Seth Earley Presentation on Knowledge Management Through Search-Based Applications

Earley & Associates, Inc. | Classification: CONFIDENTIAL USE, NO REPRINTS Copyright © 2014 Earley & Associates, Inc. All Rights Reserved.

Knowledge management Through Search-Based Applications

2Copyright © 2014 Earley & Associates, Inc. All Rights Reserved.

About Me

[email protected] @sethearleyContact me:

• Founder & CEO, Earley & Associates• 20 years experience building

content and knowledge management systems

• Author, speaker & industry influencer

Seth Earley

Earley & Associates, Inc. | Classification: CONFIDENTIAL USE, NO REPRINTS Copyright © 2014 Earley & Associates, Inc. All Rights Reserved.

The Challenge of Search


What a Search Engine Sees

Topic of this page is PlayStation 3,

correct?


What a Search Engine Sees


Topical Analysis of Page Content


Metadata is Essential

What we give to the search engine…



What the search engine sees…



What properties can be inferred?

Title Author Created Date

Modified Date

File Type

…

Overview of SharePoint 2013 Preview Installation and Configuration

Alex Yarrow

06/21/2012

10/16/2012

docx …


Metadata is derived or applied

DEF Company

Support

ABC Company

ABC shall provide first level technical support to all Licensed Product end users and/or

Sublicensed Product customers/users. DEF will provide second level support. DEF shall provide to ABC a primary and a secondary support person to

act as the primary interface with ABC’s technical and customer support team. DEF shall provide

direct technical support to ABC for all uses of the DEF Software. Support level definitions and

responsibilities are set forth in Exhibit C. An “SLA Failure” as defined in Exhibit C shall qualify as a

Release Condition sufficient to authorize the Escrow Agent to release to Source Code to ABC pursuant to Section 7 and the Escrow Agreement.

LicenseContent Type =

Organization =

ABCcustomers

customer supportcustomer support team

DEFDEF software

end usersescrow agreement.

escrow agentexhibit c

licensed product

release conditionsection 7

secondary supportSLA

SLA failuresoftware

source codesupport level

sublicensed producttechnical support

Topic =

Forward Index – Words per documentInverted Index – Documents per word

Explicit metadata

Used to derive implicit metadata


Search index points to document

12

34

Forward Index – Words per documentInverted Index – Documents per word

A search index becomes derived metadata

about a collection of documents

Term Document

Acme 1, 2, 3, 4

customers 2, 3

escrow 3, 4

exhibit c 2

license 1, 4

…etc …etc

In which documents do these words occur?


Truth #1.

We have to change our definition of search.


Truth #2.

Search algorithms are getting better,

but they cannot infer human

context & intent.


Truth #3.

Taxonomy, metadata and information

architecture are key aspects of search.

Image credit: http://boxesandarrows.com/files/banda/developing_and_creatively_leveraging_hierarchical_metadata_and_taxonomy/ricci_01.gif

Metadata


Truth #4.

Search is increasingly looking like navigation.


Truth #5.

Search is messy.


“…search terms are short, ambiguous and an approximation of the searcher’s real information need…”

Source: http://research.microsoft.com/~ryenw/papers/WhiteCONTEXT2002.pdf Ryen W. White, Joemon M. Jose and Ian Ruthven

http://research.microsoft.com/~ryenw/papers/WhiteCONTEXT2002.pdf


1-Unpredictable 2-Aware 3-Competent 4-Synchronized 5-Choreographed

Metadata Processes

chaotic tagging practices with no taxonomy

custom metadata, use of listsenterprise taxonomy replicated

in site columns & managed metadata

document sets, term store, & attributes used effectively to

group, aggregate, sort, & filter assets

contextualized/ personalized metadata with auto-population

of values & quality audits

IA / User Experience

haphazard creation sites with inconsistent content models

and poor site experience

more consistent stable departmental sites with little

cross site structure

site collection consistency, reference metadata with

content relevant to specific groups and audiences

cross collection consistency, enforced metadata standards

context dependent user experience

integration of structured and unstructured information from multiple systems dynamically

presented to support user tasks

Search Integration random document generatorsome tuning of search with

content tagging

scopeable search with consistent but uncontrolled

tagging

faceted search with taxonomy-governed tagging

search based application with associative relationships (related search), tuned

algorithms, facets, and metrics driven use cases

User Proficiency and Content

Practices

poor or minimal usage, lack of awareness of capabilities or

content practices

early adopters and power users using out-of-box features, little

control of content

departmental collaboration with basic content control

cross-team project-oriented collaboration with information

lifecycle management

automated workflows and reporting for compliance with enterprise content standards

Governanceinformation sprawl, lack of

vision, no intentional decision making

awareness of challenges, activity is monitored but not

constrained

assigned responsibilities, oversight and communications

infrastructure in place

intentional decision making, resource allocation, change

controls in effect

agenda-driven business leadership and stakeholder

engagement to effect continuous process

improvement

Findability Model: Maturity in Search, IA & Content Management


What is a Search Based Application?


