Power BI, Fabric Apps, or Claude?
Why the Data Model Determines Your Reporting
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Data & Analytics
»Does this replace Power BI?«
We’ve been hearing this question in almost every conversation about reporting for the past few weeks. The reason is understandable: at the end of September, Microsoft introduced Fabric Apps, a way to build custom data applications directly on the Fabric platform. These apps are largely programmed by AI assistants. In early October, Anthropic followed with Claude Dashboards: a single question in the chat is all it takes to generate an interactive dashboard, which can be connected live to your own data upon request.
Both tools are still in their early stages. Fabric Apps are in preview, and Claude Dashboards are in the testing phase. Nevertheless, they are changing the way we view reporting. When dashboards can be created in just a few minutes, the finished dashboard itself loses value, as the technical process of creating it becomes increasingly less time-consuming and complex. What remains crucial is the data model on which it is based.
We’ve used all three tools in projects and prototypes and have discussed their potential applications extensively internally. This article summarizes which tool we see as best suited for which purpose and what you should keep in mind before using them.
Three Tools, Three Tasks
Dashboards are not three variations of the same thing. An analogy from our internal discussion captures it well: Power BI is like Excel. It’s off-the-shelf software with a wealth of built-in features that provides a fixed framework. A Fabric App, on the other hand, is a custom software developed for a specific business process. The design possibilities are virtually unlimited, though every feature must be developed and maintained. A Claude Dashboard is more like a sketch on a whiteboard – quick to create, helpful for discussion, but not meant for the file cabinet.
Power BI reports.
Power BI is designed for monthly reporting, management dashboards, and standard reports for a large number of users, and it remains the first choice for these purposes. Features such as Excel exports, subscriptions, drill-through, and permissions are already built in. The platform automatically optimizes queries and loads only as much data as a visual requires. However, we take a more realistic view of the self-service concept than we did a few years ago. Good reports require knowledge of the data model, key metric logic, and design. That’s why, in most business units, reports are created by BI teams and a few trained power users, rather than by every employee. AI has also long been used in Power BI: today, we already create models and reports with the support of AI agents.
Fabric Apps map processes.
A Fabric App is not just a better report, but a custom application that runs on the same platform and uses the same data. Its strength lies where a report ends: entering plan data, approving corrections, maintaining master data, and managing projects. The app can write entries back to its own database, map workflows, and offers nearly unlimited design possibilities. However, this also comes with the typical overhead of custom software: development, testing, and maintenance. Fabric Apps are therefore less of a tool for the business unit and more for a central team with development expertise.
Explore Claude Dashboards.
Here, a dashboard is generated from a question in the chat. This is impressive for a quick analysis before a meeting or for a first draft. Anyone with an idea can see it come to life in minutes. Above all, everyone follows their own analysis path instead of sticking to predefined drill-down paths dictated by a report. However, the results can vary. Two similar questions can lead to different visualizations and, in the worst case, to differing numbers. Versioning and a structured path to production are currently lacking.
The Foundation: The Data Model
As different as the three tools may be, they all rely on the same foundation: the data model. The data model translates raw data into the language of the business and defines what counts as revenue, which customer is assigned to which region, and who is authorized to view which figures. Power BI queries the data model for each visual. A Fabric app does the same with queries written by an AI assistant, while Claude accesses it via an interface.
This has a simple consequence: If the model is accurate, all three tools deliver the same numbers. If it’s unclear or incomplete, the tools fill in the gaps themselves. With AI-powered tools, this happens faster than you might think. If a metric is missing from the model, the AI simply calculates it on its own. The result looks plausible but still differs from the monthly report. This creates multiple truths once again – exactly the problem that centralized reporting was supposed to solve.
AI introduces a second challenge. A human knows which of three customer tables is relevant for the sales report. An AI only knows this if it’s specified in the model. Descriptions, technical terms, and rules therefore belong in the model – not in individual prompts or in the developers’ heads. For us, these model instructions are the new code. They must be centrally maintained, versioned, and tested just like any other software.
