Enterprise Information Architecture Because Users Don’t Care About Your Org Chart Fall 2007 Louis Rosenfeld www.louisrosenfeld.com

Fall 2007

Louis Rosenfeld

www.louisrosenfeld.com

About Me Independent IA consultant and blogger (www.louisrosenfeld.com) Founder, Rosenfeld Media, UX publishing house (www.rosenfeldmedia.com) Work primarily with Fortune 500s and other large enterprises Co-author, Information Architecture for the World Wide Web (1998, 2002, 2006) Founder and past director, the Information Architecture Institute (www.iainstitute.org) and User Experience Network (www.uxnet.org) Background in librarianship/information science

Independent IA consultant and blogger (www.louisrosenfeld.com)

Founder, Rosenfeld Media, UX publishing house (www.rosenfeldmedia.com)

Work primarily with Fortune 500s and other large enterprises

Co-author, Information Architecture for the World Wide Web (1998, 2002, 2006)

Founder and past director, the Information Architecture Institute (www.iainstitute.org) and User Experience Network (www.uxnet.org)

Background in librarianship/information science

Seminar Agenda Welcome/Introduction Top-Down Navigation Bottom-Up Navigation Search EIA and the Organization Research methods Governance and more Discussion

Welcome/Introduction

Top-Down Navigation

Bottom-Up Navigation

Search

EIA and the Organization

Research methods

Governance and more

Discussion

Introduction

Introduction: IA in one slide Definition: the art and science of structuring, organizing and labeling information to help people find and manage information Balances characteristics and needs of users, content and context Top down (questions) & bottom up (answers)

Definition: the art and science of structuring, organizing and labeling information to help people find and manage information

Balances characteristics and needs of users, content and context

Top down (questions) & bottom up (answers)

Introduction: Only one IA rule Pareto’s Principle (“the 80/20 rule”) 20% of content satisfies 80% of users’ needs 20% of possible IA options address 80% of content 20% of IA options address 80% of users’ needs IA’s goal: figure out which 20% No other rules, just guidelines

Pareto’s Principle (“the 80/20 rule”)

20% of content satisfies 80% of users’ needs

20% of possible IA options address 80% of content

20% of IA options address 80% of users’ needs

IA’s goal: figure out which 20%

No other rules, just guidelines

Introduction: IA is about priorities

What an Enterprise Is Large, distributed, decentralized organization made up of multiple business units Distributed Functionally in many different “businesses” (e.g., HR vs. communications, or hardware vs. software) Geographically Decentralized Large degree of authority and responsibility resides in hands of business units in practice (if not officially) Business units often own significant infrastructure (technical, staff, expertise)

Large, distributed, decentralized organization made up of multiple business units

Distributed

Functionally in many different “businesses” (e.g., HR vs. communications, or hardware vs. software)

Geographically

Decentralized

Large degree of authority and responsibility resides in hands of business units in practice (if not officially)

Business units often own significant infrastructure (technical, staff, expertise)

IA and EIA: The differences The “enterprise challenge”: providing centralized access to information in a large, decentralized, distributed environment Information often organized by business function (e.g., “org chart”), not in ways users think Not “textbook” IA; highly dependent on business context

The “enterprise challenge”: providing centralized access to information in a large, decentralized, distributed environment

Information often organized by business function (e.g., “org chart”), not in ways users think

Not “textbook” IA; highly dependent on business context

The Challenge of EIA: Competing trends Trend toward autonomy Cheap, easy-to-use democratizing technology Human tendency toward autonomy Trend toward centralization Users’ desire for single-point of access Management’s desire to control costs and communications These tend to cancel each other out, getting us nowhere Result: content “silos” and user confusion

Trend toward autonomy

Cheap, easy-to-use democratizing technology

Human tendency toward autonomy

Trend toward centralization

Users’ desire for single-point of access

Management’s desire to control costs and communications

These tend to cancel each other out, getting us nowhere

Result: content “silos” and user confusion

Indicators of Problematic EIA: Intranet glitches “How come I didn’t know your department was developing a product similar to ours?” “Why couldn’t we find any relevant case studies to show that important prospect?” “Why do our sales and support staff keep giving our customers inconsistent information?”

“How come I didn’t know your department was developing a product similar to ours?”

“Why couldn’t we find any relevant case studies to show that important prospect?”

“Why do our sales and support staff keep giving our customers inconsistent information?”

Indicators of Problematic EIA: External-facing site glitches “Our customers think we’re still in the widget business; after all these M&As, why don’t they realize that we’ve diversified?” “We have so many great products that go together; why don’t we cross-sell more?” “Customers keep asking for product support through our sales channel; why don’t they use the site’s FAQs and tech support content?”

“Our customers think we’re still in the widget business; after all these M&As, why don’t they realize that we’ve diversified?”

“We have so many great products that go together; why don’t we cross-sell more?”

“Customers keep asking for product support through our sales channel; why don’t they use the site’s FAQs and tech support content?”

The Holy Grail: Cutting against the political grain

Example: Expense Reporting

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

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

Top-Down Navigation

Top-Down Navigation Roadmap Main page Site hierarchy Site map Site index Selective navigation

Top-Down Challenges Top-down IA Anticipates questions that users arrive with Provides overview of content, entry points to major navigational approaches Issues What do we do about main pages? Portals: the answer? Other ways to navigate from the top down The dangers of taxonomies

Top-down IA

Anticipates questions that users arrive with

Provides overview of content, entry points to major navigational approaches

Issues

What do we do about main pages?

Portals: the answer?

Other ways to navigate from the top down

The dangers of taxonomies

Top-Down Evolution: Univ. Michigan example 1/2 Cosmetic changes

Cosmetic changes

Top-Down Evolution: Univ. Michigan example 2/2

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, or ownership 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)

Organizational challenges

Fixation on cosmetic, political

Inability to enforce style guide changes, portal adoption

Lack of ownership of centralizing initiatives, or ownership 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)

Portal Solutions: Why they fail 2/2 Challenges for users Portals are shallow (only one or two levels deep) Poor interface design Users don’t typically personalize More in James Robertson’s “ Taking a business-centric approach to portals” (http://www.steptwo.com.au/papers/kmc_businessportals/index.html)

Challenges for users

Portals are shallow (only one or two levels deep)

Poor interface design

Users don’t typically personalize

More in James Robertson’s “ Taking a business-centric approach to portals” (http://www.steptwo.com.au/papers/kmc_businessportals/index.html)

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

Main pages

Supplementary navigation

Tables of contents

Site indices

Guide pages

Taxonomies for browsing

Varieties: product, business function, topical

Topic pages

Top-Down Navigation: Main pages Often 80% of discussion of EIA dedicated to main 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 creating alternatives, new real estate: supplementary navigation

Often 80% of discussion of EIA dedicated to main 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 creating alternatives, new real estate: supplementary navigation

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 implement Drawbacks: Often unwieldy for largest enterprises (not at IBM, Microsoft, failure at Vanguard)

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 implement

Drawbacks:

Often unwieldy for largest enterprises (not at IBM, Microsoft, failure at Vanguard)

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 chart Alternative plan Use site map as test bed for migration to user-centric design Apply card sorting exercises on second and third level nodes Result may cut across organizational boundaries

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 chart

Alternative plan

Use site map as test bed for migration to user-centric design

Apply card sorting exercises on second and third level nodes

Result may cut across organizational boundaries

Site Map: Visually

Site Map: State of Nebraska Majority of links reflect org chart

Site Map: State of Kentucky Evolving toward more user-centered, topical approach

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)

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)

Site Index: Visually

Site Index: Am. Society of Indexers example Full site index @1000 entries for smallish site Too large to easily browse Replace with search?

Full site index

@1000 entries for smallish site

Too large to easily browse

Replace with search?

Specialized Site Index: CDC example Not a full site index Focuses on health topics Narrow domain Specialized terminology Possibly still too large to browse

Not a full site index

Focuses on health topics

Narrow domain

Specialized terminology

Possibly still too large to browse

Specialized Site Index: PeopleSoft example Product focus A large undertaking at PeopleSoft High value to users

Product focus

A large undertaking at PeopleSoft

High value to users

“Mature” Site Index: Informed by search analytics

Top-Down Navigation: Guides Single page containing selective set of important links embedded 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 to guides) 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

Single page containing selective set of important links embedded 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 to guides)

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

Guides: Visually

Guides: Vanguard example 1/2

Guides: Vanguard example 2/2

Guides: IBM example

Top-Down Navigation: Topic Pages “Selective taxonomy improvement” Portions of a taxonomy that expand beyond navigational value Help knit together enterprise content deeper down in taxonomy New “real estate” can be used by Individual business units (to reduce pressure on main page) or… Cross-departmental initiatives

“Selective taxonomy improvement”

Portions of a taxonomy that expand beyond navigational value

Help knit together enterprise content deeper down in taxonomy

New “real estate” can be used by

Individual business units (to reduce pressure on main page) or…

Cross-departmental initiatives

Topic Pages: CDC example Subtopics now comprise only a small portion of page

Top-Down Navigation: Taxonomies & portals Can a single taxonomy unify an enterprise site? First: can one be built at all? Software tools don’t 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: Build shallow, broad taxonomy that will answer “where will I find the information I need?” Rely on subsite taxonomies to answer “where in this area will I find the information I need?”

