Dashboard and Data Visualization Design Questions

Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.

MediumTechnical

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Design a responsive dashboard layout for field sales managers on mobile devices. Identify which components to surface first, how to reorder and collapse content, how to handle filters and actions for touch, and strategies for dealing with intermittent connectivity and offline access.

MediumTechnical

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Explain when to use small multiples instead of a single combined chart. Provide a concrete example using 12 product categories over 12 months and discuss readability, comparison tasks, and layout constraints on desktop and mobile dashboards.

EasyTechnical

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List best practices for KPI card design on operational dashboards. Which elements should a KPI card include (value, unit, change, target, sparkline), how should you display target vs actual, how to show directionality, and how to incorporate thresholds and alert status so users act without experiencing alarm fatigue?

MediumTechnical

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You discover that dashboard numbers differ from a reconciled finance report. Describe a step-by-step debugging process to identify the root cause including checks for data latency, timezone conversions, joins, aggregations, currency or unit mismatches, and semantic layer definitions. Which quick queries and artifacts would you produce to demonstrate the issue?

MediumTechnical

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You have a Postgres orders table described as orders(order_id PK, user_id, occurred_at TIMESTAMP, region VARCHAR, amount DECIMAL) with about 100M rows. Write an optimized SQL query to compute daily unique active users for the last 30 days per region. Explain index recommendations, partitioning approach, and how to avoid full table scans.

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