The document discusses structured data and metadata. It notes that as more metadata is added to content, the data becomes more structured, ranging from unstructured (no metadata) to semi-structured (some user-added metadata like tags) to structured (explicitly defined metadata standards are followed). When data is highly structured with a variety of metadata fields completed according to standards, it enables many types of relationships to be discovered and supports more precise searching.

1. Structured Data
None / Some / All
Prepared by:
Paul Kahn – Experience Design Director
October, 2014
Media Lab, Aalto University
Helsinki, Finland

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1995-2012 = 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. 2012=Massive Pattern of Nodes

5. 2012=Nodes with Geo Context

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Today 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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Today Users want to
— Record, share, publish
— Be convinced, amused, in control
— Find, sort, sift and copy
— Mix, reorder and arrange
They don’t explicitly know what metadata is (in most cases)
They are solving problems by implicitly manipulating metadata

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Today’s IA/UX Problem
Every IA/UX problem is a Metadata Continuum
No Structure Leaping into a Vacuum Raw
Some Structure Stepping into a Marsh Eatable
Complete Structure Traversing a Field Cooked

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Structured Data Value Proposition
— People want to find things, they don’t want to “learn” how to
find things
— People understand how to use Structured Data
— No one wants to create Structured Data
— It is our task to leverage the Structured Data people already
understand

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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: not missing anything 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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There are many “paul kahn”s
Paul W.
Kahn, author
and Law
Professor at
Yale
University,
New Haven CT
Dr. Paul
Kahn,
Urologist in
Plantation FL
Paul Kahn,
General
Partner at
Himalaya
Capital
Ventures,
Silicon Valley,
CA
Roshi Paul
Genki Kahn
Spiritual
Director of
Zen Garland
in Wyckoff, NJ
Paul Kahn
President of
Airbus Group
in the UK

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What are most people searching for?

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Who is searching?

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Use algorithms to surface what users might want to
see (and what we want them to see)

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Cut to the chase

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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
— Document content

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Semi-Structured Data
Data Marsh: some metadata without predefined language
or requirements
— Tagging : users add uncontrolled keywords
— Profile: users intentionally add metadata about themselves
— Time / Location stamps: where and when
— Tracking: users unintentionally add metadata about
themselves as interactions are tracked

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

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Explicit
metadata:
— tags

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Implicit
metadata:
— Related

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Explicit
metadata:
— Views
— Likes
— Description

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Explicit
metadata:
— Statistics
— Description

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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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Item with Facets

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Profile metadata
— Flickr
— LinkedIn
— Facebook
— Amazon

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Profile metadata
— Flickr
— LinkedIn
— Facebook
— Amazon

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Profile metadata
— Flickr
— LinkedIn
— Facebook
— Amazon

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Transaction metadata
— Flickr
— LinkedIn
— Facebook
— Amazon

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— Amazon
Profile metadata
Transaction metadata

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Combining user + content metadata
— YouTube
— Netflix
— LinkedIn
— Spotify

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Contact Information
Paul Kahn
Experience Design Director
pkahn@madpow.com
Mad*Pow
Portsmouth | Boston | Louisville
www.madpow.com