Search cannot be “bolted on” to a project or an application…

institutional awareness has to be designed into the fabric of the

tool.

Search Based Applications require integrated design methods – around user, process, task,

content, inputs, outputs, use cases and scenarios – in order to optimize the search experience.


From: http://www.domorewithsearch.com

Example SBA’s

“Search Based Application”? Or “Application”?


What are Search Based Applications (SBA’s)?

• Disparate locations• Structured and unstructured • Aggregated content• Integrated into user experience


What makes an application a “search based application”?


Success with Search Based Applications

• Content/Data Structure

• Business processes

• Holistic perspective

• Current state score card (heuristic assessment)


Developing SBAs requires an integrated information strategy

• Why develop SBAs? How will this benefit the organization?

• What needs to be in place in order to be successful?

• SBA’s consider content, data, structure and business processes from a holistic perspective.

• This presentation will point out critical elements of a successful SBA development program and lay out a high level plan for developing them.

An analysis and heuristic evaluation of the current search and content environment has been performed.

Opportunities for improvement have been identified in four specific areas: Information Architecture User Experience Search Integration Development of Search Based applications


LESS STRUCTURE MORE STRUCTURE

Chaotic Processes Controlled Processes

Problem solvingCollaboration

Accessing informationAnswering questions

Knowledge Creation Knowledge Reuse

ENTERPRISE KNOWLEDGE MANAGEMENTSpans Structured and Unstructured Processes

CL

AS

S o

f TO

OL

BlogsRecords

ManagementDocument

Management

Process ManagementWikis

Collaborative Spaces

Instant Messaging

Email Management

Web Content Management

Learning Management

Digital Asset Management

My SitesCentralized Publishing

The Content Continuum

SharePoint Span of Control


Unfiltered Reviewed/Vetted/Approved

Lower Value Higher Value

Lower Cost

Higher Cost

RELATIVE VALUE OF CONTENT

Not all content is of equal value

TY

PE

OF

CO

NT

EN

T

(More difficult to access)

(Easier to access)

Formal Tagging/Organizing Processes

Message text

External News Example deliverables

Discussion postings

Interim deliverables

Content Repositories

Success Stories

Benchmarks

Approved Methods

Best Practices

Social tagging (“folksonomy”)

Structured tagging

(taxonomy)



Application Construct Less Structure More Structure

Nature of Process Chaotic Processes Controlled Processes

Knowledge Management Knowledge Creation Knowledge Reuse

Purpose/Application Problem Solving/Collaboration Accessing Information/Answering Questions

SharePoint Span of Control My Sites Enterprise Publishing

Class of Tool Collaboration/Communication Workflow/Document Management


Information Construct Unfiltered Filtered

Cost Lower Cost Higher Cost

Value Lower Value Higher Value

Editing/Vetting Informal Formal

Tagging Folksonomy Taxonomy

Ease of Access Low High

Type of Content News/Messaging/Interim Deliverables Best Practices/Approved Methods/Reference Materials


Information and Access are Heterogeneous

Search/Tagging/Taxonomy Application Integration Framework

Data Sources

Access Mechanisms

BI Integration

Auto categorization/Clustering

Entity Extraction

Faceted Search

Semantic Search

Business Intelligence

Customer Relationship Mgt

Document repositories Custom databases and applications

Intranets/web pages

Product Lifecycle Management Digital Asset

Management

Data Warehouses

MessagingERP Systems

Ontology Navigation


Organizational Governance and Process Infrastructure


Designing for Content in Context

Audience Analysis

Content Analysis

Content Authoring

& Publishing Processes

Content Types,

Metadata Schemas

& Taxonomy Design

Workflow &

System Integration

Solution

Architecture Design

Search Based Applications

(Content in Context)

Workstreams


Improving the Search Experience | User Task Scenario

Peter ThompsonCustomer Service Agent


Improving the Search Experience | User Task Scenario

TA concepts that Peter must navigate and disambiguate to put together a common transaction.


