This is how I, Chris Lysy of freshspectrum.com, define data UX. It is inspired by the Don Norman and Jakob Nielsen definition of User Experience but tailored to data products.
Over the last couple of decades I have called myself a data designer, data visualization designer, report designer, data communicator, human centered data designer, and more. The problem was, none of those categories quite fit what I do for my clients. I do more than just visualization and reporting and what I do is more specific than user experience design and communications.
In short, I believe what I do is Data User Experience (a.k.a. Data UX).
What is Data UX?
The overall experience a person has when discovering, interpreting, and acting upon data and insights – encompassing all aspects of how users interact with data products, visualizations, reports, and analytics tools, including their ability to access information, comprehend findings, derive meaning, and translate insights into decisions or actions.
Key aspects of Data UX:
- Human-centered – Focuses on how effectively users can generate valuable insights from data
- Context-aware – Considers the user’s analytical skill level, time constraints, and decision-making needs
- Outcomes-driven – Measures success by whether users achieve their intended goals
- End-to-end – Covers the complete data interaction journey from initial question through final outcome
The Seven Assessment Criteria
- Accessible – Can users actually get to and use this data in their real-world context?
- Readable – Is the presentation clear and easy to comprehend?
- Engaging – Does it capture initial attention and motivate users to dive in?
- Interesting – Does it sustain involvement and keep users moving through the content?
- Informative – Does it provide valuable, relevant insights?
- Memorable – Do key points stick with users afterward?
- Actionable – Does it lead to clear decisions or next steps?
The Seven Deadly Sins of Data UX
- Inaccessible – Locked away in formats, systems, or complexity that users can’t reach or navigate
- Unreadable – Cluttered, confusing, or overwhelming presentation that obscures meaning
- Ignorable – Fails to capture attention; users dismiss or skip without engaging
- Boring – Loses users along the way with monotonous, tedious, or overly dense content
- Meaningless – Provides data without context, relevance, or valuable insights
- Forgettable – Nothing sticks; users walk away with no lasting understanding
- Directionless – Provides information but no clear path forward or next steps
Additional Considerations
Beyond the core seven factors, consider these supplementary aspects when evaluating data experiences:
- Trust & Credibility – Are data sources, methodologies, and limitations transparently communicated? Does the design convey authority without misleading?
- Efficiency & Performance – How quickly can users find answers, navigate between views, or complete their tasks? Do visualizations load promptly?
- Error Prevention & Recovery – Does the design prevent misinterpretation of data? Are limitations and context clearly communicated to avoid user mistakes?
- Personalization & Customization – Can the experience adapt to different user types (executives vs analysts) or allow users to customize views, filters, or formats to their needs?
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