or.. Making light work of data Stephen Hall National Lead, Web Strategy & Information Architecture 28 August 2009 Improving the UX of data rich interfaces

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

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

UX Australia peer reviews – earnest pleas:

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

The classic hierarchy Discrete, objective facts about a thing or event Data with relevance & purpose Information with experience, values, insights & context

The knowledge value chain Value add Value add Comprehensible Actionable

The knowledge value chain Comprehensible Actionable The 5 Cs: Condensation Contextualisation Calculation Correction Categorisation The 4 Cs: Conversation Connection Consequences Comparison

The 5 Cs:

Condensation

Contextualisation

Calculation

Correction

Categorisation

The 4 Cs:

Conversation

Connection

Consequences

Comparison

Condensation Comprehensible

Contextualisation Comprehensible

Calculation Comprehensible

Correction Comprehensible

Categorisation Exposed structure Exposed structure Exposed structure Self streaming Comprehensible

Conversation Actionable

Connection Linking data sets Actionable

Consequences Actionable

Comparison Exposing relative values User control over criteria Actionable

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!

Of course meaning depends.. … on where you’re coming from

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 (thanks, Donna)

Real world examples- overview 593 pages

Real world examples- overview GroceryChoice training.gov.au New site coming Some themes: Structure Content Tools Juxtaposition Connection Visualisation ..for bringing out meaning

Structure

Content

Tools

Juxtaposition

Connection

Visualisation

Structure Find a subset quickly Expose structure Create your own structure Discover unsought info Find a subset quickly Expose structure

Content access Clarity of purpose Self streaming Self elimination Anticipated needs Non-preferred terms Contextual support Information scents Forgiveness “ Aquatic invertebrates” “ Edible fats”

Tools Decision support Be notified Save stuff Personalise the view Take stuff away Contribute

Juxtaposition & connection Side by side version comparison Juxtaposition of different data sets

Visual Design Visual wayfinding system Visual wayfinding system Jon Hicks- Icons for interaction

Visualisation http://www.informationisbeautiful.net/

The government data wave The cathedral vs the bazaar

The govt data wave..

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

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

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

Digressions - tools One pair of licences to give away. Is it under your seat? Thanks, guys

Takeaways Comprehensible Actionable To reveal or enable Meaning The 5 Cs: Condensation Contextualisation Calculation Correction Categorisation The 4 Cs: Conversation Connection Consequences Comparison

The 5 Cs:

Condensation

Contextualisation

Calculation

Correction

Categorisation

The 4 Cs:

Conversation

Connection

Consequences

Comparison

Questions?

Questions?