Can a single taxonomy unify an enterprise site?

First: can one be built at all?

Software tools don’t 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:

Build shallow, broad taxonomy that will answer “where will I find the information I need?”

Rely on subsite taxonomies to answer “where in this area will I find the information I need?”

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

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

Top-Down Navigation Roadmap Main page Site hierarchy Site map Site index Selective navigation

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 more topical approach Site index: Move from generalized to specialized around a single topic, or augment with frequent search queries/best bets work Guides: Start with a handful, then expand and rotate based on seasonality or other criteria of relevance

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 more topical approach

Site index: Move from generalized to specialized around a single topic, or augment with frequent search queries/best bets work

Guides: Start with a handful, then expand and rotate based on seasonality or other criteria of relevance

Bottom-Up Navigation

Bottom-Up Navigation Roadmap Content modeling Metadata development Metadata tagging

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

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

Content Modeling: The heart of bottom-up navigation Content models Used to convey meaning within select, high-value content areas Accommodate inter-connectedness Same as data or object modeling? Absolutely not! Many distinctions between data and semi-structured text Text makes up majority of enterprise sites

Content models

Used to convey meaning within select, high-value content areas

Accommodate inter-connectedness

Same as data or object modeling? Absolutely not!

Many distinctions between data and semi-structured text

Text makes up majority of enterprise sites

Content Modeling: The basics Based on patterns revealed during content inventory and analysis What makes up a content model? Content objects Metadata (attributes and values) Contextual links Applies to multiple levels of granularity Content objects Individual documents

Based on patterns revealed during content inventory and analysis

What makes up a content model?

Content objects

Metadata (attributes and values)

Contextual links

Applies to multiple levels of granularity

Content objects

Individual documents

Content Modeling: We’re already doing it at page level album page = title/artist/release + tracks + cover image

Content Modeling: Content analysis reveals patterns artist descriptions album reviews album pages artist bios

Content Modeling: Answer some questions What contextual navigation should exist between these content objects? (see Instone’s “Navigation Stress Test”-- http://user-experience.org/uefiles/navstress/ ) Are there missing content objects? Can we connect objects automatically? artist descriptions album reviews album pages artist bios

What contextual navigation should exist between these content objects? (see Instone’s “Navigation Stress Test”-- http://user-experience.org/uefiles/navstress/ )

Are there missing content objects?

Can we connect objects automatically?

Content Modeling: Fleshing out the model artist descriptions album reviews album pages artist bios discography concert calendar TV listings

Content Modeling: Connecting with metadata, rules artist description artist description artist description artist bio, discography, concert calendar, TV listing album review, artist description album page album review, discography, artist … link to other Content Objects… Artist Name , Channel, Date, Time… Artist Name , Tour, Venue, Date, Time… Artist Name , Individual Artist Name… Artist Name , Desc Author, Desc Date… Artist Name , Album Name , Release Date… Album Name , Artist Name , Review Author, Source, Pub Date… Album Name , Artist Name , Label, Release Date… … by leveraging common Metadata Attributes TV listing concert calendar artist bio artist description discography album review album page Content Objects…

Content Modeling: Problematic borders artist descriptions album reviews album pages artist bios discography concert calendar TV listings

Content Modeling: When to use Use only for high 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 outside formal content models

Use only for high 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 outside formal content models

Content Modeling: Steps for developing a model Determine key audiences (who’s using it?) Perform content inventory and analysis (what do we have?) Determine document and object types (what are the objects?) Determine metadata classes (what are the objects about?) Determine contextual linking rules (where do the objects lead us to next?)

Determine key audiences (who’s using it?)

Perform content inventory and analysis (what do we have?)

Determine document and object types (what are the objects?)

Determine metadata classes (what are the objects about?)

Determine contextual linking rules (where do the objects lead us to next?)

Content Modeling: Content object types 1/2 List known object types For each audience: Are there types that don’t fit? Examples: company executive bios, Q&A columns Venue reviews may be part of a separate content model

List known object types

For each audience:

Are there types that don’t fit?

Examples: company executive bios, Q&A columns

Venue reviews may be part of a separate content model

Content Modeling: Content object types 2/2 For each audience (continued): Gap analysis: are there types missing that users might expect? Examples: Gig reviews, Buy the CD, Links to music in the same genre Which types are most important to each audience? Fans of the band: Interviews with the band members Casual listener: Samples of the CD tracks

For each audience (continued):

Gap analysis: are there types missing that users might expect?

Examples: Gig reviews, Buy the CD, Links to music in the same genre

Which types are most important to each audience?

Fans of the band: Interviews with the band members

Casual listener: Samples of the CD tracks

Content Modeling: Metadata 1/2 Determine which objects would benefit from metadata Develop three types of metadata Descriptive Intrinsic Administrative

Determine which objects would benefit from metadata

Develop three types of metadata

Descriptive

Intrinsic

Administrative

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

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

Content Modeling: Contextual linking rules Are there specific objects for which these questions 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?

Are there specific objects for which these questions 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?

Content Modeling: Impacts on the enterprise Content models are a means for tying together content across business unit boundaries Content modeling is modular; over time, content models can be connected across the enterprise Major benefits to users who get beyond main page Can help justify CMS investments Not all content areas and owners are appropriate to work with

Content models are a means for tying together content across business unit boundaries

Content modeling is modular; over time, content models can be connected across the enterprise

Major benefits to users who get beyond main page

Can help justify CMS investments

Not all content areas and owners are appropriate to work with

Content Modeling: Putting it all together

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

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

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

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: Three types Intrinsic: metadata that an object holds about itself (e.g., file name or size) Descriptive: metadata that describes the object (e.g., subject, title, or audience) Administrative: metadata used to manage the object (e.g., time last saved, review date, owner)

Intrinsic: metadata that an object holds about itself (e.g., file name or size)

Descriptive: metadata that describes the object (e.g., subject, title, or audience)

Administrative: metadata used to manage the object (e.g., time last saved, review date, owner)

Metadata: Common sources Vocabularies from other parts of your organization (e.g., research library) Competitors Commercial sources (see www.taxonomywarehouse.com) Your site’s users Search analytics Folksonomies User studies (e.g., free listing, card sorting)

Vocabularies from other parts of your organization (e.g., research library)

Competitors

Commercial sources (see www.taxonomywarehouse.com)

Your site’s users

Search analytics

Folksonomies

User studies (e.g., free listing, card sorting)

Metadata: Value for the Enterprise 1/2 Search: cluster or filter the search by metadata, like title or keyword Browse: create topical indexes by aggregating pages with the same metadata Personalization and customization: show content to an employee based on their role or position in the company, e.g. engineer or manager

Search: cluster or filter the search by metadata, like title or keyword

Browse: create topical indexes by aggregating pages with the same metadata

Personalization and customization: show content to an employee based on their role or position in the company, e.g. engineer or manager

Metadata: Value for the Enterprise 2/2 Contextual linking: create relationships between individual or classes of content objects (e.g., cross-marketing on llbean.com) The purpose is to connect Content to content Users to content To provide value, metadata requires consistency (structural and semantic)

Contextual linking: create relationships between individual or classes of content objects (e.g., cross-marketing on llbean.com)

The purpose is to connect

Content to content

Users to content

To provide value, metadata requires consistency (structural and semantic)

Metadata: Enterprise big picture

Metadata: Scaling problems Barriers to enterprise metadata development: Volume of metadata vocabs./silos Complexity of semantic relationships (beyond synonyms)

Barriers to enterprise metadata development:

Volume of metadata vocabs./silos

Complexity of semantic relationships (beyond synonyms)

Metadata attributes: Easy to difficult 1/2 Although many standards exist (e.g., state abbreviations and postal codes), many enterprises (and their business units) use custom terms for regions (such as sales territories) Place names Moderate to Difficult Variations in formats (e.g., 12/31/07 versus 31/12/07) usually can be addressed by software Chronology Easy to Moderate These are typically already available and standardized Business unit names Easy Comments Metadata Attribute Level of Difficulty

Metadata attributes: Easy to difficult 2/2 The most ambiguous type of metadata; difficult for individuals, much less business units, to come to agreement on topical metadata Topics Difficult Audiences, such as customers or types of employees, vary widely from unit to unit Audiences Difficult 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 Product names Moderate to Difficult Comments Metadata Attribute Level of Difficulty

Metadata: Structural consistency Standard formats and approaches enable interoperability, which enables sharing of metadata. Examples RDF (Resource Description Format) Topic Maps Dublin Core OAI (Open Archives Initiative) Sources Academia/scholarly publishing world Little from data management world

Standard formats and approaches enable interoperability, which enables sharing of metadata.