In practice, a few details emerge that aren’t obvious at first glance. Large models with thousands of columns overwhelm AI tools (for now). They need a condensed view of the essentials, and not every question requires the full data set. For many analyses, pre-aggregated data is faster and more robust – for all three tools.
What Decision-Makers Should Look For
The technical question of which tool builds the prettier dashboard is secondary for clients. These points are more important.
Are the numbers accurate?
AI assistants can produce an astonishing amount in a short time. However, they do not relieve you of the responsibility for verification, testing, and technical approval. The faster something is created, the more important this oversight becomes. We also use AI itself for this purpose – for example, to automatically cross-check key metrics in many reporting scenarios. The final judgment on what is correct remains within the business unit.
Who keeps track of everything?
In the past, everyone had their own Excel spreadsheet; tomorrow, everyone will have their own dashboard. Self-service with AI is tempting because it’s so easy. Without guidelines, many versions of the same metric can quickly emerge. Define what ad-hoc dashboards are intended for and when a result should be transferred to central reporting.
Where is the data stored?
Power BI and Fabric can run in European data centers upon request. With Claude, the processing location depends on the contract. Clarify this with your data protection and information security teams before financial or HR data enters the chat.
Who will maintain this in three years?
A report in Power BI benefits from every platform update; a Fabric app does not. If the design of a chart used in twenty apps changes, that means twenty times the work. If you’re planning multiple apps, you’ll need shared templates and rules from the very beginning.
How does it feel?
Loading times aren’t a minor detail. If someone in financial control has to wait eight seconds for a filter to change, they won’t enjoy working with the tool. In Power BI, the platform handles performance. In a Fabric app, the development team has to take care of it themselves.
Typical Use Cases
In our projects today, we assign the tools as follows. The boundaries are fluid, but the direction is clear.
|
SITUATION |
WERKZEUG |
WARUM? |
|---|---|---|
| Monthly reporting, management dashboard, standard reports for many users |
Power BI |
Well-established, easy to maintain, with export and subscriptions included |
| Reports for committees, the supervisory board, or external parties |
Power BI |
Fixed layout, can be distributed as a PDF or via subscription |
|
Self-service-trained power user
|
Power BI |
Custom views on shared models; key metrics remain centralized |
| Planning, Forecasting, Approval of Corrections |
Fabric App |
Data Entry and Processes Directly on the Data |
| Maintenance of Master Data or Mapping Data |
Fabric App |
Replaces Excel lists and manual uploads |
|
Project Management with a Custom Workflow
|
Fabric App |
Customizable interface for a straightforward process |
| A quick analysis before a meeting, a first draft of a new report |
Claude Dashboard |
Visible within minutes; then consciously decide what to keep |
| Investigate the cause of a discrepancy in the monthly report |
Claude Dashboard |
Custom analysis path instead of predefined drill-down paths |
| Special Request from Management Without a Final Report |
Claude Dashboard |
Same-day response instead of a ticket to the BI team |
The process may also involve multiple tools. A controller designs a dashboard in the chat. If the view proves effective, the BI team incorporates the metrics into the model and uses them to build a standard report.
The dashboard is negotiable. The model is not.
Power BI isn’t being replaced – it’s getting some company. Fabric Apps open up reporting for processes that were previously handled using Excel or specialized software. Claude Dashboards make data analysis more accessible than ever. It’s hard to predict today which interface your users will be using three years from now. What’s certain is that they’ll all need a clean, well-documented semantic model. Anyone investing in a reporting solution today should therefore start with the foundation. Key metrics belong in the data model, processes in the app, and the rules for AI usage must be maintained just as carefully as code. We’ll guide you every step of the way: from data preparation through the development of a reliable data model to the reporting built on top of it – whether with Power BI, Fabric Apps, or Claude.
You may want to read more: Strategic IT Insights for Decision-Makers
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