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Enterprise Information ArchitectureBecause Users Dont Care About Your Org Chart
March 30, 2010
Merit Network
Louis Rosenfeld
www.louisrosenfeld.com
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About Me
Independent IA consultant and blogger(www.louisrosenfeld.com)
Founder, Rosenfeld Media, UX publishinghouse (www.rosenfeldmedia.com)
Work primarily with Fortune 500s and other largeenterprises
Co-author, Information Architecture for the WorldWide Web (1998, 2002, 2006)
Founder and past director, the InformationArchitecture Institute (www.iainstitute.org) and UserExperience Network (www.uxnet.org)
Background in librarianship/information science
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Seminar Agenda
Welcome/Introduction
Topic: Top-Down Navigation
Break
Topic: Bottom-Up Navigation (content modeling)Exercise #1: Metadata
Topic: Bottom-Up Navigation (metadata)
Lunch
Topic: Search
Exercise #2: Search AnalyticsBreak
Topic: Research Methods
Topic: Governance and Organizational Change
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Introduction
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Introduction:
IA in one slideDefinition: the art and science of
structuring, organizing and labelinginformation to help people find and
manage information Balances characteristics
and needs ofusers,content and context
Top down (questions)& bottom up (answers)
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Introduction:
Only one IA ruleParetos Principle (the 80/20 rule)
20% of content satisfies 80% of usersneeds
20% of possible IA options address 80% ofcontent 20% of IA options address 80% of users
needs
IAs goal: figure out which 20%No other rules, just guidelines
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Introduction:
IA is about priorities
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What an Enterprise Is
Large, distributed, decentralized organizationmade up of multiple business units
Distributed
Functionally in many different businesses (e.g.,HR vs. communications, or hardware vs. software)
GeographicallyDecentralized
Large degree of authority and responsibilityresides in hands of business units in practice (ifnot officially)
Business units often own significant infrastructure(technical, staff, expertise)
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IA and EIA:
The differencesThe enterprise challenge: providing
centralized access to information in alarge, decentralized, distributed
environmentInformation often organized by business
function (e.g., org chart), not in waysusers think
Not textbook IA; highly dependent onbusiness context
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The Challenge of EIA:
Competing trendsTrend toward autonomy
Cheap, easy-to-use democratizing technology Human tendency toward autonomy
Trend toward centralization Users desire for single-point of access Managements desire to control costs and
communications
These tend to cancel each other out, getting usnowhere
Result: content silos and user confusion
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Indicators of Problematic EIA:
Intranet glitchesHow come I didnt know your department
was developing a product similar toours?
Why couldnt we find any relevant casestudies to show that importantprospect?
Why do our sales and support staff keepgiving our customers inconsistentinformation?
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Indicators of Problematic EIA:
External-facing site glitchesOur customers think were still in the
widget business; after all these M&As,why dont they realize that wevediversified?
We have so many great products that gotogether; why dont we cross-sell more?
Customers keep asking for productsupport through our sales channel; whydont they use the sites FAQs and techsupport content?
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The Holy Grail:
Cutting against the political grain
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Example: Expense Reporting
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So How Do We Get There?
Let it go There is no single solution Redemption lies within phased, modular, evolving
approaches that respect 80/20 rule
Your friends Straw men Your colleagues and professional networks
This seminar provides straw men for EIA design EIA methods EIA team design and governance
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Top-Down
Navigation
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Top-Down Navigation Roadmap
Main page
Site hierarchy
Site map
Site index
Selective navigation
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Top-Down Challenges
Top-down IA
Anticipates questions that users arrive with Provides overview of content, entry points
to major navigational approachesIssues
What do we do about main pages? Portals: the answer? Other ways to navigate from the top down The dangers of taxonomies
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Top-Down Evolution:Univ. Michigan example 1/2
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Top-Down Evolution:Univ. Michigan example 2/2
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Portal Solutions:Why they fail 1/2
Organizational challenges Fixation on cosmetic, political Inability to enforce style guide changes, portal
adoption
Lack of ownership of centralizing initiatives, orownership in wrong hands (usually IT)
Information architecture challenges Taxonomy design required for successful portal
tool implementation Always harder than people imagine Taxonomies break down as they get closer to local
content (domains become specialized)
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Portal Solutions:Why they fail 2/2
Challenges for users Portals are shallow (only one or two levels deep) Poor interface design Users dont typically personalize
More in James Robertsons Taking a business-centric approach to portals (http://www.steptwo.com.au/papers/
kmc_businessportals/index.html)
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Top-Down Navigation:Design approaches
Main pages
Supplementary navigation Tables of contents Site indices Guide pages
Taxonomies for browsing
Varieties: product, business function,topical
Topic pages
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Top-Down Navigation:Main pages
Often 80% of discussion of EIA dedicated tomain page Important real estate But there are other important areas
Navigational pages Search interface Search results Page design (templates, contextual navigation)
Divert attention from main pages by creatingalternatives, new real estate: supplementarynavigation
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Top-Down Navigation:Supplementary navigation
Examples Site maps/TOC Site indices
Benefits: Create new real estate Can evolve and drive evolution from org-chart
centered design to user-centered design
Relatively low cost to initially implementDrawbacks:
Often unwieldy for largest enterprises (not at IBM,Microsoft, failure at Vanguard)
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Top-Down Navigation:Site maps
Condensed versions of site hierarchy Hierarchical list of terms and links Primarily used for site orientation Indirectly cut across subsites by presenting multi-
departmental content in one place
But still usually reflects org chartAlternative plan
Use site map as test bed for migration to user-centric design
Apply card sorting exercises on second and thirdlevel nodes
Result may cut across organizational boundaries
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Site Map:Visually
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Site Map: State of Nebraska
Majority of
links reflectorg chart
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Site Map: State of Kentucky
Evolving
toward
more user-
centered,
topicalapproach
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Top-Down Navigation:Site indices
Flat (or nearly flat) alpha list of terms and links
Benefits Support orientation and known-item searching
Alternative flattened view of content Can unify content across subsites
Drawbacks Require significant expertise, maintenance May not be worth the effort if table of contents and
search are already available
Specialized indices may be preferable (shorter,narrower domain, focused audience)
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Site Index:Visually
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Site Index:Am. Society of Indexers example
Full site index
@1000entries for
smallish site Too large to
easily browse
Replace withsearch?
