SVG visualizations as the fulfillment of dreams about your own custom visualization

I. Introduction

SVG visuals in Power BI unlock a level of precision and storytelling that standard charts can’t easily reach. Because SVG is just markup, every pixel – positions, shapes, labels, and interactions – can be driven by measures and dynamic business rules. The result: compact, brand-consistent visuals (custom gauges, bullet charts, waterfalls, timelines, scorecards, schematics) that stay responsive to slicers, drill-throughs, and row-level context without external add-ins.
In this article, we’ll walk step-by-step through building an SVG visual from scratch: choosing the coordinate system, binding measures to geometry, formatting text for legibility, layering annotations, and wiring interactivity. Along the way we’ll highlight best practices (scales, padding, alignment, accessibility, color tokens, performance hygiene) and call out common pitfalls (overdraw, label collisions, DAX bloat, and maintenance trade-offs). You’ll finish with a reusable pattern you can adapt to any KPI or layout.

We’ll also weigh the pros and cons. On the plus side: pixel-perfect control, small footprint, semantic-model reusability, and seamless theming. On the minus side: a learning curve, the need for disciplined naming and testing, and potential performance issues if markup or DAX isn’t optimized. Understanding both sides helps you decide when SVG is the right tool – and when a native visual is “good enough.”

Our team specializes in exactly this craft: transforming your favorite charts and KPIs into fully automated, production-ready Power BI reports powered by custom SVG. From discovery and design systems to DAX engineering and governance, we support clients end-to-end so their reports aren’t just beautiful—they’re maintainable, fast, and trusted.

II. What’s SVG is

SVG visualizations in Power BI are visuals that render graphics using Scalable Vector Graphics (SVG)—an XML-based format that stays crisp at any size. In Power BI, you typically generate SVG strings in DAX or Power Query and display them with a visual that supports HTML/SVG (e.g., HTML Content, Deneb, or custom visuals). This lets you draw fully custom shapes (icons, bars, bullets, Gantt bars, flags, sparklines) with dynamic colors, sizes, and labels driven by your data.

Why use SVG in Power BI?

- Pixel-perfect, resolution-independent graphics.
- Highly dynamic: every attribute (color, position, opacity, text) can respond to measures.
- Enables charts not available out-of-the-box (bullet charts, waterfall bridges, bespoke KPI tiles).

Trade-offs:

- More effort to design and maintain (you’re crafting SVG markup).
- Can be slower if you output very large SVGs per row.
- Accessibility and tooltips need deliberate handling.

III. Pros&cons of using SVG

Pros:

- Pixel-perfect control
  
  Exact positioning, sizing, and styling of every element (labels, ticks, shapes) beyond what native visuals allow.
- Fully data-driven
  
  Geometry and text can respond to slicers/filters via DAX, enabling slicer-aware KPIs, dynamic thresholds, and conditional annotations.
- Unlimited visual types
  
  Build bespoke charts (e.g., bullet gauges, custom waterfalls, timelines, schematics) not available natively.
- Brand consistency
  
  Centralize colors, typography, and spacing with measure-based tokens for consistent theming across reports.
- Lightweight & portable
  
  No external custom visual code; SVG is just text, easy to template, reuse, and version in source control.

Cons:

- Steep learning curve
  
  Requires comfort with both DAX and SVG; debugging long string measures can be tedious.
- Maintenance risk
  
  Without strict naming and modularization, SVG/DAX quickly becomes hard to read, test, and extend.
- Limited native interactivity
  
  Cross-highlighting, built-in tooltips, and accessibility features are weaker vs. standard visuals.
- Export & layout quirks
  
  Email subscriptions, PDF export, and mobile view can render differently; careful testing is needed.
- Performance pitfalls
  
  Excessive markup, many elements, or heavy string concatenation (especially in DirectQuery) can slow visuals.

IV. Best practices of using SVG

There is five best practices for using SVG in your report:

Design tokens first (colors, sizes, spacing)
- Centralize style as measures (e.g., __Color_Primary, __Font_S, __Pad_M), so themes change in one place.
- Example:
```
VAR cMain = [__Color_Primary]
VAR f12 = [__Font_S]
```
Modularize the SVG (compose from fragments)
- Build small measures for primitives (axis, bars, labels) and concatenate them.
- Example pattern:
```
__svg_axis(), __svg_bars(), __svg_labels() → CONCATENATEX({[__axis],[__bars],[__labels]},[Value],"")
```
Use a stable coordinate system
- Set a viewBox="0 0 W H" and derive all positions from margins and plot width/height.
- Example:
```
VAR W = 280
VAR mL = 12
VAR plotW = W - (mL + [mR])
positions = mL + scaleX(Value)
```
Be ruthless about element count
- Limit shapes; aggregate where possible; avoid per-point heavy decorations.
- Collapse repeated attributes with <g> groups and shared class/style strings.
Performance hygiene in DAX
- Cache with VAR and reuse; avoid repeated CALCULATE for the same sub-results.
- Build markup via CONCATENATEX over a small VAR table instead of many & concatenations.
- Guardrails: cap rows (TOPN), avoid heavy per-row logic in DirectQuery.

V. SVG as a future of Power BI Advanced Reports – real example

The image above shows a tornado chart displaying the percentage distribution of the turnover rate by department and gender.

How to create a measure to show it? Dax code has more than 150 rows so I will go through step by step

Each SVG visualization demands to set key settings of height and width of selected visual.

Key settings:

Wbase = 280 → base canvas width (px/user units) for the whole SVG.
mL, mR, mT, mB = 12 → left/right/top/bottom margins (padding) around the drawing area.
rowH = 34 → height of one row/category lane.
barH = ROUND(rowH*0.65,0) → bar thickness set to ~65% of the row height (so some vertical padding remains).

Typical use:

Inner plot width = Wbase - mL - mR
Row y-position = mT + rowIndex * rowH
Bar y = rowY + (rowH - barH)/2 (centers the bar within the row)