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 didn’t 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 presenter’s own direct experiences from large scale projects in the pharmaceutical, educational, aged care and consumer advocacy sectors.

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

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

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 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

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!

26. Visualisation http://www.informationisbeautiful.net/

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