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Specialized Site Index:CDC example
Not a full site
index
Focuses on health
topics Narrow domain Specialized
terminology
Possibly still toolarge to browse
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Specialized Site Index:PeopleSoft example
Product focus
A largeundertaking
atPeopleSoft
High valueto users
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Mature Site Index:Informed by search analytics
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Top-Down Navigation:Guides
Single page containing selective set of important linksembedded in narrative text
Address important, common user needs Highlight content for a specific audience Highlight content on a specific topic Explain how to complete a process
Can work as FAQs (and FAQs can serve as interface toguides)
Benefits Technically easy to create (single HTML page) Cut across departmental subsites Gap fillers; complement comprehensive methods of
navigation and search
Can be timely (e.g., news-oriented guides, seasonal guides) Minimize political headaches by creating new real estate
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Guides:Visually
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Guides:Vanguard example 1/2
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Guides:Vanguard example 2/2
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Guides:IBM example
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Top-Down Navigation:Topic Pages
Selective taxonomy improvement
Portions of a taxonomy that expandbeyond navigational value
Help knit together enterprise contentdeeper down in taxonomyNew real estate can be used by
Individual business units (to reducepressure on main page) or
Cross-departmental initiatives
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Topic Pages:CDC example
Subtopics
now
compriseonly a small
portion of
page
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Top-Down Navigation:Taxonomies & portals
Can a single taxonomy unify an enterprise site? First: can one be built at all? Software tools dont solve problems (see
metadata discussion)
Approaches Multiple taxonomies that each cover a broad
swath of enterprise content: audience, subject,task/process, etc.
Two-step approach:1. Build shallow, broad taxonomy that will answer where
will I find the information I need?
2. Rely on subsite taxonomies to answer where in thisarea will I find the information I need?
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Top-Down Navigation:Impacts on the enterprise
Potential of small steps around which to build
more centralized enterprise efforts
Site map and site index creation and maintenance Guide and topic page creation and maintenance Large editorial role, minimal technical
requirements for both
May be preferable to tackle more ambitious
areas much later Developing and maintaining top-level taxonomy Connecting high-level and low-level taxonomies
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Top-Down Navigation Roadmap
Main page
Site hierarchy
Site map
Site index
Selective navigation
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Top-Down Navigation Takeaways
Main pages and portals: Bypass for now, add guides
over time
Site hierarchy/taxonomy: Start shallow,"simple" (e.g., products); add progressively harder
taxonomies (work toward faceted approach)
Site map/ToC: Use as a staging ground for a moretopical approach
Site index: Move from generalized to specialized
around a single topic, or augment with frequentsearch queries/best bets work
Guides: Start with a handful, then expand and rotate
based on seasonality or other criteria of relevance
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Bottom-UpNavigation
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Bottom-Up Navigation Roadmap
Content modeling
Metadata development
Metadata tagging
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Bottom-Up Navigation:The basics
Focuses on extracting answers from
content
How do I find my way through this content?
Where can I go from here?Goals
Answers rise to the surface Leverage CMS for reuse and syndication of
content across sites and platforms
Improve contextual navigation Increase the effectiveness of search
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Content Modeling:The heart of bottom-up navigation
Content models
Used to convey meaning within select,high-value content areas
Accommodate inter-connectednessSame as data or object modeling?
Absolutely not!
Many distinctions between data and semi-structured text
Text makes up majority of enterprise sites
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Content Modeling:The basics
Based on patterns revealed duringcontent inventory and analysis
What makes up a content model?
1. Content objects2. Metadata (attributes and values)3. Contextual links
Applies to multiple levels of granularity Content objects Individual documents
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Content Modeling:Were already doing it at page level
album page = title/artist/release + tracks + cover image
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Content Modeling:Content analysis reveals patterns
artist descriptionsalbum reviews
album pages artist bios
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Content Modeling:Answer some questions
artist descriptions album reviews
album pages artist bios
What contextual navigation
should exist between
these content objects?(see Instones
Navigation StressTest--http://user-
experience.org/uefiles/
navstress/ )Are there missing content
objects?
