How data teams can use Power Platform for metadata management, data catalogs, and more
We had a data pipeline that depended on a list of project codes owned and maintained by the business, and stored on SharePoint in an Excel file. The reasoning behind this was that Excel and SharePoint are the technologies the business is familiar with, so we’d match them to reduce the friction.
But then someone added a blank row. Another person merged cells “just to make it look cleaner.” A third entered a project code with a trailing space. The pipeline broke, small issue, small fix. But then again. We fixed it. Again. And the cycle repeated itself until we asked the question: why are we letting unstructured data entry be the input for a production pipeline?
We realized we needed more control: the business needed a proper interface, and the data team needs to own it. The problem was that no one in the data team was an application developer. So we turned to Power Platform, Microsoft’s low-code app development suite. The pipeline stabilized. And then we started noticing the same pattern in other places, too.
That’s what this post is about.
What Is Power Platform?
Power Platform is Microsoft’s suite of low-code tools for building business applications, automations, and data experiences, without needing a full development team behind every solution.
In 2019, Microsoft unified its tools under the Power Platform umbrella, but the roots go back further. PowerApps was introduced in 2015, aiming to simplify app development for businesses, while Microsoft Flow (now Power Automate) launched around the same time to enable users to automate repetitive tasks across services. Microsoft Dataverse, previously known as Common Data Service, became a core component, providing a unified data storage layer that integrates with Power Apps, Dynamics 365, and the broader Microsoft ecosystem (more on this here).
The platform today consists of four main products: Power Apps, Power Automate, Power BI, and Power Virtual Agents. For data teams operating in a Microsoft environment, the relevant ones are the first three, and for this post especially the combination of Power Apps and Power Automate.
The pitch is simple: anyone can build an app. Whether that’s actually true depends on what you’re building, but the bar is genuinely lower than traditional development or vibecoding something, which is precisely what makes it interesting for data teams that don’t want to maintain a full-stack application just to solve an interface problem.
Power Platform Concepts
Before diving into use cases, it helps to understand the building blocks.
Environments and Solutions
An environment is an isolated container for your apps, data, and automations. Think of it like a workspace or a deployment stage: you’ll typically have development, test, and production environments. Solutions are the packaging mechanism for moving work between them — roughly analogous to a code repository or a deployment artifact. Getting these two concepts right from the start saves you a lot of pain later.
Dataverse
Dataverse is the native data storage layer for Power Platform. It’s a managed relational database with built-in support for roles, permissions, auditing, and relationships between tables. It’s not a data warehouse (please don’t treat it like one) but for operational data owned by business processes, it’s a solid choice. The main caveat: it requires a premium license. More on that shortly.
Power Automate
Power Automate is the automation backbone. Underneath, it is built on the same foundation as Azure Logic Apps, and supports integration with hundreds of Microsoft and third-party services. You use it to trigger actions based on events — a form submission, a new row in Dataverse, an approval being completed. It works well for orchestrating lightweight workflows between apps and services.
One thing to be honest about: many of the more useful automation features require a premium subscription. If you’re planning to connect flows to Dataverse or call HTTP endpoints, budget accordingly.
Canvas Apps vs Model-Driven Apps
This is where a lot of first-timers get confused.
Canvas Apps are fully flexible. You design the layout from scratch, drag and drop controls, write formulas, and wire up data sources manually. Total control, more effort.
Model-Driven Apps derive their layout from your data model. You define tables and relationships in Dataverse, and the app generates views and forms around that structure. Less flexibility, but much faster to get something useful running, and it scales better when your data model is complex.
For metadata management use cases, model-driven apps tend to win. For more custom UX requirements — like a portal or a multi-step intake form — canvas is usually the better fit.
Use Cases for Data Teams
Metadata Management Apps
This is where it started for us, and it’s probably the strongest use case for data teams specifically.
Any time your data platform depends on input from the business — project codes, cost center mappings, category hierarchies, ownership records — you have a metadata management problem. The naive solution is a shared Excel. The Excel breaks pipelines. Then you build a Power App.
The advantages over Excel are concrete: you control what goes into the form (validated dropdowns, required fields, no rogue formatting), you get an audit trail for free via Dataverse, and you can trigger downstream actions via Power Automate when something changes. A new project code gets submitted, a flow fires, your pipeline picks it up that night. The business feels ownership. The pipeline stays green. Everyone wins.