Examples

RDF (Resource Description Format)

Topic Maps

Dublin Core

OAI (Open Archives Initiative)

Sources

Academia/scholarly publishing world

Little from data management world

Metadata: RDF (Resource Description Format) A syntax for expressing semantic relationships Basic components Resource Property type From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html 3. Value 4. Property 1 3 2 4

A syntax for expressing semantic relationships

Basic components

Resource

Property type

3. Value

4. Property

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 Topic Maps, Steve Pepper (http://www.ontopia.net/topicmaps/materials/tao.html ) topics occurrences associations

Potential syntax for content modeling, semantic webs

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 in enterprise environment Attribute review can be useful as an enterprise-wide exercise

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 in enterprise environment

Attribute review can be useful as an enterprise-wide exercise

Metadata: Dublin Core elements 1/2 Title: A name given to the resource Creator: An entity primarily responsible for making the content of the resource Subject: A topic of the content of the resource Description: An account of the content of the resource Publisher: An entity responsible for making the resource available Contributor: An entity responsible for making contributions to the content of the resource Date: A date of an event in the lifecycle of the resource

Title: A name given to the resource

Creator: An entity primarily responsible for making the content of the resource

Subject: A topic of the content of the resource

Description: An account of the content of the resource

Publisher: An entity responsible for making the resource available

Contributor: An entity responsible for making contributions to the content of the resource

Date: A date of an event in the lifecycle of the resource

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 context Source: 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

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 context

Source: 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

Metadata: Dublin Core in HTML Dublin Core elements identified with “DC” prefix From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html

Dublin Core elements identified with “DC” prefix

Metadata: Dublin Core and RDF Syntax and schema combination is useful But where are the metadata values? From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html

Syntax and schema combination is useful

But where are the metadata values?

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 between service and data providers via HTTP; replies (results) encoded in XML Metadata harvesting Enables improved searching across compliant distributed repositories Does not address semantic merging of metadata (i.e., vocabulary control)

OAI: Open Archives Initiative

Comes from academic publishing world

Provides means for central registration of “confederate repositories”

Repositories use Dublin Core; requests between service and data providers via HTTP; replies (results) encoded in XML

Metadata harvesting

Enables improved searching across compliant distributed repositories

Does not address semantic merging of metadata (i.e., vocabulary control)

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)

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)

Metadata: Semantic consistency 2/2 Control vocabulary…so your users don’t have to!

Control vocabulary…so your users don’t have to!

Metadata: Semantic relationships Three types Equivalence: Variant terms with same meaning (e.g., abbreviations and synonyms) Hierarchical: Broader term, narrower term relationships Associative: Related terms that are related to each other

Three types

Equivalence: Variant terms with same meaning (e.g., abbreviations and synonyms)

Hierarchical: Broader term, narrower term relationships

Associative: Related terms that are related to each other

Metadata: Levels of control

Metadata semantic relationships: Hard to hardest Thesauri Associative Hardest Classification schemes Hierarchical Harder Synonym rings and authority lists Synonymous Hard Examples Type of Relationship Level of Difficulty

Metadata: Synonym rings Used in many search engines to expand the number of results Words that are similar to each other are linked together Example 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)

Used in many search engines to expand the number of results

Words that are similar to each other are linked together

Example 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)

Metadata: Authority files Pick list of the authorized words 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

Pick list of the authorized words 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

Metadata: Classification schemes Classification Systematic arrangement of knowledge, usually hierarchical Placement of objects into a scheme which makes sense to the user and relates them to other objects Two types of classification schemes Enumerative classification: hierarchical organization into which objects are placed Faceted classification: organization by facets or attributes that describe the object

Classification

Systematic arrangement of knowledge, usually hierarchical

Placement of objects into a scheme which makes sense to the user and relates them to other objects

Two types of classification schemes

Enumerative classification: hierarchical organization into which objects are placed

Faceted classification: organization by facets or attributes that describe the object

Metadata: Enumerative classification Really good to classify small numbers of objects or objects that can live in only one place Provides good browsing structure Can be polyhierarchical, where objects live in many places Best known: the taxonomy of life, Dewey Decimal Classification, Library of Congress Classification Most familiar on the Web: Yahoo!, Open Directory

Really good to classify small numbers of objects or objects that can live in only one place

Provides good browsing structure

Can be polyhierarchical, where objects live in many places

Best known: the taxonomy of life, Dewey Decimal Classification, Library of Congress Classification

Most familiar on the Web: Yahoo!, Open Directory

Metadata: Enumerative classification example

Metadata: Faceted classification 1/2 Describes the object with numerous facets or attributes Each facet could have a separate controlled vocabulary of its own Can mix and match the facets to create a browsing structure Easier to manage the controlled vocabularies

Describes the object with numerous facets or attributes

Each facet could have a separate controlled vocabulary of its own

Can mix and match the facets to create a browsing structure

Easier to manage the controlled vocabularies

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 can make indexing hard to do Browsing facets may not be as clear as browsing a hierarchy; many paths to the same object

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 can make indexing hard to do

Browsing facets may not be as clear as browsing a hierarchy; many paths to the same object

Metadata: Faceted classification example

Metadata: Faceted classification example

Metadata: What is a thesaurus? Traditional use Dictionary of synonyms (Roget’s) From one word to many words Information retrieval context A controlled vocabulary in which equivalence, hierarchical, and associative relationships are identified for purposes of improved retrieval From many words to one word

Traditional use

Dictionary of synonyms (Roget’s)

From one word to many words

Information retrieval context

A controlled vocabulary in which equivalence, hierarchical, and associative relationships are identified for purposes of improved retrieval

From many words to one word

Metadata: Thesaurus entry example

Enterprise Metadata: Challenges Two barriers to enterprise metadata Interoperability (structural) 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

Two barriers to enterprise metadata

Interoperability (structural)

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

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 Bet records)

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 Bet records)

Enterprise Metadata: Merging vocabularies Extremely difficult, and currently rare Mostly found in libraries, academia, scholarly publishing, and other resource-poor environments Examples, hard to hardest Cross-walking vocabularies Switching vocabularies Meta-thesaurus Single thesaurus

Extremely difficult, and currently rare

Mostly found in libraries, academia, scholarly publishing, and other resource-poor environments

Examples, hard to hardest

Cross-walking vocabularies

Switching vocabularies

Meta-thesaurus

Single thesaurus

Merging Vocabularies : Vocabulary cross-walking Map terms peer-to-peer between individual vocabularies Primarily handles synonyms, not relationships Can be handled manually or through automated means (pattern-matching) Doesn’t scale well beyond two or three vocabularies

Map terms peer-to-peer between individual vocabularies

Primarily handles synonyms, not relationships

Can be handled manually or through automated means (pattern-matching)

Doesn’t scale well beyond two or three vocabularies

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 are more than two or three vocabularies to connect

A single vocabulary that maps to existing vocabularies (primarily synonyms)

Similar to cross-walking, but better at handling translation when there are more than two or three vocabularies to connect

Merging Vocabularies : Meta-thesaurus A switching vocabulary which also includes thesaural relationships (essentially a thesaurus of thesauri) Example: National Library of Medicine’s UMLS (Unified Medical Language System) Merges over 100 vocabularies Describes fairly homogeneous domain (medical literature) for fairly homogeneous audience (health science professionals)

A switching vocabulary which also includes thesaural relationships (essentially a thesaurus of thesauri)

Example: National Library of Medicine’s UMLS (Unified Medical Language System)

Merges over 100 vocabularies

Describes fairly homogeneous domain (medical literature) for fairly homogeneous audience (health science professionals)

Merging Vocabularies : Single unified thesaurus Highly impractical in enterprise context

Highly impractical in enterprise context

Enterprise Metadata: Impacts on the enterprise 1/2 Requires coordinated strategy 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

Requires coordinated strategy 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: Impacts on the enterprise 2/2 “ Serious metadata” is beyond the means of most enterprises Encourage local (e.g., departmental) vocabulary development Provides organizational learning and local benefit Enterprise-wide, start with “easier” vocabularies; work your way to harder ones over time; suggested sequence: Business functions Products Topics

