OK so Enterprise Search is "Janky" - Now What? The Path to Search-Based Applications

Copyright © 2016 Earley Information Science1
OK so Enterprise Search
is "Janky" – Now What?
April 20, 2016
Copyright © 2016 Earley Information Science
Seth Earley, EIS
Dino Eliopulos, EIS
Ed Dale, EY
Jeff Fried, BA Insight

Today’s Agenda
• Welcome & Housekeeping
• Dino Eliopolus, Managing Director, Earley Information Science
(@DEliopulos)
• Session duration & questions
• Session recording & materials
• Take the polls & the survey!
• The Panelist Point of View
• Seth Earley, CEO, Earley Information Science (@SethEarley)
• Ed Dale, Ernst & Young (@EdDale)
• Jeff Fried, CTO, BA Insight (@JeffFried)
• Expert Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable

Copyright © 2016 Earley Information Science3 Copyright © 2016 Earley Information Science
OK so Enterprise Search is "Janky" – Now What?

• Experienced leader and innovator in industry and high-end professional IT
consulting with deep specialization in user experience and highly complex
business applications.
• Has over 2 decades of experience in applying Machine Learning, Data
Mining and other AI techniques to deliver rich content-driven solutions for
Retail, CRM, hi-tech manufacturing, healthcare / insurance and financial
services.
• Has depth in many industries including Financial Services, Retail / CPG,
Telecommunications, Travel and Entertainment, Healthcare,
Pharmaceuticals, Hi-Tech Manufacturing and Energy.
• Expertise in all aspect of IT Professional Services including strategy,
planning, forecasting, budgeting, measurement, sales, talent acquisition /
management and retention, career stewardship, program management and
service delivery.
• Highly collaborative and results-oriented management style delivers
outstanding outcomes for his clients, his employers and his teams.
Dino Eliopulos - Biography
Dino Eliopulos
Managing Director
Earley Information
Science

• For years, organizations have tried to harness the power of search
technologies to give employees access to the content and
resources they need to do their jobs.
• During that same time, search technology has evolved to include
keyword, tagging, natural language and semantic search.
• But the experience that users have with search has not seemed to
improve at the rate that search technologies have evolved.
• Organizations continue to introduce new technologies, processes
and data into their enterprise to make search better, but still the
number one complaint that users have about their intranets is that
they can’t find anything.
The Problem with Enterprise Search
"I can't find anything on our portal."
Intranet Connections: “10 Complaints about your Intranet
Portal”
“One of our staff’s main complaints
(there were many) about our old intranet
was the search. It was slow and the
results didn’t contain what you were
looking for.“
Interact-Intranet: “What a difference an intranet makes”

• Virtual Assistants
– New model that leverages learning and prediction and structured
interactions (E.g., Cortana, Siri, ABIe, Google Now)
• Discovery
– Proactive collection of information to create personalized information
feeds for users (E.g., Spotify, Pandora)
• Graph Search
– Integrating multiple sources of information together about a user to
improve relevance and personalize results (e.g., Facebook,
Sharepoint 2016 / GQL)
Recent Trends in Search that will “help”

Seth Earley - Biography
Seth Earley
CEO and Founder
Earley Information
Science
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Guest speaker
AIIM Master Trainer
Course Developer &
Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
US Strategic Command briefing on knowledge networks
Northeastern University
Boston Knowledge Management Forum
Long history of industry education and research in emerging fields
Academy of Motion Picture Arts and Sciences, Science
and Technology Council Metadata Project Committee
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data
management systems, taxonomy, ontology and
metadata governance strategies

• The definition of “search” has changed.
– It’s not a white box. It’s an experience.
– Search is about aggregation, access, and capabilities.
– Search algorithms may be improving, but they can’t infer intent or human context (i.e., searcher’s role or
perspective).
• Search can’t be “bolted on” to a project or application.
– Search optimization requires integrated design methods:
• user
• task and process
• content
• inputs and outputs
• use cases and scenarios
Search Functionality in Context
(Search-Based Applications)

ORGANIZING
PRINCIPLES &
INFORMATION
ARCHITECTURE
SEARCH TOOLS
&
TECHNOLOGY
PROCESS,
CULTURE &
GOVERNANCE
Findability Ecosystem
Search effectiveness is reliant
on a combination of factors
Why do people have
trouble locating
information?
The answer required us to
look at the complete findability
ecosystem.