Can we connect objectsautomatically?
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Content Modeling:Fleshing out the model
artist descriptions
album reviews
album pages
artist biosdiscography
concert calendar
TV listings
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Content Modeling:Connecting with metadata, rules
Content
Objects
link to other Content Objects by leveraging common
Metadata Attributes
album page album review, discography, artist Album Name, Artist Name,
Label, Release Date
album review album page Album Name, Artist Name,
Review Author, Source,Pub Date
discography album review, artist description Artist Name, Album Name,
Release Date
artist
description
artist bio, discography, concert
calendar, TV listing
Artist Name, Desc Author,
Desc Date
artist bio artist description Artist Name, Individual
Artist Name
concert
calendar
artist description Artist Name, Tour, Venue,
Date, Time
TV listing artist description Artist Name, Channel,
Date, Time
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Content Modeling:Problematic borders
artist descriptions
album reviews
album pages
artist biosdiscography
concert calendar
TV listings
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Content Modeling:When to use
Use only forhigh value content
High value content attributes based on users,content, context, including
High volume Highly dynamic Consistent structure Available metadata
Available content management infrastructure Willing content owners
Much content can and will remain outsideformal content models
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Content Modeling:Steps for developing a model
1. Determine key audiences (whos using it?)2. Perform content inventory and analysis
(what do we have?)
3. Determine document and object types (whatare the objects?)
4. Determine metadata classes (what are theobjects about?)
5. Determine contextual linking rules (where dothe objects lead us to next?)
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Content Modeling:Content object types 1/2
List known object types
For each audience:
Are there types that dont fit? Examples: company executive bios, Q&A
columns
Venue reviews may be part of a separatecontent model
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Content Modeling:Content object types 2/2
For each audience (continued):
Gap analysis: are there types missing thatusers might expect?
Examples: Gig reviews, Buy the CD, Links tomusic in the same genre
Which types are most important to eachaudience?
Fans of the band: Interviews with the bandmembers
Casual listener: Samples of the CD tracks
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Content Modeling:Metadata 1/2
Determine which objects would benefit
from metadata
Develop three types of metadata
Descriptive Intrinsic Administrative
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Content Modeling:Metadata 2/2
Aim to balance utility and cost
Answer most important questions: who,what, where, why, when, how?
Cost-benefit analysis Development and maintenance costs of
controlled vocabularies/thesauri
Ability of in-house staff to apply properly
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Content Modeling:Contextual linking rules
Are there specific objects for which thesequestions arise again and again? Where would I go from here? What would I want to do next? How would I learn more?
You have a rule if The questions apply consistently The answers work consistently Metadata can be leveraged to connect questions
and answers
Unidirectional links or bidirectional?
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Content Modeling:Impacts on the enterprise
Content models are a means for tying togethercontent across business unit boundaries
Content modeling is modular; over time, contentmodels can be connected across theenterprise
Major benefits to users who get beyond mainpage
Can help justify CMS investments
Not all content areas andowners areappropriate to work with
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Content Modeling:Putting it all together
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CMS Selection:EIA needs
Support metadata management
(Interwoven)
Support shared metadata workflow
Author creation/submission/tagging(distributed)
Editorial tagging (centralized)
Editorial review (centralized)Ability to support contextual linking logic
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Metadata:What is metadata?
Data about data
Information which describes a document,
a file or a CD
Common metadata
CD information: title, composer, artist, date MS Word document properties: time last
saved, company, author
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Metadata:Three types
1. Intrinsic: metadata that an object holdsabout itself (e.g., file name or size)
2. Descriptive: metadata that describesthe object (e.g., subject, title, oraudience)
3. Administrative: metadata used tomanage the object (e.g., time lastsaved, review date, owner)
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Metadata:Common sources
Vocabularies from other parts of your
organization (e.g., research library)
Competitors
Commercial sources (seewww.taxonomywarehouse.com)
Your sites users
Search analytics Folksonomies User studies (e.g., free listing, card sorting)
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Metadata:Value for the Enterprise 1/2
Search: cluster or filter the search bymetadata, like title or keyword
Browse: create topical indexes byaggregating pages with the samemetadata
Personalization and customization: showcontent to an employee based on theirrole or position in the company, e.g.
engineer or manager
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Metadata:Value for the Enterprise 2/2
Contextual linking: create relationshipsbetween individual or classes of contentobjects (e.g., cross-marketing onllbean.com)
The purpose is to connect
Content to content Users to content
To provide value, metadata requires
consistency (structural and semantic)
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Metadata: Scaling problems
Barriers toenterprise
metadatadevelopment:
Volume ofmetadatavocabs./silos
Complexityof semantic
relationships
(beyond
synonyms)
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Metadata attributes:Easy to difficult 1/2
Level of
Difficulty
Metadata
Attribute
Comments
Easy Business unit
names
These are typically already
available and standardized
Easy toModerate Chronology Variations in formats (e.g.,12/31/07 versus 31/12/07) usually
can be addressed by software
Moderate to
Difficult
Place names Although many standards exist
(e.g., state abbreviations and
postal codes), many enterprises
(and their business units) usecustom terms for regions (such as
sales territories)
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Metadata attributes:Easy to difficult 2/2
Level of
Difficulty
Metadata
Attribute
Comments
Moderate to
Difficult
Product
names
Product granularity can vary
greatly; marketing may think in
terms of product families; sales in
terms of items with SKU numbers,
and support in terms of product
parts that can be sold individually
Difficult Audiences Audiences, such as customers or
types of employees, vary widely
from unit to unit
Difficult Topics The most ambiguous type of
metadata; difficult for individuals,
much less business units, to come
to agreement on topical metadata
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Metadata:Structural consistency
Standard formats and approaches enableinteroperability, which enables sharing ofmetadata.