“ Serious metadata” is beyond the means of most enterprises

Encourage local (e.g., departmental) vocabulary development

Provides organizational learning and local benefit

Enterprise-wide, start with “easier” vocabularies; work your way to harder ones over time; suggested sequence:

Business functions

Products

Topics

Bottom-Up Navigation Roadmap Content modeling Metadata development Metadata tagging

Bottom-Up Navigation Takeaways 1/3 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

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 2/3 Metadata development Distinguish attributes (and structural interoperability) from values (and semantic merging) Costs and value both increase as these increase: Complexity of relationships between terms (equivalence=>hierarchical=>associative) Level of control (synonym rings=>authority files=>classification schemes=>thesauri) Think small: facets instead of a single taxonomy

Metadata development

Distinguish attributes (and structural interoperability) from values (and semantic merging)

Costs and value both increase as these increase:

Complexity of relationships between terms (equivalence=>hierarchical=>associative)

Level of control (synonym rings=>authority files=>classification schemes=>thesauri)

Think small: facets instead of a single taxonomy

Bottom-Up Navigation Takeaways 3/3 Metadata tagging Make choices based on actual needs (e.g., content models) rather than exhaustive indexing Consider costs of application and upkeep Need for professional expertise Metadata is a moving target that matches other moving targets (users and content)

Metadata tagging

Make choices based on actual needs (e.g., content models) rather than exhaustive indexing

Consider costs of application and upkeep

Need for professional expertise

Metadata is a moving target that matches other moving targets (users and content)

EIA and Search

EIA and Search Search systems are a natural enterprise IA tool Automated Crawls what you tell it to Doesn’t care about politics Problems with shrink-wrapped search tools Default settings, IT ownership minimize customization to fit the enterprise’s needs Results often not relevant, poorly presented Customization is the answer Within the realm of your team’s abilities … and if IT will allow it!

Search systems are a natural enterprise IA tool

Automated

Crawls what you tell it to

Doesn’t care about politics

Problems with shrink-wrapped search tools

Default settings, IT ownership minimize customization to fit the enterprise’s needs

Results often not relevant, poorly presented

Customization is the answer

Within the realm of your team’s abilities

… and if IT will allow it!

EIA and Search: Visually

Enterprise Search Design: Potential improvements Our focus: Clear interface Enhanced queries Improved results (relevance & presentation) Basic search system components

Our focus:

Clear interface

Enhanced queries

Improved results (relevance & presentation)

Enterprise Search Roadmap Search queries Search interface Search results

Search Interface Design: The “Box” The “Box” unifies IBM.com Consistent: Placement Design Labeling Functionality

The “Box” unifies IBM.com

Consistent:

Placement

Design

Labeling

Functionality

Search Interface Design: Combine interfaces when possible Two boxes bad, one box good, usually… Will users understand?

Two boxes bad, one box good, usually…

Search Interface Design: The role of “advanced search” 1/2 Continued… Not a likely starting point for users who are searching

Search Interface Design: The role of “advanced search” 2/2 Suggestions Use for specialized interfaces Reposition as “Revise Search” Don’t bother

Suggestions

Use for specialized interfaces

Reposition as “Revise Search”

Don’t bother

Contextualizing Search Help: Ebay example

Search Interface and Queries: Functionality and visibility Hide functionality? Consider the “Google Effect,” human nature and the LCD Don’t 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)

Hide functionality? Consider the “Google Effect,” human nature and the LCD

Don’t 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: Query language considerations Natural language Usually don’t 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 than “OR” 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” )

Natural language

Usually don’t 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 than “OR”

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: Query building considerations Large potential benefits to improving “intelligence” 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

Large potential benefits to improving “intelligence” 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: Sur La Table example A la Google…

A la Google…

Stemming: IBM example IBM uses Fast Search

Concept Searching: Social Security Admin. example SSA uses Convera

SSA uses

Convera

Thesaural Search: ERIC example

Enterprise Search Interface: Guidelines Hide functionality on initial enterprise-wide search Cast the net widely: rely on query builders to generate larger, higher quality result sets Use filtering/clustering to narrow Use Best Bets to ensure strong initial results

Hide functionality on initial enterprise-wide search

Cast the net widely: rely on query builders to generate larger, higher quality result sets

Use filtering/clustering to narrow

Use Best Bets to ensure strong initial results

Individual Search Results: Goals Enable users to quickly understand something about each document represented That “something”: confirm that a known-item has been found, or distinguish from other results Align to searching behaviors (determined through user testing, persona/scenario analysis, local site search analytics) Known-item Open-ended/exploratory Comprehensive research

Enable users to quickly understand something about each document represented

That “something”: confirm that a known-item has been found, or distinguish from other results

Align to searching behaviors (determined through user testing, persona/scenario analysis, local site search analytics)

Known-item

Open-ended/exploratory

Comprehensive research

Individual Search Results: Approaches Basic approaches Document titling Displaying appropriate elements for each result These approaches have value in any context, but especially useful in enterprise setting

Basic approaches

Document titling

Displaying appropriate elements for each result

These approaches have value in any context, but especially useful in enterprise setting

Document Titling: DaimlerChrysler example What do these document titles tell you? And what do they tell you about DaimlerChrysler?

What do these document titles tell you?

And what do they tell you about DaimlerChrysler?

Document Titling: Ford example Descriptive document titles provide clear value … but rely upon highly centralized authoring procedures and style guide

Descriptive document titles provide clear value

Displaying Appropriate Elements: 1) Determine common elements Develop table of available elements (including metadata) for disparate documents and records Comes after content inventory and analysis Develop 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)

Develop table of available elements (including metadata) for disparate documents and records

Comes after content inventory and analysis

Develop 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) Select appropriate elements Choose common elements which match most common searching 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

Choose common elements which match most common searching 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 elements Step #2: select elements to display N Y N N Y FAQ N Y N Y Y Product Sheet Y Y Y N Y Policy Y Y Y Y Y Tech. Report Date Topic Creator Desc. Title Step #1 Y Y N Y Y Open-Ended Y N Y N Y Known-Item Date Topic Creator Desc. Title Step #2

Individual Search Results: Columbia University example Long display for open-ended searchers… … shorter display for known-item searchers

Long display for open-ended searchers…

Individual Search Results: What happens next? Augment with “next step” actions per result Open in separate window Get more like this Print Save Email Determine next steps through contextual inquiry

Augment with “next step” actions per result

Open in separate window

Get more like this

Print

Save

Email

Determine next steps through contextual inquiry

Presenting Search Result Groups: Ranked results Difficulties with relevance ranking Depends on consistent elements across documents Term frequency-dependent approaches create an “apples and oranges effect” on ranking Google effect: benefits of popularity make less sense in enterprise context than in open web Consider alternatives Clustering and filtering Manually-derived results (aka “Best Bets”)

Difficulties with relevance ranking

Depends on consistent elements across documents

Term frequency-dependent approaches create an “apples and oranges effect” on ranking

Google effect: benefits of popularity make less sense in enterprise context than in open web

Consider alternatives

Clustering and filtering

Manually-derived results (aka “Best Bets”)

Presenting Search Result Groups: 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 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? last update?) Security setting (via login, cookies) Use explicit metadata (harder) Language Product Audience Subject/topic

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? last update?)

Security setting (via login, cookies)

Use explicit metadata (harder)

Language

Product

Audience

Subject/topic

Clustering by Topic: LL Bean example Category matches displayed rather than individual results

Filtering by Source: BBC example Selecting a tab filters results

Clustering by Content Type: c|net example Mention content modeling Results clustered in multiple content types

Mention content modeling

Clustering by Language Example: PeopleSoft Netherlands Result clusters for Dutch and English

The Zipf Curve: Consistent and telling From http://netfact.com/rww/write/searcher/rww-searcher-msukeywords-searchdist-apr-jul2002.gif Zipf distribution from Michigan State University search logs (derived from local site search analytics)

Common Queries: What they tell us

“Best Bets”: By popular demand Recommended links Ensure useful results for top X (50? 100?) most popular search queries Useful resources for each popular query are manually determined (guided by documented logic) Useful resources manually linked to popular queries; automatically displayed in result page

Recommended links

Ensure useful results for top X (50? 100?) most popular search queries

Useful resources for each popular query are manually determined (guided by documented logic)

Useful resources manually linked to popular queries; automatically displayed in result page

“Best Bets” Example: BBC Logic for BBC Best Bets Is query a country name? (yes) Then do we have a country profile? (yes) Then do we have a language service? (yes)

Logic for BBC Best Bets

Is query a country name? (yes)

Then do we have a country profile? (yes)

Then do we have a language service? (yes)

“Best Bets”: In the enterprise context Who does the work? Difficult to “assign” queries to different business units (e.g., “computing” means different things to different business units) Can serve as impetus for centralized effort Operational requirements Logic based on users’ needs (e.g., queries) and business rules Policy that 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 for researchers, another for IT staff)

Who does the work?

