Making light work of data- improving the UX of data rich interfaces- UX Australia
Download as PPT, PDF1 like747 views
The document discusses improving user experience (UX) in data-rich interfaces by highlighting the importance of presenting data in a comprehensible and actionable manner. It explores concepts like knowledge management, the value chain, and methodologies to enhance data interpretation through various strategies. Real-world examples showcase the application of these principles in different projects, focusing on user interaction with data to reveal meaningful insights.
Making light work of data- improving the UX of data rich interfaces- UX Australia
- 1. or.. Making light work of data Stephen Hall National Lead, Web Strategy & Information Architecture 28 August 2009 Improving the UX of data rich interfaces
- 2. Definitions Data Rich Discrete, objective facts about a thing or event Heavy Full of possibility Interface … the means by which users interact with a system
- 3. Qualification & a story UX Australia peer reviews – earnest pleas: “ Focus on real world stuff, please” But first – let’s talk about Knowledge Management “ This subject is too big” What this presentation is: About SMS’s experience over numerous projects…. … involving presentation of sets of data to existing or new audiences…. … .that sought to bring out the potential of the data to satisfy both user and client needs And what this presentation is not: We don’t pretend to be expert in all aspects of the UX of data presentation These were real world projects, with constraints- not necessarily bleeding edge What I can show in 45 minutes is necessarily limited
- 4. The classic hierarchy Discrete, objective facts about a thing or event Data with relevance & purpose Information with experience, values, insights & context
- 5. The knowledge value chain Value add Value add Comprehensible Actionable
- 6. The knowledge value chain Comprehensible Actionable The 5 Cs: Condensation Contextualisation Calculation Correction Categorisation The 4 Cs: Conversation Connection Consequences Comparison
- 11. Categorisation Exposed structure Exposed structure Exposed structure Self streaming Comprehensible
- 13. Connection Linking data sets Actionable
- 15. Comparison Exposing relative values User control over criteria Actionable
- 16. The overall UX design goal To reveal or enable Meaning Inherent in the data- structure, themes Emerging through meta-information Emerging over time Emerging through juxtaposition Not imposed!
- 17. Of course meaning depends.. … on where you’re coming from
- 18. Behaviours & circumstances Information seeking behaviour Known item Exploratory Don’t know.. Re-finding Circumstances Multiple, parallel ways for meaning to be revealed Search, browse, fuzzy search, contextual discovery, non-preferred terms, personalisation, notifications, preference setting, export, best bets, top item showcase…… Fuzzy search, contextual help, tool tips, personalisation, preference setting, notifications, non-preferred terms, cookies, best bets
- 19. Real world examples- overview 593 pages
- 20. Real world examples- overview GroceryChoice training.gov.au New site coming Some themes: Structure Content Tools Juxtaposition Connection Visualisation ..for bringing out meaning
- 21. Structure Find a subset quickly Expose structure Create your own structure Discover unsought info Find a subset quickly Expose structure
- 22. Content access Clarity of purpose Self streaming Self elimination Anticipated needs Non-preferred terms Contextual support Information scents Forgiveness “ Aquatic invertebrates” “ Edible fats”
- 23. Tools Decision support Be notified Save stuff Personalise the view Take stuff away Contribute
- 24. Juxtaposition & connection Side by side version comparison Juxtaposition of different data sets
- 25. Visual Design Visual wayfinding system Visual wayfinding system Jon Hicks- Icons for interaction Beware of unintended consequences!
- 27. The government data wave The cathedral vs the bazaar
- 28. The govt data wave..
- 29. When doesn’t this work? Volume Complexity e.g. Open Source Intelligence Autonomy IDOL- revealing structure in unstructured data Disambiguation of concepts Faceted results Dynamic multi-dimensional presentation
- 30. When doesn’t this work? Volume Complexity e.g. Open Source Intelligence Autonomy IDOL- revealing structure in unstructured data ‘ Heat’ in data clusters Video text analysis
- 31. When doesn’t this work? Volume Complexity e.g. Open Source Intelligence Palantir- revealing structure in unstructured data Entity extraction from multiple data streams Connecting entities to find the bad guys
- 32. Digressions - tools One pair of licences to give away. Is it under your seat? Thanks, guys
- 33. Takeaways Comprehensible Actionable To reveal or enable Meaning The 5 Cs: Condensation Contextualisation Calculation Correction Categorisation The 4 Cs: Conversation Connection Consequences Comparison
- 34. Questions?
Editor's Notes
- #3: Data- alternative definitions Simple observations Text that does not answer questions to a particular problem Facts which are not yet interpreted
- #4: IS: About my company’s experience with a number of projects, both private & public sector These projects have involved the presentation of sets of data to either the same audiences who always experienced them (but better), or to new audiences Within the inevitable constraints, we have worked to create UXs that brought our the potential of the data and were aligned to audience and business needs NOT: I do not get up here to proclaim myself an expert in all aspects of data presentation These projects were not at all bleeding edge What you will see over 45 minutes cannot be comprehensive, however there may be things which interest you
- #5: Knowledge Management- these days lower profile, but underlies most things we do as UX professionals What do I mean by that? A core aspect of KM is a hierarchy- the Knowledge hierarchy or Information hierarchy, depending on how you come at it The hierarchy defines the relationships between Data, Information and Knowledge- and usually throws wisdom in at the top.
- #9: Revisit: what do we mean by data in KM sense When dealing with data the key task of the UX professional is to add value by: Creating context, and allowing the rich latent meaning to emerge from it. Not creating meaning, but creating the conditions for meaning to become visible How do we go about this?
- #10: Revisit: what do we mean by data in KM sense When dealing with data the key task of the UX professional is to add value by: Creating context, and allowing the rich latent meaning to emerge from it. Not creating meaning, but creating the conditions for meaning to become visible How do we go about this?