1-UNPREDICTABLE 2-AWARE 3-COMPETENT 4-SYNCHRONIZED 5-CHOREOGRAPHED
METADATA
PROCESSES
Chaotic tagging practices with
no taxonomy
Custom metadata, use of lists
Enterprise 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 generator
Some 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
GOVERNANCE
Information 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:
Search, IA & Content Management

Bottom-Up Development
Both top-down and bottom up approaches to developing IA are needed
OBSERVE SUMMARIZE CONCEIVE DEVELOP
IDENTIFY
AUDIENCES
DEFINE
TASKS
BUILD USE
CASES
IDENTIFY
CONTENT
ORGANIZE
CONTENT
ANALYSIS
TAXONOMY METADATA
CONTENT
MODELS
MENTAL
MODELS
SITE MAPS &
NAVIGATION
WIREFRAMES
Top-down Information Architecture
Bottom-up Information Architecture

How to interpret use cases (example):
• Actor: persona development, actor taxonomy/model
• Action: search domains, scenarios
• Objective: scenarios and test cases, search design
• Content: content types, schemas, autoclassification
• Metadata: taxonomies, content models
Detailed Use Cases Library
ACTOR ACTION OBJECTIVE CONTENT USED METADATA
Consultant Find approaches for
use in a project
Mobilize an engagement Methodology Industry
Project type
Topic

Search as an Application
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
CONTENT MODELING
• Content Type Definitions
• Metadata Schema Design
• Managed Metadata Service
Design
• Taxonomy Framework and
Development
Process & Integration –
• Workflow Design
• Automated vs. Manual Process
Analysis
• Online vs. Offline Functional
Capability
• Data Integration and
Synchronization
Solution Architecture Design –
• Site Collection Architecture
• Site Maps and Logical Content
Organization
Search Based Applications –
• Navigation
• Wireframes
• User Interface Design
Audience & Content Analysis –
• Content Audits and Inventories
• Personas, User and Group
Matrices
• User Scenarios and Use Cases
Content Authoring & Publishing –
• Content Creation and Curation
• Information Lifecycle
Management Design
• Publication Process Modeling

Content Continuum: Structure
Less Structure More Structure
• Problem solving
• Collaboration
• Opportunity work
• Creative authoring
KNOWLEDGE CREATION KNOWLEDGE REUSE
Spans Structured and
Unstructured Processes
CLASSOFAPPLICATION
Blogs
Records
Management
Document
Management
Process
Management
Wikis Collaborative
Spaces
Instant
Messaging
Email
Management
Web Content
Management
Learning
Management
Digital Asset
Management
PORTALSpan of Control (SharePoint)MYSITES
• Accessing information
• Answering questions
• Scheduled work
• Content management
CHAOTIC
PROCESSES:
CONTROLLED
PROCESSES:

Content Continuum: Value
Less Value More Value
UNFILTERED CONTENT VETTED & APPROVED
Spans Casual and Formal
Tagging & Organizing Principles
STRUCTURED TAGGING
(TAXONOMY)Span Of Metadata Intentionality
SOCIAL TAGGING
(FOLKSONOMY)
LOW-COST CONTENT
LESS ACCESSIBLE
HIGH-COST CONTENT
MORE ACCESSIBLE
TYPEOFCONTENT
Best PracticesBenchmarks
Approved
Methodologies
Message Text Discussion
Postings
External News
Interim
Deliverables
Templates
Example
Deliverables
Content
Repositories

Content Continuum: Task context
Localized Application Generalized Application
• More focused use
• Service line/ function
scope
• “Employee desk”
• “In the weeds” technical
problem solving
NARROW AUDIENCE BROAD AUDIENCE
Spans Narrow and Broad
Audiences and Application
GLOBALSpan of ConsumptionLOCAL
• General use
• Organizational scope
• “Headquarters lobby”
• Higher-level messaging
and common processes
APPLICATION
SPECIFIC:
ENTERPRISE
WIDE:
TASKOBJECTIVE
Communicate
policy
Common
process
documentation
Firm-wide
messagingTechnical assets SME asset
curation
Solve
specific
problems
Capability
development
Executive level
vision
Reuse broadly
applied assets
BU Level Assets