Difficult to “assign” queries to different business units (e.g., “computing” means different things to different business units)

Can serve as impetus for centralized effort

Operational requirements

Logic based on users’ needs (e.g., queries) and business rules

Policy that 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 for researchers, another for IT staff)

Enterprise Search: 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 local site search analytics Editorial (e.g., Best Bets creation)

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 local site search analytics

Editorial (e.g., Best Bets creation)

Search Tool Selection: EIA needs 1/2 To basic evaluation criteria (from SearchTools.com)… Price Platform Capacity Ease of installation Maintenance

To basic evaluation criteria (from SearchTools.com)…

Price

Platform

Capacity

Ease of installation

Maintenance

Search Tool Selection: 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 multiple search engines/data sources)

… 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 multiple search engines/data sources)

Enterprise Search Roadmap Search queries Search interface Search results

Enterprise Search Takeaways 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

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

EIA Research Methods

EIA Research Methods: Learn about these three areas Content, users and context drive: IA research IA design IA staffing IA education … and everything else

Content, users and context drive:

IA research

IA design

IA staffing

IA education

… and everything else

EIA Research Methods: Sampling challenges How do you achieve representative samples in the face of these difficulties? Awareness: Who and what are out there? Volume: How much is there? Can we cover it all? Costs: Can we afford to investigate at this order of magnitude? Politics: Who will work with us? And who will try to get in the way?

How do you achieve representative samples in the face of these difficulties?

Awareness: Who and what are out there?

Volume: How much is there? Can we cover it all?

Costs: Can we afford to investigate at this order of magnitude?

Politics: Who will work with us? And who will try to get in the way?

EIA Research Methods: Reliance on alternative techniques Standard techniques may not work in enterprise settings Alternatives often incorporate traditional methods and new technologies Web-based surveys (e.g., SurveyMonkey) Remote contextual inquiry and task analysis (via WebEx) Web-based “card” sorting (e.g., EZsort) Auto-categorization, auto-classification tools (e.g., Semio) Log analysis tools (e.g., WebTrends)

Standard techniques may not work in enterprise settings

Alternatives often incorporate traditional methods and new technologies

Web-based surveys (e.g., SurveyMonkey)

Remote contextual inquiry and task analysis (via WebEx)

Web-based “card” sorting (e.g., EZsort)

Auto-categorization, auto-classification tools (e.g., Semio)

Log analysis tools (e.g., WebTrends)

EIA Research Methods: 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 Local site search analytics

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

Local site search analytics

Content Inventory: 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

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 EIA requires balanced, iterative sampling (where CMS implementation 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

EIA requires balanced, iterative sampling (where CMS implementation 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

Content Migration Strategy: Value Tier Approach Determine value tiers of content quality that make sense given your users/content/context Answer “what content is important to the enterprise?” Help determine what to add, maintain, delete How to do it? Prioritize and weight quality criteria Rate content areas Cluster into tiers Score content areas while performing content analysis

Determine value tiers of content quality that make sense given your users/content/context

Answer “what content is important to the enterprise?”

Help determine what to add, maintain, delete

How to do it?

Prioritize and weight quality criteria

Rate content areas

Cluster into tiers

Score content areas while performing content analysis

Value Tier Approach: Potential quality criteria Select appropriate criteria for your business context, users, and content Authority Strategic value Currency Usability Popularity/usage Feasibility (i.e., “enlightened” content owners) Presence of quality existing metadata

Select appropriate criteria for your business 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

Value Tier Approach: P rioritization

Assessing Stakeholders: What to learn from them 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

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 Org chart: business unit representatives Will provide strategic overview 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 reps Functional/audience-centered Subject Matter Experts (SMEs): represent power users; valuable for pointing out content that addresses major information needs Audience advocates (e.g., switchboard operators): can describe content with high volume usage

Org chart: business unit representatives

Will provide strategic overview 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 reps

Functional/audience-centered

Subject Matter Experts (SMEs): represent power users; valuable for pointing out content that addresses major information needs

Audience advocates (e.g., switchboard operators): can describe content with high volume usage

Stakeholder Interviews: Finding the low-hanging fruit Assessment should reveal degree of “enlightenment” Early adopters Successful track records visible within the enterprise Understand/have experience with enterprise-wide initiatives Willingness to benefit the enterprise as a whole They just plain “get it” You’ve got to play to win: lack of interest and availability mean loss of influence

Assessment should reveal degree of “enlightenment”

Early adopters

Successful track records visible within the enterprise

Understand/have experience with enterprise-wide initiatives

Willingness to benefit the enterprise as a whole

They just plain “get it”

You’ve got to play to win: lack of interest and availability mean loss of influence

Stakeholder Interviews: Indicators of enlightenment Technology assessment: who has/uses the “classic 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

Technology assessment: who has/uses the “classic 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 … … … … C+ F C Passes “navigation stress test” B B+ A Supports comprehensive research C C B Supports associative learning C C+ A Supports known-item searching B B B- Supports orientation Dept. 3 Dept. 2 Dept. 1 Information Architecture Heuristic

“Safe” User Sampling: The 2D Scorecard Combines alternative, apolitical methods for 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., task analysis, card sorting)

Combines alternative, apolitical methods for 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., task analysis, card sorting)

The 2D Scorecard: Role-based segmentation Roles cut across political boundaries Profile core enterprise-wide business functions Why does the enterprise exist? Examples: Sell products, B2B or B2C activities, manufacture products, inform opinion, etc. Determine major “actors” in each process

Roles cut across political boundaries

Profile core enterprise-wide business functions

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 Standard, familiar measure; also cuts across political boundaries Gender Geography Age Income level Education level Your marketing department probably has this data already

Standard, familiar measure; also cuts across political boundaries

Gender

Geography

Age

Income level

Education level

Your marketing department probably has this data already

The 2D Scorecard: Combining roles & demographics 32 4 10 12 6 TOTAL 7 0 4 3 0 Role 4 10 1 2 4 3 Role 3 6 1 1 2 2 Role 2 9 2 3 3 1 Role 1 TOTAL Demo. Profile D Demo. Profile C Demo. Profile B Demo. Profile A TEST SAMPLE SIZE

The 2D Scorecard: Incorporating contextual bias Role/demographic “scorecard” is pure Serves as a structure that doesn’t have to change substantially But how to incorporate stakeholder bias? Stakeholder bias can be accommodated Poll/interview stakeholders to determine how cell values should change Axes and totals stay mostly the same Distraction is our friend

Role/demographic “scorecard” is pure

Serves as a structure that doesn’t have to change substantially

But how to incorporate stakeholder bias?

Stakeholder bias can be accommodated

Poll/interview stakeholders to determine how cell values should change

Axes and totals stay mostly the same

Distraction is our friend

The 2D Scorecard: After stakeholder input 32 4 13 10 5 TOTAL 7 1 3 3 0 Role 4 10 1 2 4 3 Role 3 6 1 3 1 1 Role 2 9 1 5 2 1 Role 1 TOTAL Demo. Profile D Demo. Profile C Demo. Profile B Demo. Profile A TEST SAMPLE SIZE

Maintaining a User Pool: Build your own for fun and power Through automated surveys, lower level information architect built an enterprise-wide pool of 1,500 users Prescreened by demographics and skills Provided him with substantial leverage with others who wanted access to users He just got there first and did the obvious More information: http://louisrosenfeld.com/home/bloug_archive/000408.html

Through automated surveys, lower level information architect built an enterprise-wide pool of 1,500 users

Prescreened by demographics and skills

Provided him with substantial leverage with others who wanted access to users

He just got there first and did the obvious

More information: http://louisrosenfeld.com/home/bloug_archive/000408.html

Metadata Development: Conventional techniques Techniques Open card-sorting to gather terms Closed card-sorting to validate terms Can be difficult to carry out in enterprise environment (scope of vocabulary, subject sampling) Modifications for enterprise setting Use remote tools (e.g. IBM’s EZsort) Apply in “stepped” mode: test subsections of taxonomy separately Drawback: lack of physical cards may diminish value of data

Techniques

Open card-sorting to gather terms

Closed card-sorting to validate terms

Can be difficult to carry out in enterprise environment (scope of vocabulary, subject sampling)

Modifications for enterprise setting

Use remote tools (e.g. IBM’s EZsort)

Apply in “stepped” mode: test subsections of taxonomy separately

Drawback: lack of physical cards may diminish value of data

Metadata Development: Classification scheme analysis Review existing schemes, looking for: Duplication of domain Overlapping domains Consistency or lack thereof Can some vocabularies be reused? Improved? Eliminated?

