Enterprise Information Architecture Because Users Dont Care About Your Org Chart 2758

8/3/2019 Enterprise Information Architecture Because Users Dont Care About Your Org Chart 2758

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2010 Louis Rosenfeld LLC (www.louisrosenfeld.com). All rights reserved. 1

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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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



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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



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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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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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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



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Content modeling


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



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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



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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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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



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gCombine interfaceswhen possible

Two boxes bad, one box good, usually

Will users understand?



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gThe role of advanced search 1/2

Continued

Not a likely

startingpoint

for users who

are searching



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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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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)



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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

Enterprise Information Architecture Because Users Dont Care About Your Org Chart 2758

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