preparation (structured columns, correct datatypes, standardized tags), and choose Copilot Studio or Azure AI integrations only when they serve a real business goal. Document model dependencies and cost implications.

🚩 “Van life guy”

Tinder version: Free spirit with zero stability

Dataverse version: Uses trial environments or developer environments for apps with live business users or production data.

Why it’s bad: Trial environments are temporary! Flows using premium connectors may break when the environment expires. Admins cannot enforce policies or guarantee data recovery. Trial environments also do not support secure ALM or production SLAs.

What we really really want: Use sandbox and production environments with environment-level governance. Apply DLP policies, define security roles, and ensure retention policies are in place. Trials are for demos and experiments, not business-critical workflows.

🚩 “Crypto bro”

Tinder version: Obsessed with hype, overconfident, and always promising exponential returns.

Dataverse version: Frequently rebuilds the solution architecture based on trends, switching from Canvas to model-driven, then to Power Pages, without completing any of them or defining a long-term data model.

Why it’s bad: Changing technologies mid-project creates confusion, duplicate work, and inconsistent user experience. It also disrupts security planning, licensing, and support handover. Teams lose trust in the platform and developers when nothing is stable for more than a sprint.

What we really really want: Define a stable architecture. Choose between Canvas, Model-driven, or Power Pages based on use case, user roles, and licensing; and commit to the model. Use technical design documents and decision logs to guide change intentionally, not impulsively.

🚩 “Let’s go on an adventure”

Tinder version: Spontaneous. Dangerously so.

Dataverse version: Builds large Power Automate flows with 30+ actions, nested conditionals, and multiple connectors — with no naming conventions, no error handling, and no retry logic.

Why it’s bad: When a flow fails, it’s difficult to troubleshoot without clear naming or scope grouping. Missing error handling leads to data loss or incomplete processes. Lack of logging makes audit trails impossible. Complex flows are harder to maintain and scale.

What we really really want: Use scopes to group logic, apply Configure Run After for error handling, and compose actions to isolate reusable logic. Document flows with comments and consistent naming. Build for readability and monitoring. Practice makes progress.

🚩 “Guy who ghosted”

Tinder version: Promises the world; then vanishes

Dataverse version: Delivers a working app in an unmanaged solution and disappears; leaving behind undocumented business rules, JavaScript, and custom plugins with no source control.

Why it’s bad: Unmanaged solutions in production environments are mutable. Changes won’t be tracked, and deploying updates can overwrite important customizations. With no documentation or ALM pipeline, maintenance becomes guesswork.

What we really really want: Use managed solutions in production and commit solution versions to source control. Document everything. Use pipelines or at least manual export/import with release notes.

🚩 “My ex was crazy”

Tinder version: Never their fault. Always someone else’s. Dataverse version: Blames Microsoft for everything: Slow forms, broken flows, weird errors; when it’s really their own unsupported JavaScript, sloppy plugin code, or broken logic.

Why it’s bad: You’ve seen it: a form throws an error, and the first reaction is “Dataverse is buggy”. But dig a little deeper, and there’s a JavaScript function that silently fails. Or a flow that references a deleted column. None of it logged. None of it tested. Instead of fixing the issue, they escalate to IT, file support tickets, and rant in Teams, Reddit, and LinkedIn. Meanwhile, the real problem is still buried in unmanaged code.

What we really really want: Take ownership. Write code that fails loudly and clearly. Use try/catch, trace logs, and show meaningful error messages to makers. If you add complexity, document it. Don’t blame Microsoft for problems you created; especially if you’re the only one who can fix them.

Final thoughts: Tell me what you want

We’ve all made red-flag decisions in Power Platform. Maybe you built that flow at midnight. Maybe you skipped governance “just this once”. Maybe you were the fish pic guy. But we can do better.

So next time you design an app, a flow, or a solution:

Be someone your future team would swipe right on.

Be someone who leaves behind clarity, not cleanup.