Examples RDF (Resource Description Format) Topic Maps Dublin Core OAI (Open Archives Initiative)
Sources Academia/scholarly publishing world Little from data management world
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Metadata:RDF (Resource Description Format)
A syntax for expressing semantic
relationships
Basic components
1. Resource2. Property type
From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html
3. Value
4. Property
132
4
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Metadata:Topic Maps
Potential syntax for content modeling, semantic webs
Most simply, made up of
topics (e.g., Lucca,
Italy), occurrences
(e.g., map, book),and associations (e.g.,
is in, written
by)
Source: Tao of TopicMaps, Steve Pepper(http://www.ontopia.net/
topicmaps/materials/
tao.html)
topics
occurrences
associations
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Metadata:The Dublin Core
A schema for expressing semantic relationships
Can use HTML or RDF syntax
Useful tool (or model) for creating document
surrogates (e.g., Best Bet records)A standard, but not a religious one
Selecting fewer attributes may be a necessity inenterprise environment
Attribute review can be useful as an enterprise-wide exercise
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Metadata:Dublin Core elements 1/2
Title: A name given to the resource
Creator: An entity primarily responsible for making thecontent of the resource
Subject: A topic of the content of the resourceDescription: An account of the content of the resource
Publisher: An entity responsible for making the resource
available
Contributor: An entity responsible for makingcontributions to the content of the resource
Date: A date of an event in the lifecycle of the resource
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Metadata:Dublin Core elements 2/2
Type: The nature or genre of the content of the resource
Format: The physical or digital manifestation of the resource
Identifier: An unambiguous reference to the resource within a
given contextSource: A Reference to a resource from which the present
resource is derived
Language: A language of the intellectual content of the
resource
Relation: A reference to a related resource
Coverage: The extent or scope of the content of the resource
Rights: Information about rights held in and over the resource
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Metadata:Dublin Core in HTML
Dublin Core elements identified with DCprefix
From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html
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Metadata:Dublin Core and RDF
Syntax and schema combination is usefulBut where are the metadata values?
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Metadata:OAI and metadata harvesting
OAI: Open Archives Initiative Comes from academic publishing world Provides means for central registration of
confederate repositories
Repositories use Dublin Core; requests betweenservice and data providers via HTTP; replies(results) encoded in XML
Metadata harvesting
Enables improved searching across compliantdistributed repositories Does not address semantic merging of metadata
(i.e., vocabulary control)
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Metadata:Semantic consistency 1/2
Provided through controlled vocabularies.
What is a controlled vocabulary?
A list of preferred and variant terms A subset of natural language
Why control vocabulary? Language is Ambiguous
Synonyms, homonyms, antonyms,contronyms, etc. (e.g., truck, lorry, semi,pickup, UTE)
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Metadata:Semantic consistency 2/2
Users
Documents and Applications
Communication Chasm
Example
Personal Digital Assistant
Synonyms
Handheld Computer
"Alternate" Spellings
Persenal Digitel AsistentAbbreviations / Acronyms
PDA
Broader Terms
Wireless, Computers
Narrower Terms
PalmPilot, PocketPC
Related Terms
WindowsCE, Cell Phones
Control vocabularyso yourusers dont have to!
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Metadata:Semantic relationships
Three types
1. Equivalence: Variant terms with samemeaning (e.g., abbreviations and
synonyms)2. Hierarchical: Broader term, narrower
term relationships
3. Associative: Related terms that arerelated to each other
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Metadata:Levels of control
Simple Complex
Synonym
Rings
Authority
Files Thesauri
Classification
Schemes
Equivalence Hierarchical Associative
(Vocabularies)
(Relationships)
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Metadata semantic relationships:Hard to hardest
Level of
Difficulty
Type of
Relationship
Examples
Hard Synonymous Synonym rings andauthority lists
Harder Hierarchical Classification
schemes
Hardest Associative Thesauri
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Metadata:Synonym rings
Used in many search engines to expandthe number of results
Words that are similar to each other are
linked togetherExample for a multinational company
Annual leave (Australia), the holidays (US),public holidays (Australia, US), vacation
(US), bank holidays (UK), holiday(Australia and UK), personal leave (all)
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Metadata:Authority files
Pick list of the authorizedwords to use in
a field
Can have some equivalence relationships
Example using authors
Poe, Edgar Allan--USE FOR Poe, E.A. Poe, E.A.--USE Poe, Edgar Allan
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Metadata:Classification schemes
Classification Systematic arrangement of knowledge, usually
hierarchical
Placement of objects into a scheme which makessense to the user and relates them to otherobjects
Two types of classification schemes Enumerative classification: hierarchical
organization into which objects are placed
Faceted classification: organization by facets orattributes that describe the object
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Metadata:Enumerative classification
Really good to classify small numbers of objectsor objects that can live in only one place
Provides good browsing structureCan be polyhierarchical, where objects live in
many placesBest known: the taxonomy of life, Dewey
Decimal Classification, Library of CongressClassification
Most familiar on the Web: Yahoo!, Open
Directory
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Metadata:Enumerative classification example
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Metadata:Faceted classification 1/2
Describes the object with numerous
facets or attributes
Each facet could have a separate
controlled vocabulary of its ownCan mix and match the facets to create a
browsing structure
Easier to manage the controlledvocabularies
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Metadata:Faceted classification 2/2
Facets for a roast chicken recipe
Preparation: Roast / bake Main ingredient: Chicken Course: Main dish
Drawbacks of faceted classification
Too many facets attached to an object canmake indexing hard to do
Browsing facets may not be as clear asbrowsing a hierarchy; many paths to thesame object
M t d t
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Metadata:Faceted classificationexample
M t d t
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Metadata:Faceted classificationexample
M t d t
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Metadata:What is a thesaurus?