Review existing schemes, looking for:

Duplication of domain

Overlapping domains

Consistency or lack thereof

Can some vocabularies be reused? Improved? Eliminated?

Automated Metadata Development: Two classes of tools Auto-categorization tools Can leverage pattern-matching and cluster-analysis algorithms to automatically generate categories (e.g., Autonomy, Interwoven) Can also use rules (i.e., concepts) to generate categories (e.g., Inktomi, Verity, Entrieva/Semio) Auto-classification tools Apply indexing to existing categories Require controlled vocabularies (generally manually-created) to index content

Auto-categorization tools

Can leverage pattern-matching and cluster-analysis algorithms to automatically generate categories (e.g., Autonomy, Interwoven)

Can also use rules (i.e., concepts) to generate categories (e.g., Inktomi, Verity, Entrieva/Semio)

Auto-classification tools

Apply indexing to existing categories

Require controlled vocabularies (generally manually-created) to index content

Automated Metadata Development: Pros and cons Benefits Apolitical applications that disregard org chart May be a necessary evil in a large enterprise environment Drawbacks Limited value in heterogeneous, multi-domain environment Perform better with rich text, not so good with database records and other brief documents

Benefits

Apolitical applications that disregard org chart

May be a necessary evil in a large enterprise environment

Drawbacks

Limited value in heterogeneous, multi-domain environment

Perform better with rich text, not so good with database records and other brief documents

Automated Metadata Development: Semio example At best, an 80% solution; none truly “automated” Significant manual proofing of the 80% of content indexed Significant manual indexing of the 20% not indexed “ E-commerce”: A human would collapse many of these categories

At best, an 80% solution; none truly “automated”

Significant manual proofing of the 80% of content indexed

Significant manual indexing of the 20% not indexed

Finding Metadata: Free listing Simple technique: “ List all of the terms you associate with ______” Perform pair analysis (co-occurrence) on results Benefits Harvests terms associated with a concept or domain Can be done in survey form with many subjects, multiple audiences Supports card sorting Less useful for structuring relationships between terms Possible alternative to local site search analytics

Simple technique:

“ List all of the terms you associate with ______”

Perform pair analysis (co-occurrence) on results

Benefits

Harvests terms associated with a concept or domain

Can be done in survey form with many subjects, multiple audiences

Supports card sorting

Less useful for structuring relationships between terms

Possible alternative to local site search analytics

Local Site Search Analytics: What does this data tell us? Keywords: focis; 0; 11/26/01 12:57 PM; XXX.XXX.XXX.2 Keywords: focus; 167; 11/26/01 12:59 PM; XXX.XXX.XXX.2 Keywords: focus pricing; 12; 11/26/01 1:02 PM; XXX.XXX.XXX.2 Keywords: discounts for college students; 0; 11/26/01 3:35 PM; XXX.XXX.XXX.59 Keywords: student discounts; 3; 11/26/01 3:35 PM; XXX.XXX.XXX.59 Keywords: ford or mercury; 500; 11/26/01 3:35 PM; XXX.XXX.XXX.126 Keywords: (ford or mercury) and dealers; 73; 11/26/01 3:36 PM; XXX.XXX.XXX.126 Keywords: lorry; 0; 11/26/01 3:36 PM; XXX.XXX.XXX.36 Keywords: “safety ratings”; 3; 11/26/01 3:36 PM; XXX.XXX.XXX.55 Keywords: safety; 389; 11/26/01 3:36 PM; XXX.XXX.XXX.55 Keywords: seatbelts; 2; 11/26/01 3:37 PM; XXX.XXX.XXX.55 Keywords: seat belts; 33; 11/26/01 3:37 PM; XXX.XXX.XXX.55

Local Site Search Analytics: Instructions Sort and count queries Identify and group similar queries (e.g., “cell phones” and “mobile phones”) Understand users’ query syntax (e.g., use of single or multiple terms, Boolean operators) and semantics (e.g., use of lay or professional terms) Determine most common queries Identify content gaps through 0 result queries Build “Best Bets” for common queries Map common queries to audiences through IP or login analysis

Sort and count queries

Identify and group similar queries (e.g., “cell phones” and “mobile phones”)

Understand users’ query syntax (e.g., use of single or multiple terms, Boolean operators) and semantics (e.g., use of lay or professional terms)

Determine most common queries

Identify content gaps through 0 result queries

Build “Best Bets” for common queries

Map common queries to audiences through IP or login analysis

Local Site Search Analytics: Benefits for i nterface development Identifies “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added (e.g., revise search, browsing alternative) Syntax of queries informs selection of search features to expose (e.g., use of Boolean operators, fielded searching) … OR…

Identifies “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added (e.g., revise search, browsing alternative)

Syntax of queries informs selection of search features to expose (e.g., use of Boolean operators, fielded searching)

Local Site Search Analytics: Benefits for metadata development Provides a source of terms for the creation of vocabularies Provides a sense of how needs are expressed Jargon (e.g., “lorry” vs. “truck”) Syntax (e.g., Boolean, natural language, keyword) Informs decisions on which vocabularies to develop/implement (e.g., thesaurus, spell-checker)

Provides a source of terms for the creation of vocabularies

Provides a sense of how needs are expressed

Jargon (e.g., “lorry” vs. “truck”)

Syntax (e.g., Boolean, natural language, keyword)

Informs decisions on which vocabularies to develop/implement (e.g., thesaurus, spell-checker)

Local Site Search Analytics: Benefits for content analysis Identifies content that can’t be found Identifies content gaps Creation of “Best Bets” to address common queries

Identifies content that can’t be found

Identifies content gaps

Creation of “Best Bets” to address common queries

Local Site Search Analytics: Pros and cons Benefits Data is real, comprehensive, available (usually) High volume Can track sessions Non-intrusive Drawbacks Lack of good commercial analysis tools Lack of standards makes it difficult to merge multiple search logs (not to mention server logs) More difficult to merge with other logs (e.g. server) Doesn’t tell you why users did what they did

Benefits

Data is real, comprehensive, available (usually)

High volume

Can track sessions

Non-intrusive

Drawbacks

Lack of good commercial analysis tools

Lack of standards makes it difficult to merge multiple search logs (not to mention server logs)

More difficult to merge with other logs (e.g. server)

Doesn’t tell you why users did what they did

Local Site Search Analytics: Enterprise context Makes case for EIA; usually demonstrates that users are requesting things that aren’t tied to departmental divisions (e.g., policies, products) Informs “Best Bets” Informs synonym creation Limited value if not analyzing merged logs

Makes case for EIA; usually demonstrates that users are requesting things that aren’t tied to departmental divisions (e.g., policies, products)

Informs “Best Bets”

Informs synonym creation

Limited value if not analyzing merged logs

EIA Research Methods Takeaways Challenges Many traditional methods can be adapted to the enterprise environment But sampling, geography, volume and politics force a less scientific, more pragmatic approach Also force greater reliance on automated tools We need new methods Focus on minimizing politics and geographic distribution Most are untested Information architects need to be willing to experiment, innovate, and live with mistakes

Challenges

Many traditional methods can be adapted to the enterprise environment

But sampling, geography, volume and politics force a less scientific, more pragmatic approach

Also force greater reliance on automated tools

We need new methods

Focus on minimizing politics and geographic distribution

Most are untested

Information architects need to be willing to experiment, innovate, and live with mistakes

EIA Framework

EIA and the Enterprise: Phased, modular model Phasing is not just about roll-out and timing Should be overarching philosophy for EIA initiatives We can phase in whom we work with We can phase in whom we hire to do EIA work We can modularize what types of EIA we do We can phase in what degree of centralization we can support

Phasing is not just about roll-out and timing

Should be overarching philosophy for EIA initiatives

We can phase in whom we work with

We can phase in whom we hire to do EIA work

We can modularize what types of EIA we do

We can phase in what degree of centralization we can support

Why a Phased Model? Because mandates don’t work “ Just do it!”… … all (e.g., all subsites) … now (e.g., in 3-6 months) … with few resources and people (e.g., one sad webmaster) … in a way that minimizes organizational learning (e.g., hire an outside consultant or agency) Results of the mandated “solution”: completely cosmetic, top-down information architecture

“ Just do it!”…

… all (e.g., all subsites)

… now (e.g., in 3-6 months)

… with few resources and people (e.g., one sad webmaster)

… in a way that minimizes organizational learning (e.g., hire an outside consultant or agency)

Results of the mandated “solution”: completely cosmetic, top-down information architecture

