Building Power BI Reports That People Actually Use

Most Power BI reports we audit have the same problem: they were built to answer the question "can we show this data?" instead of "what decision does this help someone make?"

The result is pages of charts that technically work but don't get used. The sales team still checks their spreadsheet. The ops manager still messages the analyst for a custom export every Friday. And the executive? They glance at the dashboard in the Monday meeting and then ignore it until next Monday.

Microsoft's report creation tools in Power BI have come a long way. Between Desktop, the web service, and Copilot, you've got plenty of options for building reports. But more options don't automatically produce better outcomes. What matters is knowing which approach fits your situation, and what design decisions will determine whether your report becomes a daily tool or digital wallpaper.

Two Ways to Build: Desktop vs the Web Service

Power BI gives you two authoring environments. Picking the right one matters more than most people think.

Power BI Desktop is the full authoring experience. It runs locally on Windows and gives you complete control over data connections, the data model, DAX measures, and report layout. If you're building anything that will be used in production by more than a handful of people, this is where you should be working.

The Power BI service (the web browser experience) has improved a lot. You can now create reports directly from uploaded data or from existing semantic models without installing anything. Microsoft has added a "quick reports" feature that auto-generates visualisations from your data, giving you a starting point to customise.

When to use each:

• Desktop: Anything with a proper data model, complex DAX, multiple data sources, or enterprise governance requirements. This is your production authoring tool.
• Web service: Quick ad-hoc analysis, prototyping a report before building it properly in Desktop, or situations where you need something fast and the data model already exists.
• Both: Start a report in the web to prototype, download the .pbix file, and finish it in Desktop. This workflow actually works well for getting stakeholder buy-in before investing in a polished report.

The mistake we see constantly: someone builds a quick report in the web service, it gets shared around, and suddenly it's "the report." No data model. No governance. No version control. Quick reports are prototypes, not production assets.

Visuals: Less Is Almost Always More

Power BI ships with dozens of visual types - bar charts, line charts, scatter plots, maps, matrices, cards, KPIs, decomposition trees - plus a marketplace of custom visuals.

The temptation is to use lots of them. Resist it.

What works in practice:

• One page, one question: Each report page should answer a specific business question. "How are sales tracking this quarter?" is a page. "What's our customer churn rate?" is a different page. Mashing both together dilutes both.
• 5-7 visuals per page, maximum: More than that and the page becomes a wall of data that nobody reads. If you need more detail, add a drill-through page.
• Cards for the headline numbers: Put the 2-3 most important metrics as large card visuals at the top. People will look at these first. Everything else supports the headline.
• Tables and matrices for detail, not for summaries: If someone needs to look up a specific value, a table is perfect. For understanding trends and patterns, use a chart.

Visual interactions are underused: By default, clicking a data point in one visual cross-filters the others on the page. Most report builders leave the defaults and never think about it. But sometimes you want a click to filter, sometimes to cross-highlight, and sometimes to do nothing at all. Take ten minutes to configure these interactions deliberately. It's the difference between a report that feels responsive and one that feels chaotic.

Personalise visuals is worth enabling: This lets viewers temporarily swap out visual types, change fields, or adjust formatting without touching the original report. Turn it on. Your power users get to explore without filing a queue of "can you add this to the report?" requests, and your report stays clean for everyone else.

Report Design That Actually Gets Read

Nobody wants to admit this, but design quality directly affects adoption. We've watched it happen dozens of times: two reports with identical data, one gets used daily, the other collects dust. The difference is always how it looks and feels.

Themes save time and enforce consistency: A Power BI theme is a JSON file that defines your colours, fonts, and default visual formatting. Build one that matches your organisation's brand and apply it everywhere. It takes maybe half a day to set up, and then every report you create looks professional without manual formatting. Microsoft's built-in themes are fine as a starting point, but a custom theme that matches your corporate style pays for itself almost immediately.

Gridlines and snap-to-grid exist for a reason: Misaligned visuals look sloppy. Use the grid. Align your visuals. It takes seconds and makes the report feel intentional rather than thrown together.

Backgrounds and visual grouping: A light grey rectangle behind a set of related visuals tells the viewer "these go together" without needing a label. It sounds trivial, but small visual cues like backgrounds, borders, and shapes make the difference between a report that feels navigable and one that feels like a wall of charts.

Page sizing matters: The default page size works for most desktop scenarios, but think about where your report actually gets viewed. Embedded in a Teams tab? On a phone in the back of a taxi? A report designed for a 27-inch monitor is borderline unusable on anything smaller, so configure page dimensions for the real context.

Copilot: Useful, With Caveats

Copilot in Power BI can generate report pages, suggest visuals, and write DAX from natural language. It works in both Desktop and the web service. Here's what we've found after using it on real projects.

