Tips for Designing Attractive Power BI Dashboards

Introduction

At Versich, we build Power BI dashboards for finance, operations, and sales teams every week, and we have noticed the same pattern across almost every engagement. The dashboards that get used daily are not the ones with the most charts or the flashiest colours. They are the ones that are easy to read, quick to understand, and built around what the audience actually needs to decide.

A dashboard is not a report. It is a decision-making tool. When it is cluttered, inconsistent, or hard to scan, people stop opening it within a few weeks, no matter how much effort went into the data model behind it. We have seen beautifully engineered dashboards get abandoned simply because the layout did not respect how people actually read a screen, and we have also seen relatively simple dashboards become indispensable because every visual was placed with intent.

Good dashboard design sits at the intersection of data work and visual communication. It requires technical skill to build the underlying model and DAX measures correctly, but it also requires a separate set of judgement calls about layout, colour, hierarchy, and interactivity. Many teams get the first part right and skip the second, which is exactly where usable dashboards turn into ignored ones.

In this post, we are sharing the design principles we apply across our Power BI consulting and development work. These are not abstract design theories. They are practical habits that consistently produce dashboards our clients keep coming back to, whether they are reviewing daily operations, monthly finance close, or executive-level KPIs. We have organised them in roughly the order we apply them during a build, starting with planning and ending with user testing, so you can use this as a checklist for your own next dashboard project.

Start With the Question, Not the Chart

Before we open Power BI Desktop, we ask a simple question: what decision will this dashboard help someone make? A sales director checking pipeline health needs different visuals than a finance controller reviewing cash flow. Designing for the question first keeps us from building dashboards that look impressive but answer nothing specific.

We find it useful to write down the top three questions a dashboard needs to answer before placing a single visual. If a chart does not help answer one of those questions, it usually does not belong on the page. This single habit removes more clutter than any formatting trick, and it also speeds up the build process, since we are no longer trying to retrofit purpose onto visuals after the fact.

This step also shapes how we structure the report into pages. A dashboard built around one core question per page tends to feel calmer and more focused than one that tries to cram an entire department's reporting needs into a single canvas. We often split what a client originally asked for as one dashboard into two or three pages, each answering a distinct question, and the feedback is almost always positive once people see how much easier it is to navigate.

Establish a Clear Visual Hierarchy

Attractive dashboards guide the eye in a deliberate order. We typically place the most important KPI, such as total revenue or open pipeline value, in the top-left corner, since that is where most people look first on a left-to-right reading pattern. Supporting detail and trends follow below or to the right, in roughly the same order someone would naturally ask follow-up questions.

We use size, position, and colour weight to signal importance rather than relying on borders or boxes around every element. A large card with a bold number naturally reads as more important than a small line chart tucked in a corner, and that contrast does most of the organisational work for us. We also keep a consistent grid across the page, aligning card edges and chart boundaries, since even small misalignments make a dashboard feel unfinished even when the numbers are completely accurate.

One technique we rely on often is the inverted pyramid: the single most critical number sits at the top, summary trends sit in the middle, and granular, drillable detail sits at the bottom. This mirrors how most executives actually consume a dashboard. They glance at the top number, check the trend if something looks off, and only go to the detail when they need to investigate further.

We also think carefully about grouping. Visuals that relate to the same theme, such as three charts all covering regional sales, should sit closer together than visuals covering an unrelated topic like headcount. Proximity itself communicates relationship, so we use it deliberately rather than letting it happen by accident based on whichever order visuals were added to the canvas.

Choose a Disciplined Colour Palette

One of the fastest ways to make a dashboard look unprofessional is to use too many colours. We typically limit a dashboard to one primary brand colour, one or two accent colours for highlighting specific values, and a neutral grey scale for everything else. Colour should carry meaning, not decoration, and every colour we introduce has to earn its place by communicating something specific.

We also pay attention to accessibility when choosing a palette. Roughly one in twelve men experience some form of colour vision deficiency, and red-green combinations are particularly easy to misread for that audience. Where possible, we pair colour with a secondary cue, such as an icon, label, or pattern, so the dashboard still communicates clearly even if a colour difference is hard to distinguish.

Here is the simple framework we apply on most client dashboards:

Most organisations already have brand colours defined for marketing materials. We typically reuse those rather than introducing a separate palette specifically for analytics, since it keeps dashboards feeling like a natural extension of the company rather than a disconnected tool.

Respect White Space

We often get asked to fit more visuals onto a single page than the page can comfortably hold. Our usual advice is to resist that instinct. White space is not wasted space. It gives each visual room to breathe and helps the eye separate one piece of information from the next.

As a general rule, we avoid letting visuals touch the edges of a page or sit directly against each other without margin. A consistent gap between cards and charts, even as small as eight to twelve pixels, makes a noticeable difference in how polished a dashboard feels. We also leave breathing room around text labels and titles rather than letting them crowd the data underneath them.

When a client insists on fitting everything onto one page despite the limited space, we usually recommend tooltips, drill-through pages, or a details page instead. This keeps the main view clean while still giving people a path to the deeper detail when they actually need it, rather than forcing every number to be visible all the time.