Traditional use
Dictionary of synonyms (Rogets) From one word to many wordsInformation retrieval context A controlled vocabulary in which
equivalence, hierarchical, and associativerelationships are identified for purposes ofimproved retrieval
From many words to one word
M t d t
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Metadata:Thesaurus entry example
E t i M t d t
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Enterprise Metadata:Challenges
Two barriers to enterprise metadata
1. Interoperability (structural)2. Merging enables controlled vocabularies to
work as a whole (semantic)Interoperability must come before
merging (merging requires knowledge
of which vocabularies to merge)Few standards in use
E t i M t d t
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Enterprise Metadata:Structural approaches
If directly marking up documents, this
approach is probably impractical in the
enterprise
Better uses: Limited high value documents (e.g.,
content models)
Document surrogates (e.g., Best Betrecords)
E t i M t d t
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Enterprise Metadata:Merging vocabularies
Extremely difficult, and currently rare
Mostly found in libraries, academia,scholarly publishing, and other
resource-poor environmentsExamples, hard to hardest
Cross-walking vocabularies Switching vocabularies Meta-thesaurus Single thesaurus
M i V b l i
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Merging Vocabularies:Vocabulary cross-walking
Map terms peer-to-peer between
individual vocabularies
Primarily handles synonyms, notrelationships
Can be handled manually or throughautomated means (pattern-matching)
Doesnt scale well beyond two or three
vocabularies
Merging Vocab laries
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Merging Vocabularies:Switching vocabulary
A single vocabulary that maps to existing
vocabularies (primarily synonyms)
Similar to cross-walking, but better at
handling translation when there aremore than two or three vocabularies to
connect
Merging Vocabularies
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Merging Vocabularies:Meta-thesaurus
A switching vocabulary which also
includes thesaural relationships
(essentially a thesaurus of thesauri)
Example: National Library of MedicinesUMLS (Unified Medical Language
System)
Merges over 100 vocabularies Describes fairly homogeneous domain
(medical literature) for fairly homogeneous
audience (health science professionals)
Merging Vocabularies
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Merging Vocabularies:Single unified thesaurus
Highly impractical in enterprise context
Metadata:
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Metadata:What is metadata?
Data about data
Information which describes a document,
a file or a CD
Common metadata
CD information: title, composer, artist, date MS Word document properties: time last
saved, company, author
Metadata:
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Metadata:Three types
1. Intrinsic: metadata that an object holdsabout itself (e.g., file name or size)
2. Descriptive: metadata that describesthe object (e.g., subject, title, oraudience)
3. Administrative: metadata used tomanage the object (e.g., time lastsaved, review date, owner)
Metadata:
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Metadata:Common sources
Vocabularies from other parts of your
organization (e.g., research library)
Competitors
Commercial sources (seewww.taxonomywarehouse.com)
Your sites users
Search analytics Folksonomies User studies (e.g., free listing, card sorting)
Metadata:
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Metadata:Big org,
big picture
Metadata: Scaling problems
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Metadata: Scaling problems
Barriers toenterprise
metadatadevelopment:
Volume ofmetadatavocabs./silos
Complexityof semantic
relationships
(beyond
synonyms)
Metadata in the Large Org:
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Metadata in the Large Org:Challenges
Two barriers to enterprise metadata
1. Interoperability (structural)2. Merging enables controlled vocabularies to
work as a whole (semantic)Interoperability must come before
merging (which requires knowledge of
which vocabularies to merge)
Few standards in use
Metadata attributes:
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Metadata attributes:Easy to difficult 1/2
Level of
Difficulty
Metadata
Attribute
Comments
Easy Business unit
names
These are typically already
available and standardized
Easy to
Moderate
Chronology Variations in formats (e.g.,
12/31/07 versus 31/12/07) usually
can be addressed by software
Moderate to
Difficult
Place names Although many standards exist
(e.g., state abbreviations and
postal codes), many enterprises
(and their business units) usecustom terms for regions (such as
sales territories)
Metadata attributes:
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Metadata attributes:Easy to difficult 2/2
Level of
Difficulty
Metadata
Attribute
Comments
Moderate to
Difficult
Product
names
Product granularity can vary
greatly; marketing may think in
terms of product families; sales in
terms of items with SKU numbers,and support in terms of product
parts that can be sold individually
Difficult Audiences Audiences, such as customers or
types of employees, vary widely
from unit to unitDifficult Topics The most ambiguous type of
metadata; difficult for individuals,
much less business units, to come
to agreement on topical metadata
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Metadata semantic relationships:
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Metadata semantic relationships:Hard to hardest
Level of
Difficulty
Type of
Relationship
Examples
Hard Synonymous Synonym rings andauthority lists
Harder Hierarchical Classification
schemes
Hardest Associative Thesauri
Metadata:
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Metadata:Strategy for large orgs 1/2
Coordinate to ensure:
Structural interoperability from the start Semantic mergability over time Vocabulary control and maintenance
through both manual and automated
means
A workflow model and policies to support: Decentralized tagging and vocabulary updating
(through suggestions of new terms)
Centralized review and maintenance
Enterprise Metadata:
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Enterprise Metadata:Strategy for large orgs 2/2
Serious metadata is beyond the means of
most enterprises
Encourage local (e.g., departmental) vocabularydevelopment