The EIA Framework Seven issues EIA governance: how the work and staff are structured EIA services: how work gets done in an enterprise environment EIA staffing: who handles strategic and tactical efforts EIA funding model: how it gets paid for EIA marketing and communications: how it gets adopted by the enterprise EIA workflow: how it gets maintained EIA design and timing: what gets created and when

EIA governance: how the work and staff are structured

EIA services: how work gets done in an enterprise environment

EIA staffing: who handles strategic and tactical efforts

EIA funding model: how it gets paid for

EIA marketing and communications: how it gets adopted by the enterprise

EIA workflow: how it gets maintained

EIA design and timing: what gets created and when

The EIA Framework Critical goals Re-balance the enterprise’s in-house IA expertise to support an appropriate degree of centralization Enable slow, scaleable, sustainable growth of internal EIA expertise Create ownership/maintenance mechanism for enterprise-wide aspects of IA (currently orphaned) Ensure institutional knowledge is retained

Re-balance the enterprise’s in-house IA expertise to support an appropriate degree of centralization

Enable slow, scaleable, sustainable growth of internal EIA expertise

Create ownership/maintenance mechanism for enterprise-wide aspects of IA (currently orphaned)

Ensure institutional knowledge is retained

EIA Governance: Questions What sort of individuals or group should be responsible for the EIA? Where should they be located within the organization? How should they address strategic issues? Tactical issues? Can they get their work done with carrots, sticks, or both as they try to work with somewhat autonomous business units?

What sort of individuals or group should be responsible for the EIA?

Where should they be located within the organization? How should they address strategic issues? Tactical issues?

Can they get their work done with carrots, sticks, or both as they try to work with somewhat autonomous business units?

Logical outgrowth of Web or portal team Design or branding group E-services, e-business or e-commerce unit Goals Ensure that IA is primary goal of the unit Retain organizational learning Avoid political baggage Maintain independence EIA Governance: A separate business unit 1/2

Logical outgrowth of

Web or portal team

Design or branding group

E-services, e-business or e-commerce unit

Goals

Ensure that IA is primary goal of the unit

Retain organizational learning

Avoid political baggage

Maintain independence

Ambitious, fool-hardy, unrealistic? Necessary! Models of successful new organizational efforts often start as separate entities Alternatives (none especially attractive) Be a part of IT or information services Be a part of marketing and communications Be a part of each business unit EIA Governance: A separate business unit 2/2

Ambitious, fool-hardy, unrealistic? Necessary!

Models of successful new organizational efforts often start as separate entities

Alternatives (none especially attractive)

Be a part of IT or information services

Be a part of marketing and communications

Be a part of each business unit

EIA Governance: Balancing strategic and tactical Strategic: Model on Board of Directors Represent key constituencies Track record with successes, mistakes with organization’s prior centralization efforts Mix of visionaries, people who understand money Tactical: Start with staff who “do stuff” Extend as necessary by outsourcing Enables logical planning of hiring and use of consultants and contractors

Strategic: Model on Board of Directors

Represent key constituencies

Track record with successes, mistakes with organization’s prior centralization efforts

Mix of visionaries, people who understand money

Tactical: Start with staff who “do stuff”

Extend as necessary by outsourcing

Enables logical planning of hiring and use of consultants and contractors

EIA Governance : Board of directors 1/2 Goals Understand the strategic role of information architecture within the enterprise Promote information architecture services as a permanent part of the enterprise ’ s infrastructure Align the group and its services with those goals Ensure the group ’ s financial and political viability Help develop the group ’ s policies Support the group ’ s management Makeup Draw first from effective leaders Then from major units that would be strategic partners

Goals

Understand the strategic role of information architecture within the enterprise

Promote information architecture services as a permanent part of the enterprise ’ s infrastructure

Align the group and its services with those goals

Ensure the group ’ s financial and political viability

Help develop the group ’ s policies

Support the group ’ s management

Makeup

Draw first from effective leaders

Then from major units that would be strategic partners

EIA Governance : Board of directors 2/2 Qualities Experience and duration in the enterprise Wide visibility and extensive network Can draw on institutional memories and experiences Track record of involvement with successful initiatives Entrepreneurial (can read and write a business plan) Experienced with centralization efforts Does not shy away from political situations Can “ sell ” a new concept and find internal funding Is like the people you need to “ sell ” to Has experience with consulting operations Has experience negotiating with vendors

Qualities

Experience and duration in the enterprise

Wide visibility and extensive network

Can draw on institutional memories and experiences

Track record of involvement with successful initiatives

Entrepreneurial (can read and write a business plan)

Experienced with centralization efforts

Does not shy away from political situations

Can “ sell ” a new concept and find internal funding

Is like the people you need to “ sell ” to

Has experience with consulting operations

Has experience negotiating with vendors

EIA Governance: Caterpillar’s boards Strategic board (quarterly; @10 members) “ Owners” of enterprise site Decide on major policies Settle conflicts Stakeholder board (monthly; 15-20) Ensure broad participation Ensure two-way communication Make recommendations re: policy to strategic board User advocacy board (meets as needed; 5-10) Represent major user groups Maintain pool of sample users

Strategic board (quarterly; @10 members)

“ Owners” of enterprise site

Decide on major policies

Settle conflicts

Stakeholder board (monthly; 15-20)

Ensure broad participation

Ensure two-way communication

Make recommendations re: policy to strategic board

User advocacy board (meets as needed; 5-10)

Represent major user groups

Maintain pool of sample users

EIA Services: Questions What should a team responsible for EIA actually do? How do their “services” fit with work that happens within business units? Or with outside contractors and consultants? What kind of people should manage these efforts? How do IA generalists and specialists fit together?

What should a team responsible for EIA actually do?

How do their “services” fit with work that happens within business units? Or with outside contractors and consultants?

What kind of people should manage these efforts?

How do IA generalists and specialists fit together?

EIA Services : Modular service plan Avoid “monolithic” approach: “Hi, we’re the EIA team and we’re here to help… and we’re going to centralize all of your information…” Break IA and CM into digestible, non-threatening tasks and sell those Allows you to divide and conquer clients… … and helps you understand IA challenges better (e.g., applying metadata in a centralized environment)

Avoid “monolithic” approach: “Hi, we’re the EIA team and we’re here to help… and we’re going to centralize all of your information…”

Break IA and CM into digestible, non-threatening tasks and sell those

Allows you to divide and conquer clients…

… and helps you understand IA challenges better (e.g., applying metadata in a centralized environment)

EIA Services : Potential service offerings 1/3 Client workflow-oriented (map to content publication process) Content authoring and acquisition Metadata development Content titling Content tagging Content review (voice, accuracy, etc.) Content formatting Formatting review Optimization for search engine optimization Publication

Client workflow-oriented (map to content publication process)

Content authoring and acquisition

Metadata development

Content titling

Content tagging

Content review (voice, accuracy, etc.)

Content formatting

Formatting review

Optimization for search engine optimization

Publication

EIA Services : Potential service offerings 2/3 User-oriented Persona and scenario development User testing and task analysis Search and server log analysis Content-oriented Content inventory and analysis Content evaluation and assessment Content model design Content development policy (creation , maintenance) Content weeding, ROT removal, a nd archiving Content management tool (acquisition , maintenance) Metadata development Metadata maintenance Manual tagging Automated categorization and classification

User-oriented

Persona and scenario development

User testing and task analysis

Search and server log analysis

Content-oriented

Content inventory and analysis

Content evaluation and assessment

Content model design

Content development policy (creation , maintenance)

Content weeding, ROT removal, a nd archiving

Content management tool (acquisition , maintenance)

Metadata development

Metadata maintenance

Manual tagging

Automated categorization and classification

EIA Services : Potential service offerings 3/3 Context-oriented Business metrics development and analysis Internal marketing strategy and implementation Stakeholder and decision-maker interviews Business rules development (for best bets, content models, etc.) Production/Maintenance Template design and application Training Policy/procedure/standards development and acceptance Publicity of new/changed content Tool analysis/acquisition (CMS, search, portal) Quality control and editing Link checking HTML validation Liaison with visual design staff , IT staff, vendors

Context-oriented

Business metrics development and analysis

Internal marketing strategy and implementation

Stakeholder and decision-maker interviews

Business rules development (for best bets, content models, etc.)

Production/Maintenance

Template design and application

Training

Policy/procedure/standards development and acceptance

Publicity of new/changed content

Tool analysis/acquisition (CMS, search, portal)

Quality control and editing

Link checking

HTML validation

Liaison with visual design staff , IT staff, vendors

EIA Services : Assessing departmental IA needs

EIA Services : Basic & premium levels Free services can lead to fee services

EIA Services : Phased demand for IA services

EIA Staffing: Questions Who should be involved: in-house, consultant, contractor? What type of specialization should the staff have? Should they be centralized or located within business units or both?