Use the Right Visual for the Data

A common mistake we see in dashboards built without design guidance is the overuse of pie charts and 3D visuals. Pie charts are difficult to read accurately once there are more than three or four categories, and 3D effects distort how values compare to each other, making precise comparison almost impossible even for an attentive viewer.

• Use bar or column charts for comparing categories
• Use line charts for trends over time
• Use KPI cards for single, high-priority numbers
• Use tables or matrices when users need to scan exact values
• Use maps only when geography is part of the actual question being asked

Matching the visual type to the data type is one of the simplest ways to immediately improve how a dashboard reads, and it is a step we never skip during our build process. We also try to limit the total number of distinct visual types used on a single page. A page with eight different chart types competing for attention is harder to scan than one that repeats two or three familiar formats consistently.

Conditional formatting is another tool we rely on heavily within tables and matrices. Data bars, colour scales, and icon sets let a table communicate at a glance without forcing the viewer to read every individual number, which keeps detailed views useful rather than overwhelming.

Keep Typography Consistent

Font choice and sizing matter more than most people expect. We standardise on one font family across an entire dashboard, usually the organisation's existing brand font or a clean sans-serif if none is specified. Titles, axis labels, and KPI values each get a consistent size and weight applied across every page of the report.

Inconsistent font sizing, where one chart title is larger than another for no reason, is one of the fastest ways a dashboard starts to look unfinished, even if the underlying data and logic are solid. We typically set up a small style guide for each project covering title size, label size, and KPI number size, then apply it across every visual using Power BI's report theme file, so changes propagate automatically rather than requiring manual updates to every chart.

Capitalisation matters too. We pick one convention, usually sentence case for titles, and apply it consistently rather than mixing title case, all caps, and sentence case across different visuals on the same page.

Design for the Device It Will Actually Be Viewed On

We always ask clients how a dashboard will be consumed before finalising layout. A dashboard reviewed on a large monitor during a leadership meeting can support more density than one checked on a tablet or phone during a warehouse walk-through. Designing for the lowest common denominator screen size usually produces a cleaner result on every device, even the larger ones.

Power BI's mobile layout view lets us design a separate, simplified version of a report specifically for phone screens, and we use it whenever field teams or mobile-first users are part of the audience. On mobile layouts we typically keep to a single column, prioritise the two or three most critical KPIs, and drop anything that requires fine motor precision to interact with, such as small slicers or densely packed legends.

Skipping this step is one of the most common reasons dashboards go unused outside the office. We have rebuilt several client dashboards purely to add a mobile layout, and adoption among field staff increased noticeably as soon as the dashboard became genuinely usable on a phone rather than just technically viewable.

Add Interactivity With Purpose

Slicers, drill-throughs, and bookmarks make dashboards feel dynamic, but only when they serve a clear purpose. We avoid adding a slicer for every possible field simply because the data model supports it. Each interactive element should answer a real question someone has asked us during discovery, rather than existing because it was technically easy to add.

We also pay close attention to default states. A dashboard that opens to a blank or empty-looking page because no filter is pre-selected creates a poor first impression, even if the underlying functionality is strong. We set sensible defaults, such as the current month or the user's own region where row-level security applies, so the dashboard tells a useful story the moment it opens.

Bookmarks deserve a particular mention here. We use them to build guided navigation, toggle between summary and detail views, and create simple what-if scenarios without needing a separate page for each variation. Used sparingly, bookmarks add genuine flexibility. Used everywhere, they tend to confuse users who are not sure which button does what.

Test With Real Users Before Calling It Done

The final step we never skip is putting a dashboard in front of the people who will actually use it before considering the project finished. We watch how someone interacts with it for the first time, without guiding them, and note where they hesitate or ask questions. Those moments almost always point to a design fix rather than a data problem.

This feedback loop is part of why our Power BI engagements include iteration rounds rather than a single handoff. Dashboards that look attractive in a demo but confuse real users in daily use do not deliver lasting value. We would rather spend an extra round of revisions getting the layout right than hand over something polished on the surface but difficult to actually work with.

We also encourage clients to revisit dashboard design periodically, not just at launch. Business priorities shift, new KPIs become important, and a dashboard that was perfectly tuned a year ago can quietly drift out of alignment with what people actually need to see today. A short design review every few quarters keeps a dashboard relevant rather than letting it slowly become background noise.

Conclusion

Designing an attractive Power BI dashboard is not about adding more visuals or chasing the latest formatting trend. It comes down to clarity: knowing the question being answered, organising information by importance, applying colour and typography with discipline, choosing the right visual for each piece of data, and testing the result with real users. None of these steps require advanced technical skill on their own, but together they are what separate a dashboard that gets opened once during a demo from one that becomes part of someone's daily routine.

These are the habits we apply on every Power BI engagement at Versich, whether we are building a single executive summary page or a full suite of operational reports across multiple departments. They are also habits that compound. A dashboard built with a clear hierarchy, a disciplined palette, and the right visuals for its data tends to need far fewer revisions down the line, because it was designed around how people actually think and work from the very first draft.

If you would like our team to design or refine a Power BI dashboard for your organisation, take a look at our Power BI consulting and development services, or browse examples of our work in our Power BI portfolio.To discuss your dashboard project with our team, please visit our Contact Us page.