Data & Analytics

Strategizing and crafting compelling branding and user experiences in a data-driven world.

Analytics products fail in a distinctive way: they present everything and help with nothing. The technical work of getting data onto a screen is often complete long before anyone has decided which decision that screen is supposed to support.

What makes analytics UX hard

• Dashboards without a question. A dashboard that does not answer a specific recurring question becomes wallpaper within a month.
• Cognitive load. Charts, filters, segments and date ranges multiply quickly. Defaults do more for usability than options.
• Explaining methodology. Users need to know how a number was calculated before they will act on it, especially when it contradicts their expectation.
• Visualisation honesty. Axis choices, aggregation and colour scales can mislead without anyone intending it.
• Self-serve versus curated. Giving everyone a query builder is not the same as making an organisation data-literate.

How we approach it

We start from the decisions, not the data model — the specific recurring judgements the product should make faster or more confidently. Everything on the screen then has to justify itself against one of those decisions.