Who should be involved: in-house, consultant, contractor? What type of specialization should the staff have?

Should they be centralized or located within business units or both?

EIA Staffing : Tactical team basics 1/2 Goals Delivers IA services to the enterprise in content, users, and context areas Implements the strategic team ’ s policies Works directly with clients to understand their needs and develop new services to meet those needs

Goals

Delivers IA services to the enterprise in content, users, and context areas

Implements the strategic team ’ s policies

Works directly with clients to understand their needs and develop new services to meet those needs

EIA Staffing : Tactical team basics 2/2 Make-up driven by “market demand,” existing resources “ Vertical” IA generalists: split between EIA project enterprise business units “ Horizontal” IA specialists: “consultants” for both groups of generalists Tools (e.g., search, portal, CMS) Metrics Evaluation Metadata development XML and other markup languages

Make-up driven by “market demand,” existing resources

“ Vertical” IA generalists: split between EIA project enterprise business units

“ Horizontal” IA specialists: “consultants” for both groups of generalists

Tools (e.g., search, portal, CMS)

Metrics

Evaluation

Metadata development

XML and other markup languages

EIA Staffing : Tactical team qualities Entrepreneurial mindset Ability to consult (i.e., do work and justify IA and navigate difficult political environments) Willingness to acknowledge ignorance and seek help Ability to communicate with people from other fields Sensitivity to users ’ needs … and know about IA and related fields

Entrepreneurial mindset

Ability to consult (i.e., do work and justify IA and navigate difficult political environments)

Willingness to acknowledge ignorance and seek help

Ability to communicate with people from other fields

Sensitivity to users ’ needs

… and know about IA and related fields

EIA Staffing: Tactical team backgrounds/skills Human Computer interaction Cognitive Psychology Librarianship (reference) Marketing Branding Merchandising Organizational Psychology Business Management Operations Engineering Social Network Analysis Ethnography Economics Librarianship (tech. services) Information Science Journalism Technical Communication Computer Science Graphic design

Human Computer interaction

Cognitive Psychology

Librarianship (reference)

Marketing

Branding

Merchandising

Organizational Psychology

Business Management

Operations Engineering

Social Network Analysis

Ethnography

Economics

Librarianship (tech. services)

Information Science

Journalism

Technical Communication

Computer Science

Graphic design

EIA Staffing: Shoot for this org chart

EIA Staffing & Governance

EIA Funding Model: Questions How should this group be funded? How should other expenses (e.g., software licenses) be covered? Charge-back fees for individual services? Flat “tax” paid by business units? Covered by general administration's tab? Some hybrid thereof? Should certain services be performed gratis, while others require payment?

How should this group be funded?

How should other expenses (e.g., software licenses) be covered? Charge-back fees for individual services? Flat “tax” paid by business units? Covered by general administration's tab? Some hybrid thereof?

Should certain services be performed gratis, while others require payment?

EIA Funding Model: Looking for inspiration Study the successes/failures of the enterprise’s other centrally funded services Possible plan Initially: “tax” on business units and/or “seed capital” from senior management Ultimately: self-funding (models: IT, HR, special projects) Key: funding should be from central group (e.g., senior management) or self-funded; else too much dependency on business units

Study the successes/failures of the enterprise’s other centrally funded services

Possible plan

Initially: “tax” on business units and/or “seed capital” from senior management

Ultimately: self-funding (models: IT, HR, special projects)

Key: funding should be from central group (e.g., senior management) or self-funded; else too much dependency on business units

Potential models already in existence in the enterprise Charge-back Tax on business units Money from general fund Hybrids Charge-back model is attractive Increasing perceived value of IA by charging fees Compares well with duplicated expenses incurred by business units EIA Funding Model: Ensuring independence

Potential models already in existence in the enterprise

Charge-back

Tax on business units

Money from general fund

Hybrids

Charge-back model is attractive

Increasing perceived value of IA by charging fees

Compares well with duplicated expenses incurred by business units

EIA Funding Model: Diversify revenue streams

EIA Marketing & Communications: Questions How to position this work and the group that supports it: IA? User Experience? Web Design? How do these terms affect the scope of the work/charter of the group? How does a plan like this get “sold,” and to whom? Whose support is needed, and what tactics are useful in convincing them to support EIA work? How to prioritize which business units around the enterprise to work with?

How to position this work and the group that supports it: IA? User Experience? Web Design? How do these terms affect the scope of the work/charter of the group?

How does a plan like this get “sold,” and to whom?

Whose support is needed, and what tactics are useful in convincing them to support EIA work?

How to prioritize which business units around the enterprise to work with?

EIA Marketing & Communications: Positioning the EIA initiative Approaching “clients” No carrot or stick Offer services and consulting that save money, reduce tedium Branding: choose the term that is Hottest Has least baggage Steps on fewest toes

Approaching “clients”

No carrot or stick

Offer services and consulting that save money, reduce tedium

Branding: choose the term that is

Hottest

Has least baggage

Steps on fewest toes

EIA Marketing & Communications: Selling IA Concrete We can make work easier and save money for individual business units We can improve the user experience and build brand loyalty among customers, organizational loyalty among employees We can minimize the enterprise’s habit of purchasing redundant licenses and services

Concrete

We can make work easier and save money for individual business units

We can improve the user experience and build brand loyalty among customers, organizational loyalty among employees

We can minimize the enterprise’s habit of purchasing redundant licenses and services

EIA Marketing & Communications: One unit at a time Start with low-hanging fruit Killer content Plentiful or influential users Strategic value (business context ) Determine current status of the “client” What are they doing now? What expertise is in-house? What relevant tools do they own (extend licenses)? Are they enlightened?

Start with low-hanging fruit

Killer content

Plentiful or influential users

Strategic value (business context )

Determine current status of the “client”

What are they doing now?

What expertise is in-house?

What relevant tools do they own (extend licenses)?

Are they enlightened?

EIA Marketing & Communications: Illustrating the concept Select an initial model for centralized approach that’s familiar, accessible Staff directory often the best Serves all enterprise users Useful, highly structured content which may have significant metadata, searching and browsing capabilities Has high value in context of the enterprise’s daily operations

Select an initial model for centralized approach that’s familiar, accessible

Staff directory often the best

Serves all enterprise users

Useful, highly structured content which may have significant metadata, searching and browsing capabilities

Has high value in context of the enterprise’s daily operations

EIA Design/Timing: Questions An EIA design is an overwhelmingly large undertaking; how might it be broken into more digestible pieces? How should they be sequence: what makes sense to take on now, later, or perhaps not at all?

An EIA design is an overwhelmingly large undertaking; how might it be broken into more digestible pieces?

How should they be sequence: what makes sense to take on now, later, or perhaps not at all?

EIA Design/Timing: Modular, phased

EIA Design/Timing: 3-6 years, not months Use early successes as models Anticipate greater centralization among and within business units over time Support different levels of centralization concurrently (Neanderthals coexist with Space Agers)

Use early successes as models

Anticipate greater centralization among and within business units over time

Support different levels of centralization concurrently (Neanderthals coexist with Space Agers)

EIA Workflow: Questions How does the content authoring and publishing process work now? Who and how many are involved? How can the group support that work, and determine the best mix of centralized and autonomous responsibilities within that workflow?

How does the content authoring and publishing process work now?

Who and how many are involved?

How can the group support that work, and determine the best mix of centralized and autonomous responsibilities within that workflow?

EIA Workflow: Supporting variation, evolution Build around business units’ demand Use as driver for CMS selection

Build around business units’ demand

Use as driver for CMS selection

EIA Framework Takeaways Be entrepreneurial Market and sell services to internal clients Become self-sustaining by diversifying revenue streams Offer modular services Specific services, not full package Logical migration path accommodates all stages of evolution along centralization/autonomy axis for customers Do what can be done in baby steps Start with projects that are low hanging fruit Selective roll-out

Be entrepreneurial

Market and sell services to internal clients

Become self-sustaining by diversifying revenue streams

Offer modular services

Specific services, not full package

Logical migration path accommodates all stages of evolution along centralization/autonomy axis for customers

Do what can be done in baby steps

Start with projects that are low hanging fruit

Selective roll-out

Discussion

Contact Information Louis Rosenfeld, LLC 705 Carroll Street, #2L Brooklyn, NY 11215 USA [email_address] www.louisrosenfeld.com +1.718.306.9396 voice +1.734.661.1655 fax

Louis Rosenfeld, LLC

705 Carroll Street, #2L

Brooklyn, NY 11215 USA

[email_address]

www.louisrosenfeld.com

+1.718.306.9396 voice

+1.734.661.1655 fax