Content Continuum: Summary
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
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 Messaging/Interim Deliverables Best Practices/Reference Materials
Task Construct Engagement Level Policy Level
Audience Narrow Broad
Application Local General
Context Function al areas Organization
Level of detail Technical / In depth Higher-level & common processes
Metaphor Employee desk Headquarters lobby

Information and Access are Heterogeneous
Search/Tagging/Taxonomy 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
Messaging
ERP Systems
Ontology Navigation

Search as Recommendation Engine
Some segmentation concepts based on work of Vladimir Dimitroff
Simple Attribute Models
• Few variables
• Unambiguous
• Objective
Less Complex
Based on empirical understanding
Easier to model
More Complex
Based on probabilities
Learning algorithms
Sophisticated Attribute
Models
• Multiple variables
• Potential ambiguity
• Subjective – requires
knowledge of domain and
experience with behaviors
Latent Attribute Models
• Many variables
• Patterns emergent
• Dependent on probabilities
“Black boxes”
“Secret sauce”
“Latent Dirichlet
Allocation”
“LDA”
Subjective Attribute
Models
• Based on judgment of
modeler
• Greater ambiguity
• More difficult to validate
Matching
algorithms
Query across data

Poll Question #1
Where are you in the maturity model?

A. Crawl
B. Walk
C. Run
D. Sub-orbital flight
E. Light-speed
Where are you in the maturity model?

Jeff Fried - Biography
Jeff Fried
CTO, BA Insight
Jeff.fried@bainsight
@jefffried
Longtime
Search Nerd
• CTO, BA Insight
• Senior PM, Microsoft
• VP, FAST
• SVP, LingoMotors
Passionate About
• Search
• SharePoint
• Search-driven
applications
• Information Strategy
Blog
DoMoreWithSearch.com
Technet Column
“A View from the
Crawlspace”
jeff.fried@bainsight.com

Search is a “Wicked Problem”

Wicked Problems are Problems Worth Solving




A wicked problem is a problem that is difficult or impossible
to solve because of incomplete, contradictory, and changing
requirements that are often difficult to recognize.

Technology
Expectations
End-User
Expectations
Consumerization of IT
& Intuitive User Experience
Commoditization of
Search Engines

Poll Question #2
What are the biggest challenges you are facing related to search in
your organization?

A. Finding and implementing the right search technology
B. Executing the right content processes
C. Managing and monitoring governance
D. Marshaling the correct resources / staff /skills
What are the biggest challenges you are facing
related to search in your organization?

• 16 years with Ernst & Young
• Leader of the Search Services Team, Global
Markets – EY Knowledge, which is responsible
for the EY Home Page intranet search and
SharePoint search
– Sets strategy for Enterprise Search which is to
leverage the new SharePoint 2013 environment as the
Enterprise Search environment, while providing
support for the existing environment
– Manages team activities in support of search
performance analysis, tuning and other issues as well
as content findability and content gap identification
Ed Dale - Biography
Ed Dale
Search Services Manager
Ernst & Young

Search is work
Earley Executive Roundtable – April 2016
Ed Dale

The context for knowledge: our organization
EY is an organization of member firms operating in 150 countries.
► We collaborate globally to offer audit, tax, transaction and advisory services.
► Each service line has a wide, diverse range of business units and offerings.
► Our organization is constantly growing and evolving.
We compete in a market where insights are the product:
knowledge is and will be a key differentiator.

The context for knowledge: our people
Our 212,000 people are our greatest asset.
► Their collective intelligence drives a client experience that is connected, responsive and
insightful.
► They could be working from any site in any location.
► We have a large population of Millennials accustomed to being self-sufficient through the
internet and connected by social networks.
We must be able to connect people to each other and to the best of EY’s
knowledge anytime, anywhere.

