Enterprise Information Architecture: Because users don't care about your org chart

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

Enterprise Information Architecture Because Users Don’t Care About Your Org Chart

March 30, 2010 Merit Network

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


Seminar Agenda

Welcome/Introduction Topic: Top-Down Navigation Break Topic: Bottom-Up Navigation (content modeling) Exercise #1: Metadata Topic: Bottom-Up Navigation (metadata) Lunch Topic: Search Exercise #2: Search Analytics Break Topic: Research Methods Topic: Governance and Organizational Change


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)


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


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)


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 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


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?”


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?”


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


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 Evolution: Univ. Michigan example 1/2


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)


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)


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


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


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)


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


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)


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?


Specialized Site Index: CDC example 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


“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


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


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: 1.  Build shallow, broad taxonomy that will answer “where

will I find the information I need?” 2.  Rely on subsite taxonomies to answer “where in 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


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


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


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 Modeling: The basics Based on patterns revealed during

content inventory and analysis What makes up a content model?

1.  Content objects 2.  Metadata (attributes and values) 3.  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

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

Content Objects…

…link to other Content Objects… …by leveraging common Metadata Attributes

album page album review, discography, artist Album Name, Artist Name, Label, Release Date…

album review album page Album Name, Artist Name, Review Author, Source, Pub Date…

discography album review, artist description Artist Name, Album Name, Release Date…

artist description

artist bio, discography, concert calendar, TV listing

Artist Name, Desc Author, Desc Date…

artist bio artist description Artist Name, Individual Artist Name…

concert calendar

artist description Artist Name, Tour, Venue, Date, Time…

TV listing artist description Artist Name, Channel, Date, Time…


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


Content Modeling: Steps for developing a model

1.  Determine key audiences (who’s using it?) 2.  Perform content inventory and analysis

(what do we have?) 3.  Determine document and object types (what

are the objects?) 4.  Determine metadata classes (what are the

objects about?) 5.  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


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


Content Modeling: Metadata 1/2 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


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?


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 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


Metadata: What is metadata? Data about data Information which describes a document,

a file or a CD Common metadata •  CD information: title, composer, artist, date •  MS Word document properties: time last

saved, company, author


Metadata: Three types 1.  Intrinsic: metadata that an object holds

about itself (e.g., file name or size) 2.  Descriptive: metadata that describes

the object (e.g., subject, title, or audience)

3.  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)


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


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)


Metadata: Enterprise big picture


Metadata: Scaling problems Barriers to enterprise metadata development:

•  Volume of metadata vocabs./silos •  Complexity of semantic relationships (beyond synonyms)


Metadata attributes: Easy to difficult 1/2 Level of Difficulty

Metadata Attribute

Comments

Easy Business unit names

These are typically already available and standardized

Easy to Moderate

Chronology Variations in formats (e.g., 12/31/07 versus 31/12/07) usually can be addressed by software

Moderate to Difficult

Place names Although many standards exist (e.g., state abbreviations and postal codes), many enterprises (and their business units) use custom terms for regions (such as sales territories)



Metadata Attribute

Comments


Product names

Product granularity can vary greatly; marketing may think in terms of product families; sales in terms of items with SKU numbers, and support in terms of product parts that can be sold individually

Difficult Audiences Audiences, such as customers or types of employees, vary widely from unit to unit

Difficult Topics The most ambiguous type of metadata; difficult for individuals, much less business units, to come to agreement on topical metadata


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


Metadata: RDF (Resource Description Format) A syntax for expressing semantic

relationships Basic components

1.  Resource 2.  Property type

From Andy Powell: http://www.ukoln.ac.uk/metadata/presentations/ukolug98/paper/intro.html

3. Value 4. Property

1 3 2

4


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


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


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


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


Metadata: Dublin Core in 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?



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)


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)


Metadata: Semantic consistency 2/2

Users

Documents and Applications

Communication Chasm

ExamplePersonal Digital Assistant

SynonymsHandheld Computer

"Alternate" SpellingsPersenal Digitel Asistent

Abbreviations / AcronymsPDA

Broader TermsWireless, Computers

Narrower TermsPalmPilot, PocketPC

Related TermsWindowsCE, Cell Phones

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


Metadata: Semantic relationships Three types

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

2.  Hierarchical: Broader term, narrower term relationships

3.  Associative: Related terms that are related to each other


Metadata: Levels of control

Simple Complex

SynonymRings

AuthorityFiles ThesauriClassification

Schemes

Equivalence Hierarchical Associative

(Vocabularies)

(Relationships)


Metadata semantic relationships: Hard to hardest

Level of Difficulty

Type of Relationship

Examples

Hard Synonymous Synonym rings and authority lists

Harder Hierarchical Classification schemes

Hardest Associative Thesauri


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)


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


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


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


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


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


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


Metadata: Thesaurus entry example


Enterprise Metadata: Challenges Two barriers to enterprise metadata 1.  Interoperability (structural) 2.  Merging enables controlled vocabularies to

work as a whole (semantic) Interoperability must come before

merging (merging requires knowledge of which vocabularies to merge)

Few standards in use


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)


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


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


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


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)


Merging Vocabularies: Single unified thesaurus Highly impractical in enterprise context


Metadata: What is metadata? Data about data Information which describes a document,

a file or a CD Common metadata •  CD information: title, composer, artist, date •  MS Word document properties: time last

saved, company, author


Metadata: Three types 1.  Intrinsic: metadata that an object holds

about itself (e.g., file name or size) 2.  Descriptive: metadata that describes

the object (e.g., subject, title, or audience)

3.  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)


Metadata: Big org, big picture


Metadata: Scaling problems Barriers to enterprise metadata development:

•  Volume of metadata vocabs./silos •  Complexity of semantic relationships (beyond synonyms)