Ambiguous Query “retirement” presents options to disambiguate



Query guidance suggests terms

based on search scenarios and top searches



Related search concepts (new

query) dependent on selected search

query



Best Bets presents landing pages for priority

terms



Faceted refinement

based on content model and specific to

search term concept



Associative terms are role

based



Experts presented based

on domain expertise

(mapped to search query)


Needed Activities for Search Disambiguation

Element Model Tuning Principle

Query guidance Taxonomy and thesaurus with role specific related search terms

Top queries, tasks, user scenarios, business goals

Narrower terms based on taxonomy

Related queries Triggered based on related terms from thesaurus

Scenario driven “you are asking for ‘a’ but you really want ‘b’

Ontological relationships of terms in domain model

Best Bet Primary landing page for ambiguous terms

Due to high overhead, limit to top 100 significant terms

Scenario driven, hand tuned

Context specific refiners Content object model Fine tune content object model based on use cases and user scenarios

Tasks are driven by detailed use cases derived from Concept of Operations

Role based associative term searches

Ontology, job roles, audiences

Similar to related queries but based on query results (post processing)

Entity extraction mapped to associative relationships and dependent on role


Enterprise Strategy

Business Unit Objectives

Increase customer satisfactionIncrease customer satisfaction

Improve retentionImprove retention

Increase wallet shareIncrease wallet share

Business Processes

Customer SupportCustomer Support Web Channel Sales

Web Channel Sales

Customer Relationship Management

Customer Relationship Management

Call Center Knowledge

base

Call Center Knowledge

baseE Commerce

System

E CommerceSystem

Processes enable objectives

L I N K

A G

E

Customer Acquisition Customer Acquisition

Grow top line revenueGrow top line revenue

Content supports processes

Objectives align with strategy

Content Sources

Working here (tools, technology, IA,

taxonomy, search, etc)

Measuring here (micro level -

effects)

Measuring here (macro level -

outcomes)

Aligning enterprise strategy with enterprise data

CEO: “Show me how will this project increase our revenue.”


Taxonomy & Information Architecture

DITA publishing

Developed Avatar application

Search Driven Help | Allstate Business Insurance

fin

an

cia

l

se

rv

ic

es

Business Challenge

ABI needed to make it easier for field agents to research and write (sell) business policies. Many agents focus on consumer insurance and are unfamiliar with business insurance offerings.

Agents have difficulty finding relevant help and how-to information

Providing agent help by phone is costly and becomes overused

Process for creating content and making it easy to find not effective

Approach

ABI’s vision for the proposed system included an avatar-based user experience

E&A’s comprehensive solution included …

Separate requirements definition and specification phase, with conceptual feasibility plan

Taxonomy developed for tagging in CMS and search in help application

User interaction model and wireframes clarified vision for client and development team

CMS selection and implementation. Content conversion of PDF and MS Word to DITA

Development of search application using text-to-speech via interactive avatar character

Governance model, change management and ongoing system support

Outcome

Search based application that provides quick, relevant results that can be communicated as text or spoken word through a custom Avatar user interface

ABI planning to open up new lines of business to infrequent users (agents)

Plan to reduce manual help desk operations by 80% in first 6 months after system goes live

New DITA-based content management system serves as foundation for future multi-channel delivery of relevant real-time content


m

an

uf

ac

tu

rin

g• Business Challenge

Desired to improve efficiency and streamline product-related information delivery to enable field service engineers to better support equipment on-site at customer locations globally.

Required a solution that:

Managed formal and collaborative content authoring processes.

Enabled metadata, taxonomy and social tagging for improved findability.

Protected customer and internal intellectual property.

Dynamically delivered relevant content in context.

• Approach

Development of process models outlining information flow for formal and collaborative publishing mechanisms including workflow design.

Design of solution architecture including site collections, subsites, lists/libraries, content types, metadata and taxonomy.

Design of a search-based application constructed on top of SharePoint 2010, Microsoft Fast Enterprise Search, Smartlogic Semaphore and BA Insight.

• Outcome

Enabled engineers to search and find product and spare part information across multiple data sources through a single, simple interface.

Offered collaborative features for engineers to engage, provide feedback and share knowledge and best practices with their peers.

Achieved a 50% reduction on time spent searching for information reducing time spent by 18,000 per week saving almost $100mm.

Named to the InformationWeek 500 as a top technology innovator for 2012.

Global, Secure Content Delivery

Solution Architecture, Process & Content Modeling

Search-Based Application & User Experience

Applied Rapid Knowledge (ARK) Portal | Applied Materials


Contact

Seth EarleyCEO

_____________________________EARLEY & ASSOCIATES, Inc.Cell: 781-820-8080Email: [email protected] Web: www.earley.com Follow me on twitter: @sethearleyConnect with me on LinkedIn: www.linkedin.com/in/sethearley

mailto:[email protected]

http://www.earley.com/

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Seth Earley Presentation on Knowledge Management Through Search-Based Applications