The importance of structured data to the practive of Information Architecture / User Experience

1. Structured Data:
None / Some / All
EuroIA VI, Paris, September 2010

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1995-2010 = Gazillions of Websites
Our design problem was an evolution of
visual literacy
—Readers were trained to find information in
printed books/magazines/newspapers
—Digital publications lack physical context
—Location and scope of information was invisible

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Clients = Publishers Users = Readers
Our Design Task was to connect
Readers to content
—Adapt graphic language – type, color, image –
from the page to the screen
—Create navigation systems that help users
understand what they can find on a website
—Communicate the structure of content in
flexible repeatable units

4. 2010=Massive Undifferentiated Data

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2010 Users are
—Convinced they can find what they want
“on the Internet”
—Producing & managing dematerialized content:
photos, videos, music, email, compound
documents
—Creators & consumers with storage/creation
and retrieval/consumption needs
—Looking for something all the time

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2010 Users want to
—Record, share, publish
—Be convinced, amused, in control
—Find, sort, sift and copy
—Mix, reorder and arrange
Users now have the experience of solving
problems by manipulating metadata even though
they don’t know what metadata is

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Today’s IA/UX Problem
Every IA/UX problem is a Data Continuum
—No Structure Vacuum Raw
—Some Structure Marsh Eatable
—Complete Structure Field Cooked

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Unstructured Data
Data Vacuum:
no metadata has been added to items
Even Data Vacuums include content & context
The 50-year-old Information Retrieval /
Library Science trade-off:
—Precision: finding only what you are looking for
—Recall: finding everything that might contain
what you are looking for

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Data with no structure: Names
—A character-string a person, place or thing is known by
—People have many names: professional names, familiar
names, legal names
—Places and things have many names in different
languages
—As data, a name presents a major problem:
IT IS NOT UNIQUE
—For example: “paul kahn”

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“paul kahn” string exist in many places
—About 299,000 results in Google
—25 different people in LinkedIn
—100+ different people in Facebook
—378 photos on Flickr
(where the tag “paulkahn” is used for two
different people)

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There are many “paul kahn”s
Paul W. Kahn, Dr. Paul Kahn, Paul Kahn, Roshi Paul Paul Kahn
author and Law Urologist in writer, editor, Genki Kahn chef, The
Professor at Plantation FL psychological Spiritual Publican,
Yale University, counselor and Director of Zen Chicago IL
New Haven CT disability rights Garland in
advocate in Wyckoff, NJ
Newton MA

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Semi-structured Workarounds
Add additional strings for context
[screen grab of sample completion for paul
kahn in Google toolbar or Google search]

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Where did I put that document?
The tools we use:
—Personal Memory
—Folder names
—Desktop search
What kinds of structure can we present?

18. Implicit metadata:
—Document type
—File name
—Time/Date stamp

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LATCH (+):
Five ways to organize information for understanding
and ease of use
Location
Alphabet
Richard Saul Wurman
Time INFORMATION ANXIETY 2
Category
Hierarchy
+ Common Focus

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Semi-Structured Data
Data Marsh: some metadata without predefined
language or requirements
—Tagging : ad hoc uncontrolled keywords
—Time / Location stamps: where and when
—Each metadata dimension is flat (no hierarchy)
and independent
—Many kinds of relationships can be inferred

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Aggregation/Reproduction Sites
—Sites that aggregate user-provided content
Slideshare / YouTube / Dailymotion / Vimeo /
SoundCloud / Flickr
—Sites where users can create and republish
content to social networks
LinkedIn / Facebook / Twitter

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Structured Data
Data Fields: where metadata has been explicitly added
to items according to an agreed-upon standard
—The Content is made to fit a pre-defined structure
—The required parts of the structure are completed
—Each metadata dimension qualifies and reinforces the
meaning of the content
—Many kinds of relationships can be harvested

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Map of the Market

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Newsmap.jp

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NY Times Immigration Explorer

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NPR Patchwork Nation

33. DC Homicide Map
— http://dc.everyblock.com/crime/by-offense/homicide/by-date/2010-01-
01,2010-05-31/#tallermap

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USHMM Propaganda Timeline

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Microsoft Live Labs Pivot

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Would the world be a better place if
—Everything had a unique ID?
—Every digital object with a unique ID contained
structured data?
How does structured data affects quality of life questions?

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A Proverb for User Centered Design
—Hwa is thet mei thet hors wettrien the him self nule
drinken
[who can give water to the horse that will not drink of
its own accord?]
(Old English Homilies, circa 1175)
—You can lead a horse to water but you can’t make it
drink

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Structured Data Value Proposition
—2010 Users know what they want
—Almost no one wants to create Structured Data
—Our clients and their audience increasingly
understand how to use Structured Data
—The Structured Data Value Proposition is the
key to our profession