Where Copilot helps:

• Generating a first draft of a report page. Describe what you want and Copilot builds a layout with reasonable visuals. Not perfect, but a decent starting point.
• Writing simple DAX measures. Something like "total sales for the current quarter" translates well.
• Creating narrative summaries. Handy for executive reports where the numbers need context.

Where it falls short:

• Complex DAX. If you need a time-intelligence measure that handles fiscal year calculations with partial periods and custom hierarchies, you're still writing that yourself. And debugging it yourself.
• Design quality. Copilot produces functional layouts, not polished ones. You'll spend time rearranging and reformatting.
• Understanding your business. Copilot has no idea that "revenue" in your organisation means something different from "billing," or that certain product lines should be excluded from regional comparisons. That context lives in people's heads, not in the data model.

Our honest take: Copilot gets you a decent first draft fast. But the part that makes a report genuinely useful for your specific business - the right metrics, the right layout for your audience, the business logic nobody documented - that still requires someone who knows the domain. For more advanced AI-driven analytics, Copilot Studio lets you build tailored AI agents that actually understand your specific business rules, which goes well beyond what out-of-the-box Copilot offers.

Cross-Filtering, Drill-Through, and Bookmarks

These three features turn a static dashboard into something people actually want to click around in.

Cross-filtering: When a user clicks a bar in a chart, other visuals on the page update to reflect that selection. This is on by default, but you should deliberately configure which visuals respond and how. Some visuals should filter, some should highlight, and some should remain unchanged.

Drill-through: Lets users right-click a data point and jump to a detail page filtered to that context. Click a customer name on a summary page, and you land on a detailed customer page with everything pre-filtered. Summary pages stay clean. Detail is there when someone wants it.

Bookmarks: Save specific filter states and visual configurations that users can switch between. Think of them as saved views - "show me last quarter" or "show me just the Eastern region" - so users don't have to wrestle with filters every time they open the report.

Design these three features together and your report stops being a thing people glance at in meetings. It becomes something they open on their own.

What We See Go Wrong

We've built and audited hundreds of Power BI reports across Australian organisations. The same problems come up again and again.

No data model underneath: Reports built directly on raw database tables or CSV imports, with no shared model tying anything together. Every report becomes a one-off. The finance team's "revenue" doesn't match the sales team's "revenue." Performance is poor because every visual fires its own queries. This is the number one problem by a wide margin, and we wrote about it in detail in our post on building an analytics culture with Power BI.

Too many visuals, not enough narrative: A page with 15 charts answers no question clearly. The best reports we've seen are almost boring-looking - fewer visuals, more white space, a clear point on each page. They tell you something instead of showing you everything.

Ignoring the mobile experience: Plenty of executives check reports on their phones now. If you haven't tested your report on mobile, it's probably unusable on a small screen. Power BI's mobile layout feature lets you create a phone-optimised version of each page - it takes maybe 20 minutes per page and it's worth doing for any report that executives rely on.

No governance on who publishes where: Without workspace governance and deployment pipelines, you end up with three versions of the same report in different workspaces. Nobody knows which one is right. Once users lose trust in the numbers, getting them back is painfully slow.

Skipping the semantic model: Power BI's semantic models (previously called datasets) let you define metrics and relationships once, then share those definitions across multiple reports. Skip this step, and you'll end up with five different reports calculating "profit margin" five different ways. We've seen it happen at nearly every organisation that treats each report as a standalone project.

The Overlap With AI

One thing we're seeing more and more: the data model you build for Power BI ends up being the same data that feeds AI applications. Predictive models, automated alerts, agents that answer questions about your business - they all need the same clean, well-structured data underneath.

So if you're building a proper semantic model for your reports, you're also doing half the work needed for AI. Our Microsoft Fabric consultants help organisations build that shared data foundation, and our Azure AI consulting team helps connect it to intelligent applications. The two are less separate than most people assume.

Getting Started

If your current Power BI reports aren't getting the usage you expected, here's where to start.

First, check whether anyone is actually opening them. Power BI has usage metrics - use them. You might find that three of your ten reports get 90% of the views, and the rest are basically abandoned. That tells you where to focus.

For the underperforming reports, the fix is usually simpler than you'd think. Does each page answer a clear question? Is there too much going on visually? Do the interactions make sense when you click around? Cleaning up the design and cutting visual clutter often moves the needle on adoption without touching the data model at all.

Then invest in a proper theme and a governance structure. A consistent theme file across all reports makes everything look professional with zero ongoing effort. And workspace standards with deployment pipelines stop the "which version is the real one?" problem before it starts.

Microsoft's documentation on Power BI report creation covers the technical details of every feature mentioned here. It's a good reference once you know what you're actually looking for - the hard part is knowing what to prioritise, which is what we've tried to cover above.

As Power BI consultants, we help Australian businesses build reports that get used, not just reports that exist. Data modelling, report design, Copilot, Fabric - whatever the starting point, we care about whether the thing actually changes how people make decisions.

Get in touch if you'd like to talk about your Power BI environment.