Data interfaces

Dashboard or data story? Choose the interface around the question

A dashboard supports repeated exploration; a data story guides a reader through an argument. Some projects need both. Define the audience, source and update rhythm before choosing charts or animation.

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Put the question before the chart

A dataset, audience and update owner determine whether the work is a dashboard, story or both. Inspect Dardo's public interfaces, then scope one representative view.

01

Identify the reader's repeat task

An operations team may need to filter recent records, compare periods and return to the same view every week. That is a dashboard task. A public audience may need context, a sequence of evidence and an explanation of what a pattern can and cannot mean. That is a story task. If both audiences matter, do not force one interface to carry both jobs without an explicit transition.

Start with three questions a real reader will ask. For each, record the data source, unit, time period, missing values and decision the answer supports. This prevents a visually striking chart from implying certainty that the underlying records do not provide.

Identify the reader's repeat task
QuestionDashboardData story
Who controls the path?The reader filters and revisitsThe author proposes a sequence
How often does it change?Usually with the underlying recordsWhen the editorial explanation is reviewed
What must be checked?Filters, units, states and permissionsSources, context, claims and navigation
02

Design the source and uncertainty into the page

A number needs a label, unit, period, geography and source. A missing record must not look like zero. A filter state needs to remain visible when someone copies a chart or reads it on a phone. When a story selects a subset, state that selection and offer a path to inspect the fuller dataset where permission allows.

Shiimain is a public prototype that begins with territory and then reveals geographic evidence and dashboard views. Janus Observatory uses a cinematic opening, atlas and explanatory layers for speculative futures. They show different ways to move between narrative and exploration. Neither case certifies the data for a new client or establishes measured reader understanding.

03

Scope the data pipeline as well as the interface

Ask who collects, cleans, approves and refreshes the data; what happens when an update fails; and which records are public. A dashboard with live filters may need authentication, access rules and monitoring. A published story may need editorial review, citations and a stable snapshot so the explanation still matches the figures readers see. Those are different delivery and maintenance responsibilities.

A useful first milestone combines one representative dataset, a view, its source note and the interaction that answers the main question. Test it with a person who did not design the chart. If they cannot state the unit, filter or limitation, improve the information design before adding more visual effects.

Sources & editorial notes

Sources

How we built this guide

EvidenceThis guide combines 2 linked sources, Dardo’s analysis and clearly labeled project examples.

LimitsVisible design and content decisions are not presented as measured ranking, traffic or conversion gains.

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