This pattern scales. Approval workflows, activation/deactivation of records, change history — all of it is available without writing a single line of backend code.
Data Catalog
A proper data catalog — one where users can browse datasets, understand what they mean, and request access — is expensive to buy and time-consuming to build. Power Platform offers a pragmatic middle ground.
You can build a lightweight catalog as a model-driven app backed by Dataverse, where each row represents a dataset, a report, or a metric. You define the metadata structure (owner, source, refresh frequency, description), let the team populate it, and surface it through a canvas app with search and filtering. Not as feature-rich as Collibra or Atlan, but it’s yours, it’s integrated with your Microsoft environment, and it took days not months to build.
It won’t serve every organization’s cataloging needs. But for teams early in their data maturity journey, it provides real value with realistic effort.
Power BI Portal
Power BI has a discoverability problem. Reports get published, links get shared over Slack, and six months later nobody knows where the authoritative version lives. The permission model is functional but clunky for end users.
A Power Apps-based portal solves this by building a layer on top of Power BI’s API. Users land in an app that shows them the reports relevant to their role, with descriptions and owners clearly labeled. Access requests go through a Power Automate flow — a form, an approval step, a notification — rather than someone manually adding people to workspace groups. It’s not a replacement for proper Power BI governance, but it dramatically improves the day-to-day experience for consumers of your reports.
When Power Platform Is a Good Fit
Not every problem needs Power Platform. Here’s when it actually makes sense.
You’re already in the Microsoft ecosystem. This is the big one. Power Platform’s native integrations with SharePoint, Teams, Outlook, Dynamics, Azure AD, and Fabric mean that the connective tissue is already there. If your organization runs on M365, you’re not paying for a new stack — you’re extending one you already have. If you’re on GCP or AWS-first, the value proposition weakens considerably.
The business needs to own the solution end-to-end. Data teams are often bottlenecks when business processes need a frontend. Power Platform lets you build the initial version and hand it off — or better yet, empower a power user on the business side to maintain it themselves. This is what “citizen development” looks like in practice when it actually works. The data team defines the data model and the integration points; the business owns the form and the content.
You want guardrails without building a full application. There’s a wide gap between “Excel on SharePoint” and “custom web application maintained by a dev team.” Power Platform lives in that gap productively. When you need input validation, access control, an audit trail, and basic workflow automation, but you don’t want to spin up an API and a frontend — this is your tool.
Your data maturity is still developing. The same nuance that applies to Fabric applies here. Power Platform is not where you run complex data transformations or build production-grade data products. But as a controlled interface between business users and your data platform, it punches well above its weight.
A Few Honest Caveats
No Good, Bad, and Ugly piece would be complete without honesty.
The licensing model is genuinely confusing. Premium connectors — including Dataverse — require a per-user premium license. If your app needs to scale to 50 business users, that cost adds up and should be modeled before you commit to the architecture.
Power Automate flows can get messy fast. They’re easy to start and hard to maintain at scale. Treat them like code: name things clearly, document your flows, and resist the urge to build a 40-step monster flow when three smaller ones would be more maintainable.
And be honest about the ceiling. Power Platform is productive within its lane. When requirements grow — complex business logic, high data volumes, custom integrations outside the Microsoft ecosystem — you’ll feel the edges. Build with that in mind from day one, and design your data model in Dataverse to be exportable if you ever need to migrate.
Closing Thoughts
The Excel on SharePoint that broke our pipeline was, in hindsight, a gift. It forced us to look for a better solution, and what we found was a tool that fits a real gap in the data team’s toolkit: a governed, low-code frontend layer for business-owned data.
Power Platform won’t replace your data platform, your orchestration layer, or your BI tooling. But as a bridge between your data team and the business users who need to interact with it, it’s underrated. Especially in Microsoft-heavy environments, the integration story is strong, the learning curve is reasonable, and the time-to-value is real.
If you’re sitting on a pile of metadata that lives in Excel files and breaks things regularly, it might be time to build something better.
Hi, I’m Bastiaan 👋 Founder of datalyft, a small data agency helping companies transform their raw data into real value. I write about the Modern Data Workflow, where I explore tools and processes to supercharge your data capabilities. Follow me for more.