Provides organizational learning and localbenefit
Enterprise-wide, start with easier vocabularies;work your way to harder ones over time;
suggested sequence:1. Business functions2. Products3. Topics
Bottom-Up Navigation Roadmap
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Bottom-Up Navigation Roadmap
Content modeling
Metadata development
Metadata tagging
Bottom-Up Navigation Takeaways
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Bottom Up Navigation Takeaways1/3
Content models
Use to support contextual navigation Apply only to homogenous, high-value
content Won't transfer easily across silos and will
require significant metadata development
Bottom-Up Navigation Takeaways
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Bottom Up Navigation Takeaways2/3
Metadata development
Distinguish attributes (and structuralinteroperability) from values (andsemantic merging)
Costs and value both increase as theseincrease: Complexity of relationships between terms
(equivalence=>hierarchical=>associative)
Level of control (synonym rings=>authorityfiles=>classification schemes=>thesauri) Think small: facets instead of a single
taxonomy
Bottom-Up Navigation Takeaways
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Bottom Up Navigation Takeaways3/3
Metadata tagging
Make choices based on actual needs(e.g., content models) rather thanexhaustive indexing
Consider costs of application and upkeep Need for professional expertise Metadata is a moving target that matches
other moving targets (users and content)
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EIA and Search
EIA and Search
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EIA and Search
Search systems are a natural enterprise IA tool Automated Crawls what you tell it to Doesnt care about politics
Problems with shrink-wrapped search tools Default settings, IT ownership minimize
customization to fit the enterprises needs
Results often not relevant, poorly presentedCustomization is the answer
Within the realm of your teams abilities and if IT will allow it!
EIA and Search:
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EIA and Search:Visually
Enterprise Search Design:
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Enterprise Search Design:Potential improvements
Basic search system components
Our focus:
1. Clear interface2. Enhanced
queries3. Improved
results
(relevance &
presentation)
Enterprise Search Roadmap
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Enterprise Search Roadmap
Search queries
Search interface
Search results
Search Interface Design:
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Search Interface Design:The Box
The Box unifies
IBM.com
Consistent:
Placement Design Labeling Functionality
Search Interface Design:
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gCombine interfaceswhen possible
Two boxes bad, one box good, usually
Will users understand?
Search Interface Design:
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gThe role of advanced search 1/2
Continued
Not a likely
startingpoint
for users who
are searching
Search Interface Design:
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gThe role of advanced search 2/2
Suggestions
Use forspecialized
interfaces
Reposition asReviseSearch
Dont bother
Contextualizing Search Help:
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g pEbay example
Search Interface and Queries:
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Functionality and visibility
Hide functionality? Consider the Google
Effect, human nature and the LCD
Dont hide it?
Not if users expect it Legacy experience (e.g., Lexis-Nexis users) Specialization (e.g., patent searchers)
Not if content allows/requires it Specialized content and applications (e.g., staff
directory)
The Query:
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yQuery language considerations
Natural language Usually dont show up in search logs Low priority, but nice to support
Operators (Booleans, proximity, wild cards) Booleans: use default AND for multi-term queries
Less forgiving than treating as phrase, more selective thanOR
Most retrieval algorithms will find results for just one term Rely on other approaches (e.g., filtering, clustering, Best
Bets) to reduce search results overload
Low priority: Proximity operators (e.g., enterprise(W3) architecture), wild cards (e.g., wom*n)
The Query:
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yQuery building considerations
Large potential benefits to improvingintelligence behind search queries Adding semantic richness to queries allows for
stronger searches without touching content
Overrides enterprise bias embedded in content A centralized (enterprise-wide) process
Query building approaches Spell checking: can be automated Stemming: can be automated Concept searching: requires manual effort Synonyms (via thesaurus): requires manual effort,
but no need to be comprehensive
Spell Checker:
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p
Sur La Table example
A la Google
Stemming:
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gIBM example
IBM uses
Fast Search
Concept Searching:
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p gSocial Security Admin. example
SSA uses
Convera
Thesaural Search:
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ERIC example
Enterprise Search Interface:G
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Guidelines
Hide functionality on initial enterprise-wide
search
Cast the net widely: rely on query builders
to generate larger, higher quality resultsets
Use filtering/clustering to narrow
Use Best Bets to ensure strong initialresults
Individual Search Results:G l
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Goals
Enable users to quickly understand somethingabout each document represented
That something: confirm that a known-itemhas been found, or distinguish from other
resultsAlign to searching behaviors (determined
through user testing, persona/scenarioanalysis, site search analytics)
Known-item Open-ended/exploratory Comprehensive research
Individual Search Results:A h
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Approaches
Basic approaches
Document titling Displaying appropriate elements for each
resultThese approaches have value in any
context, but especially useful in
enterprise setting
Document Titling:D i l Ch l l
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DaimlerChrysler example
What do these document titles tell you?
