Making light work of data- improving the UX of data rich interfaces- UX Australia

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Not so long ago, back in the days of brochure ware online, we used to be glad just to see live data dished up in web sites. It was real, it was (sometimes) up to date, even if it was also inevitably dry, dense and tabular, and was often only there to be looked at. Those of us making web sites then didnt have too many data presentation options; our challenge was usually just to make it as clean and fast loading as possible. How we have moved on! These days, the web browser is a window onto a sea of rich data. Now, we expect to be able to understand it, personalise how we view it, add our own input to it and transact with it. At the same time, the volume of what is available threatens to overwhelm us. In short, the User Experience of data has changed completely. Public and private sector organisations are increasingly willing and able to expose aspects of their data both internally and externally, and are using the web as a key channel to do so. Looking internationally we are starting to see pressure on governments to open source key data holdings to allow organisations, community groups and individuals to re-use it creatively and in ways that government owners would never imagine. The reality is that User Experience designers and Information Architects are more and more likely to be dealing regularly with the challenges of rich data presentation. This talk examines some approaches to the analysis and presentation of rich data sets on the web. Drawing on the presenters own direct experiences from large scale projects in the pharmaceutical, educational, aged care and consumer advocacy sectors.

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<ul><li> 1. or..Making light work of data Stephen HallNational Lead, Web Strategy &amp; Information Architecture 28 August 2009 Improving the UX of data rich interfaces</li></ul> <p> 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 &amp; a story </p> <ul><li>UX Australia peer reviews earnest pleas: </li></ul> <p> Focus on real world stuff, please But firstlets talk about KnowledgeManagement This subject is too big </p> <ul><li>What this presentation is: </li></ul> <ul><li>About SMSs experience over numerous projects. </li></ul> <ul><li> involving presentation of sets of data to existing or new audiences. </li></ul> <ul><li> .that sought to bring out the potential of the data to satisfy both user andclient needs</li></ul> <ul><li>And what this presentation is not: </li></ul> <ul><li>We dont pretend to be expert in all aspects of the UX of data presentation </li></ul> <ul><li>These were real world projects, with constraints- not necessarily bleeding edge </li></ul> <ul><li>What I can show in 45 minutes is necessarily limited </li></ul> <p> 4. The classic hierarchy Discrete, objective facts about a thing or event Data with relevance &amp; purpose Information with experience, values, insights &amp; context 5. The knowledge value chain Value add Value add Comprehensible Actionable 6. The knowledge value chain Comprehensible Actionable </p> <ul><li>The 5 Cs: </li></ul> <ul><li>Condensation </li></ul> <ul><li>Contextualisation </li></ul> <ul><li>Calculation </li></ul> <ul><li>Correction </li></ul> <ul><li>Categorisation </li></ul> <ul><li>The 4 Cs: </li></ul> <ul><li>Conversation </li></ul> <ul><li>Connection </li></ul> <ul><li>Consequences </li></ul> <ul><li>Comparison </li></ul> <p> 7. Condensation Comprehensible 8. Contextualisation Comprehensible 9. Calculation Comprehensible 10. Correction Comprehensible 11. Categorisation Exposed structure Exposed structure Exposed structure Self streaming Comprehensible 12. Conversation Actionable 13. Connection Linking data sets Actionable 14. Consequences Actionable 15. Comparison Exposing relative values User control over criteria Actionable 16. The overall UX design goal To reveal or enableMeaning 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 youre coming from 18. Behaviours &amp; circumstances Information seeking behaviour Known item Exploratory Dont 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 (thanks, Donna) 19. Real world examples- overview 593 pages 20. Real world examples- overview GroceryChoice training.gov.au New site coming Some themes: </p> <ul><li>Structure </li></ul> <ul><li>Content </li></ul> <ul><li>Tools </li></ul> <ul><li>Juxtaposition </li></ul> <ul><li>Connection </li></ul> <ul><li>Visualisation </li></ul> <p>..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 Selfelimination 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 &amp; 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 26. Visualisation http://www.informationisbeautiful.net/ 27. The government data wave The cathedral vs the bazaar 28. The govt data wave.. 29. When doesnt this work? Volume Complexity e.g. Open Source Intelligence Autonomy IDOL- revealing structure in unstructured dataDisambiguation of concepts Faceted results Dynamic multi-dimensional presentation 30. When doesnt 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 doesnt this work? Volume Complexity e.g. Open Source Intelligence Palantir- revealing structure in unstructured dataEntity extraction from multiple data streams Connecting entities to find the bad guys 32. Digressions - tools One pair of licences to give away. Is it underyourseat? Thanks, guys 33. Takeaways Comprehensible Actionable To reveal or enableMeaning </p> <ul><li>The 5 Cs: </li></ul> <ul><li>Condensation </li></ul> <ul><li>Contextualisation </li></ul> <ul><li>Calculation </li></ul> <ul><li>Correction </li></ul> <ul><li>Categorisation </li></ul> <ul><li>The 4 Cs: </li></ul> <ul><li>Conversation </li></ul> <ul><li>Connection </li></ul> <ul><li>Consequences </li></ul> <ul><li>Comparison </li></ul> <p> 34. </p> <ul><li>Questions? </li></ul>