Most dashboards have a scatterplot. Most scatterplots don’t tell you anything you can act on. And that’s a shame, because a well-designed scatterplot is one of the most powerful tools in Power BI – it can surface the one insight that changes a decision.
The difference between a scatterplot that drives decisions and one that just fills space isn’t the data. It’s the design choices. Get them right and every dot points your team toward a clear next step. Get them wrong and you’ve got a pretty cloud of points that everyone glances at and nobody uses.
Here are the four choices that matter most.
The axis pair: What are you actually comparing?
This is the big one. Before you touch Power BI, you need to answer a simple question: what business decision is this chart supposed to inform?
The axis pair is where that answer lives. Revenue versus cost tells you about profitability. Customer count versus satisfaction tells you about service quality at scale. Store foot traffic versus average transaction value tells you where your best-performing locations sit – and why.
The wrong axis pair doesn’t just give you a boring chart. It gives you a perfectly accurate answer to the wrong question. And that’s worse than no chart at all, because it looks authoritative.
So start with the decision. What does your team need to act on this week? The axes follow from that — not the other way around.
The grain: One dot, one decision
“Grain” sounds technical. It isn’t. It just means: what does each dot represent?
Is each dot a customer? A store? A region? A single day’s transactions? The grain determines what story the scatterplot can tell. Get it wrong and you’ll either see a cloud of noise where no pattern can survive, or a handful of summary points that hide the detail you actually need.
Here’s the rule of thumb. Each dot should represent something your team can make a decision about. If a dot represents “all of Queensland”, that’s too coarse – you can’t act on a state. If a dot represents a single transaction, that’s too granular – you’ll drown in points. The sweet spot is usually the level at which your team manages the business: a store, a product line, a customer segment, a sales rep.
Match the grain to the decision. That’s it.
Reference lines: Where “interesting” lives
A scatterplot without reference lines is just dots on a page. You can see clusters. You can guess at patterns. But you can’t tell whether something is actually worth your attention.
Reference lines change that. An average line splits the chart into quadrants – above average and below, on both axes. Suddenly those four quadrants mean something. Top-right is where your high performers live. Bottom-left is where the problems are. And the outliers (the dots sitting well away from the pack) become impossible to miss.
A target line does the same thing. A benchmark against last quarter. A break-even threshold. Any line that represents a standard your team cares about turns a scatterplot from “here’s your data” into “here’s what’s worth looking at.”
Without reference lines, you’re asking people to interpret a chart. With them, you’re handing them a starting point for a decision.
Colour: The quiet decision that changes everything
Colour isn’t decoration. It’s a second dimension: one that can reveal patterns the axes can’t.
Used well, colour segments your data in a way that adds meaning. Each region gets its own colour, and suddenly you notice that your top-performing stores are all in the same state. Each product line gets its own colour, and you see that one category is dragging down profitability across the board. Each risk level gets its own colour, and the high-risk dots jump off the page.
Used badly, colour creates a rainbow that means nothing. Ten colours for ten categories that don’t matter. A gradient that looks impressive but doesn’t map to any decision. Colour for the sake of colour.
The question to ask is simple: does this colour help someone make a decision? If the answer is no, it’s noise. And noise is the enemy of insight.
From dots to decisions
Here’s the thing about scatterplots. They’re not complicated. But they’re easy to get wrong in ways that look fine – a chart that renders, that has data, that sits on a dashboard – but quietly tells your team nothing.
When the axis pair answers a real business question, the grain matches the decision, the reference lines mark what matters, and the colour adds a dimension instead of clutter, something changes. Your team stops looking at the chart and starts acting on it. The scatterplot stops being decoration and becomes a decision tool.
Want to see what your scatterplots could be telling you? Let’s talk. We’ll walk through your current dashboards, spot the gaps, and show you where the real insights are hiding.