And what do they tell you about DaimlerChrysler?
Document Titling:F d l
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Ford example
Descriptive document titles provide clear value
Displaying Appropriate Elements:1) D t i l t
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1) Determine common elements
Develop table of available elements (includingmetadata) for disparate documents and records
Comes after content inventory and analysisDevelop table of common elements
Collapse similar elements (e.g., creator derived from author,artist, source)
Consider Dublin Core as model Include bare minimum elements (e.g., title and description)
Displaying Appropriate Elements:2) S l t i t l t
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2) Select appropriate elements
Choose common elements which match most commonsearching behaviors Known-item Open-ended Comprehensive research Etc.
Considerations Which components are decision or action based? Which components are of informational value only?
Display these elements for each search result
Step #1: common content elementsSt #2 l t l t t di l
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Step #2: select elements to display
Step #1 Title Description Creator Topic Date
Tech. Report Y Y Y Y Y
Policy Y N Y Y Y
Product
Sheet
Y Y N Y N
FAQ Y N N Y N
Step #2 Title Description Creator Topic Date
Known-Item Y N Y N Y
Open-Ended Y Y N Y Y
Individual Search Results:C l bi U i it l
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Columbia University example
Long display for open-
ended searchers
shorter display for
known-item searchers
Individual Search Results:Wh t h t?
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What happens next?
Augment with next step actions per result
Open in separatewindow
Get more like this Print Save Email
Determine next stepsthrough contextual
inquiry
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Presenting Search Result Groups:Clustering & filtering
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Clustering & filtering
clustered results
list results
Consider using
clustered results
rather than list
results
Our user studies show that all Category interfaces were more effective
than List interfaces even when lists were augmented with category names
for each result Dumais, Cutrell & Chen
Presenting Search Result Groups:Methods of clustering and filtering
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Methods of clustering and filtering
Use existing metadata and other distinctions(easier) Document type (via file format or CMS) Source (author, publisher, and business unit) Date (creation date? publication date? lastupdate?) Security setting (via login, cookies)
Use explicit metadata (harder) Language Product Audience Subject/topic
Clustering by Topic:LL Bean example
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LL Bean example
Category
matches
displayedrather than
individual
results
Filtering by Source:BBC example
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BBC example
Selecting a
tab filters
results
Clustering by Content Type:c|net example
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c|net example
Mention content modeling
Results clustered in multiple
content types
Clustering by Language Example:PeopleSoft Netherlands
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PeopleSoft Netherlands
Result clusters
for Dutch andEnglish
Mixed Presentation ofSearch Results
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Search Results
159
Best Bets:By popular demand
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By popular demand
Recommended links Ensure useful results for top X (50? 100?)
most popular search queries
Useful resources for each popular queryare manually determined (guided by
documented logic)
Useful resources manually linked topopular queries; automatically displayed inresult page
Best Bets Example:BBC
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BBC
Logic for BBCBest Bets Is query a
country name?
(yes) Then do we
have a countryprofile? (yes)
Then do wehave alanguageservice? (yes)
Best Bets:In the enterprise context
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In the enterprise context
Who does the work? Difficult to assign queries to different business units (e.g.,
computing means different things to different businessunits)
Can serve as impetus for centralized effortOperational requirements
Logicbased on users needs (e.g., queries) and businessrules
Policythat assigns responsibilities, negotiates conflicts (e.g.,who owns computing)
Opportunity to align Best Bets to user-centric divisions
(e.g., by audience: a computing best bet forresearchers, another for IT staff)
Enterprise Search:Impacts on the enterprise
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Impacts on the enterprise
Designs Simple query builders (spell checker, stemming) Search-enhancing thesaurus
Policies Best Bets design and selection Style guide (result titling, search interface implementation)
Staffing needs Content inventory and analysis Interface design Work with IT on spidering, configuration issues Ongoing site search analytics Editorial (e.g., Best Bets creation)
Search Tool Selection:EIA needs 1/2
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EIA needs 1/2
To basic evaluation criteria (fromSearchTools.com)
Price
Platform Capacity Ease of installation Maintenance
Search Tool Selection:EIA needs 2/2
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EIA needs 2/2
add: Ability to crawl deep/invisible web Ability to crawl multiple file formats Ability to crawl secure content API for customizing search results Work with CMS Duplicate result detection/removal Ability to tweak algorithms for results retrieval and
presentation
Federated search (merge results from multiplesearch engines/data sources)
Enterprise Search Roadmap
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Search queries
Search interface
Search results
Enterprise Search Takeaways
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Search interface and queries Consistent location and behavior Keep as simple as possible Use "refine search" interface instead of "advanced
search"
Soup up users queries (e.g., spell checking)Search results
Feature appropriate elements for individual results Consider clustered results, especially if explicit,
topical metadata are available Best bets results for top X common queries
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EIA ResearchMethods
EIA Research Methods:Learn about these three areas
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Learn about these three areas
Content, users and
context drive:
IA researchIA designIA staffingIA educationand everything else
EIA Research Methods:Sampling challenges
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Sampling challenges
How do you achieve representativesamples in the face of these difficulties?
