The Problems we hear about the most
A report has been sent out. No one seems to read it.
Dashboards designed for the person who built them rarely survive contact with the person who needs them.
Forty metrics that don't give answers to anything.
When a BI dashboard surfaces everything equally, users learn to ignore it, losing the signal in the volume.
Analysts may get it, but operators don't.
A product that requires expertise to read has a shrinking audience, however strong the underlying data is.
Three screens become thirty, and there's no navigation.
Data products accumulate screens faster than design thinking and lack navigational architecture.
Fast queries but comprehension is slow.
Real-time pipelines and instant query speeds mean nothing when the interface still makes thinking feel like work.
What good data analytics design looks like
Dashboard UI design where the most important number is always the first thing a user sees.Data visualisation that matches the chart to the question, not to what looks impressive in a demo.Role-specific views for analysts, operators, and executives with each answering their actual decisions.BI and reporting interfaces where filters, drill-downs, and exports work without instructions.A design foundation that scales cleanly as the product adds depth and new users.Some of the Clients we served
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Almost always, before the roadmap says it does. Teams reach for new features when adoption is low, but low adoption is rarely a feature problem. A dashboard UI review almost always finds that users stopped engaging with most of what was built, months before anyone on the product team noticed.
By refusing to design one thing for all of them. An executive, a data analyst, and a field ops manager share the same data but need entirely different relationships with it. We map each role to the decisions they make, then build views that answer those decisions directly. Shared data. Distinct experiences.
The ones people use daily answer a question they already have. The ones they avoid require a new mental model before anything makes sense. Hierarchy matters more than feature count; one clear metric, supporting context, depth behind a click. That structure is a deliberate design decision. It won't emerge from the data on its own.
Completely differently. A static report is a document. Users arrive with a question and want a complete answer. A real-time dashboard is a monitoring surface where the job is surfacing change, not displaying the state. Visual weight, alert hierarchy, and update cadence all work differently, and conflating the two is where most data visualisation goes wrong.
Progressive disclosure. Show the answer, hide the working. Most people using a BI dashboard need to trust the number, not audit the query behind it. We design for the person who checks the product every morning and needs it to be immediately readable. Depth stays accessible for the power user. It's never the first thing anyone sees.
It's a research phase that has nothing to do with data. We sit with the people who use the product, watch what they do, and listen for the decisions they're trying to make. Visualisation design follows from that. Every chart and every hierarchy answers a real question. If it doesn't, it has no place on the screen.
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