Metadata in the Large Org: Challenges Two barriers to enterprise metadata 1.  Interoperability (structural) 2.  Merging enables controlled vocabularies to

work as a whole (semantic) Interoperability must come before

merging (which requires knowledge of which vocabularies to merge)

Few standards in use



Metadata Attribute

Comments

Easy Business unit names

These are typically already available and standardized

Easy to Moderate

Chronology Variations in formats (e.g., 12/31/07 versus 31/12/07) usually can be addressed by software


Place names 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)



Metadata Attribute

Comments


Product names

Product granularity can vary greatly; marketing may think in terms of product families; sales in terms of items with SKU numbers, and support in terms of product parts that can be sold individually

Difficult Audiences Audiences, such as customers or types of employees, vary widely from unit to unit

Difficult Topics The most ambiguous type of metadata; difficult for individuals, much less business units, to come to agreement on topical metadata


Metadata: Levels of control

Simple Complex

SynonymRings

AuthorityFiles ThesauriClassification

Schemes

Equivalence Hierarchical Associative

(Vocabularies)

(Relationships)


Metadata semantic relationships: Hard to hardest

Level of Difficulty

Type of Relationship

Examples

Hard Synonymous Synonym rings and authority lists

Harder Hierarchical Classification schemes

Hardest Associative Thesauri


Metadata: Strategy for large orgs 1/2

Coordinate to ensure: •  Structural interoperability from the start •  Semantic mergability over time •  Vocabulary control and maintenance

through both manual and automated means

•  A workflow model and policies to support: •  Decentralized tagging and vocabulary updating

(through suggestions of new terms) •  Centralized review and maintenance


Enterprise Metadata: Strategy for large orgs 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:

1.  Business functions 2.  Products

3.  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


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


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)


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!


EIA and Search: Visually


Enterprise Search Design: Potential improvements

Basic search system components

Our focus: 1.  Clear interface 2.  Enhanced

queries 3.  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


Search Interface Design: Combine interfaces when possible

Two boxes bad, one box good, usually…

Will users understand?


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


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)


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”)


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


Spell Checker: Sur La Table example

A la Google…


Stemming: IBM example

IBM uses Fast Search


Concept Searching: Social Security Admin. example

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


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, 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


Document Titling: DaimlerChrysler example

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


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)


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


Step #1: common content elements Step #2: select elements to display

Step #1 Title Description Creator Topic Date Tech. Report Y Y Y Y Y

Policy Y N Y Y Y Product Sheet

Y Y N Y N

FAQ Y N N Y N

Step #2 Title Description Creator Topic Date Known-Item Y N Y N Y

Open-Ended Y Y N Y Y


Individual Search Results: Columbia University example

Long display for open-ended searchers…

…shorter display for known-item 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


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”)


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


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


Clustering by Language Example: PeopleSoft Netherlands

Result clusters for Dutch and English

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

Mixed Presentation of Search Results

159


“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


“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)


“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)


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 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


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)


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


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


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?


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., WebSort) •  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 •  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


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


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? 1. Prioritize and weight quality criteria 2. Rate content areas 3. Cluster into tiers 4. 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


Value Tier Approach: Weighting and scoring


Value Tier Approach: Prioritization


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


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


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


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


Involving Stakeholders: Departmental Report Card

Information Architecture Heuristic

Dept. 1

Dept. 2

Dept. 3

Supports orientation B- B B Supports known-item searching A C+ C Supports associative learning B C C Supports comprehensive research A B+ B Passes “navigation stress test” C F C+

… … … …


“Safe” User Sampling: The 2D Scorecard 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


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


The 2D Scorecard: Combining roles & demographics

TEST SAMPLE

SIZE

Demo. Profile

A

Demo. Profile

B

Demo. Profile

C

Demo. Profile

D TOTAL

Role 1 1 3 3 2 9

Role 2 2 2 1 1 6

Role 3 3 4 2 1 10

Role 4 0 3 4 0 7

TOTAL 6 12 10 4 32


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


The 2D Scorecard: After stakeholder input

TEST SAMPLE

SIZE

Demo. Profile

A

Demo. Profile

B

Demo. Profile

C

Demo. Profile

D TOTAL

Role 1 1 2 5 1 9

Role 2 1 1 3 1 6

Role 3 3 4 2 1 10

Role 4 0 3 3 1 7

TOTAL 5 10 13 4 32


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


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


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?


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


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


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


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 site search analytics


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 site search analytics)


Common Search Queries: What they tell us


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


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


Site Search Analytics: Benefits for interface 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…


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)


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


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


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


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


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


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


The EIA Framework Seven issues 1.  EIA governance: how the work and staff are

structured 2.  EIA services: how work gets done in an enterprise

environment 3.  EIA staffing: who handles strategic and tactical

efforts 4.  EIA funding model: how it gets paid for 5.  EIA marketing and communications: how it gets

adopted by the enterprise 6.  EIA workflow: how it gets maintained 7.  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


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?


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


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


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


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 1. Draw first from effective leaders 2. 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


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


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?


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)


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



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, and archiving •  Content management tool (acquisition, maintenance) •  Metadata development •  Metadata maintenance •  Manual tagging •  Automated categorization and classification



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?


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


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


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


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


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?


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


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


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?


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


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


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?


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


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?


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)


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?


EIA Workflow: Supporting variation, evolution 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


Discussion


Contact Information

Louis Rosenfeld, LLC 457 Third Street, #4R Brooklyn, NY 11215 USA

[email protected] www.louisrosenfeld.com @louisrosenfeld

+1.718.306.9396 voice +1.734.661.1655 fax

Enterprise Information Architecture: Because users don't care about your org chart

Technology

Transcript of Enterprise Information Architecture: Because users don't care about your org chart