Awareness: Who and what are out there?
Volume: How much is there? Can wecover it all?
Costs: Can we afford to investigate at thisorder of magnitude?
Politics: Who will work with us? And whowill try to get in the way?
EIA Research Methods:Reliance on alternative techniques
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Reliance on alternative techniques
Standard techniques may not work inenterprise settings
Alternatives often incorporate traditionalmethods and new technologies
Web-based surveys (e.g., SurveyMonkey) Remote contextual inquiry and task
analysis (via WebEx)
Web-based card sorting (e.g., WebSort) Log analysis tools (e.g., WebTrends)
EIA Research Methods:A closer look
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A closer look
Content-oriented methods Content inventories Content value tiers
Context-oriented methods
Sampling stakeholders Departmental scorecard
User-oriented methods 2-D scorecard
Automated metadata development Freelisting Site search analytics
Content Inventory:Enterprise context
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Enterprise context
Issues
Even greater sampling challenges Content research is even more critical:
serves as a cross-departmental exercise
Approaches
Balancing breadth and depth
Talking to the right people Value-driven
Multidimensional Inventory:Incomplete yet rich
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Incomplete yet rich
EIA requires balanced, iterative sampling (where CMSimplementation may require exhaustive inventory)
Balance scope (breadth) with granularity (depth)
Extend inventory to all discernible areas of content,
functionality: Portals and subsites Application (including search systems) Supplemental navigation (site maps, indices, guides) Major taxonomies Structured databases Existing content models Stakeholders
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Value Tier Approach:Potential quality criteria
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ote t a qua ty c te a
Select appropriate criteria for yourbusiness context, users, and content
Authority
Strategic value Currency Usability Popularity/usage Feasibility (i.e., enlightened content
owners)
Presence of quality existing metadata
Value Tier Approach:Weighting and scoring
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g g g
Value Tier Approach:Prioritization
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Assessing Stakeholders:What to learn from them
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Strategic Understanding of business mission and goals, and
fit with larger enterprise mission and goals Theory Practice
Culture: tilt toward centralization or autonomy Political entanglements
Practical Staff: IT, IA, design, authoring, editorial, usability,
other UX (user experience) Resources: budget, content, captive audiences Technologies: search, portal, CMS
Stakeholder Interviews:Triangulate your sample
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g y p
Org chart: business unit representatives Will provide strategicoverview of content and
whom it serves
May have some knowledge of content More importantly, they know people who do in
their units
Additionally, political value in talking with unit repsFunctional/audience-centered
Subject Matter Experts (SMEs): represent powerusers; valuable for pointing out content thataddresses major information needs
Audience advocates (e.g., switchboard operators):can describe content with high volume usage
Stakeholder Interviews:Finding the low-hanging fruit
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g g g
Assessment should reveal degree ofenlightenment Early adopters Successful track records visible within the
enterprise
Understand/have experience with enterprise-wideinitiatives
Willingness to benefit the enterprise as a whole They just plain get it
Youve got to play to win: lack of interest andavailability mean loss of influence
Stakeholder Interviews:Indicators of enlightenment
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g
Technology assessment: who has/uses theclassic 3?
Portal Search engine CMS
Staff review: who has relevant skills/expertise
on their staff?
IA review: what areas of enterprise site have
strong architectures?
These areas may indicate redundant costs,
targets for centralization
Involving Stakeholders:Departmental Report Card
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p p
Information Architecture
Heuristic
Dept.
1
Dept.
2
Dept.
3
Supports orientation B- B B
Supports known-item searching A C+ C
Supports associative learning B C C
Supports comprehensive research A B+ B
Passes navigation stress test C F C+
Safe User Sampling:The 2D Scorecard
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Combines alternative, apoliticalmethodsfor determining segments to sample,e.g.:
Role-based segmentation Demographic segmentation
Distracts stakeholders from org chart-itis, to purify sampling
Enables evaluation methods (e.g., taskanalysis, card sorting)
The 2D Scorecard:Role-based segmentation
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g
Roles cut across political boundaries
Profile core enterprise-wide businessfunctions
Why does the enterprise exist? Examples: Sell products, B2B or B2C
activities, manufacture products, inform
opinion, etc.
Determine major actors in each process
The 2D Scorecard:Demographic segmentation
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Standard, familiar measure; also cutsacross political boundaries Gender Geography Age Income level Educa
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