My perspective
► One consistent theme
► The process to improve enterprise search is:
► Identify the correct content
► Measure how well search returns that content
► Tune the search engine to return that content better
► Measure the change
► Repeat
► Technology makes the work easier, but does not replace it
► Interesting trends in search technology are ones that make the work easier
► Graph search – adds information to relevancy
► About the person
► About their actions
► Data lake simulation

Poll Question #3
How do you measure the effectiveness and quality of your search
solution?

A. We are not measuring
B. We are reviewing feedback from users
C. We are using analytics to manually monitor
and inform continuous improvement
D. We are dynamically targeting and measuring
personalized relevancy based on multiple
sources of evidence
How do you measure the effectiveness and
quality of your search solution?

Panel Discussion

Roundtable Discussion
Ed Dale
Search Services
Manager
Ernst & Young
Dino Eliopulos
Managing Director
Earley Information
Science
Seth Earley
CEO
Earley Information
Science
Jeff Fried
CTO
BA Insight

Center of Excellence Model for Enterprise Search
1 Evaluate
Current State
Envision
Future State
Determine
Gaps
Prioritize
Projects
Create
Roadmaps
Assessment Search and Findability Strategy
Education and Knowledge Transfer
Research & Discovery (phase two)
2 3 4 5
6 7 8 9 10
11 12
Research & Discovery (phase one)
Interaction
Design
Requirements
Definition
Content
Analysis
& Modeling
Process
Analysis &
Improvement
User
Analysis
& Modeling
User Types
Audience Profiles
Personas
Personalization
User Scenarios
Task Analysis
Use Case
Definitions
“who”
Content Profiles
Metadata Schemas
Taxonomy
Information Lifecycle
“why” “what”
Technology
Solution Architecture
Navigational Models
Wireframes
“how”
Test &
Validate
Future State
Search Program Governance Strategic Advisory Program
Strategy&VisionDesign&DevelopMaintain
EnhanceandEvolve

Suggested Resources
Groups
Enterprise Search Professionals on LinkedIn
https://www.linkedin.com/groups/161594
Enterprise Search on LinkedIn
Enterprise Search Products & Services on LinkedIn
Books
Enterprise Search by Martin White
http://www.amazon.com/Enterprise-Search-Enhancing-Business-
Performance/dp/1491915536/ref=sr_1_1?s=books&ie=UTF8&qid=14611640
50&sr=1-1&keywords=enterprise+search
Relevant Search by Doug Turnbull & John Berryman
https://www.manning.com/books/relevant-search
Search User Interfaces by Marti Hearst
http://www.amazon.com/Search-User-Interfaces-Marti-
Hearst/dp/0521113792/ref=sr_1_1?s=books&ie=UTF8&qid=1461164235&sr
=1-1&keywords=search+marti
Search Patterns by Peter Morville & Jeffery Callender
http://www.amazon.com/Search-Patterns-Discovery-Peter-
Morville/dp/0596802277/ref=sr_1_2?s=books&ie=UTF8&qid=1461164235&s
r=1-2&keywords=search+marti
Other Resources
IDC Case Study on Knowledge Sharing and Reuse
http://www.earley.com/sites/default/files/IDC_Case-study_Applied-
Materials_2014-06-04.pdf
Six Critical Success Factors for SharePoint Enterprise
Content Management (ECM) Implementations (white
paper)
http://info.earley.com/6-critical-success-factors-sharepoint-ecm-
implementation-whitepaper
High Impact Solutions for Solving Content Chaos (recorded
webcast)
http://www.earley.com/training-webinars/high-impact-solutions-solving-
content-chaos
Making Intelligent Virtual Assistants a Reality
http://info.earley.com/make-intelligent-virtual-assistant-reality-whitepaper

Earley Information Science
(EIS)
Information Architects
for the Digital Age
Founded – 1994
Headquarters – Boston, MA
www.earley.com
For more info contact:
info@earley.com
careers@earley.com
Thanks to our Sponsors
Next Roundtable topic
May 25th - Predictive Analytics, AI
and the Promise of Personalization

OK so Enterprise Search is "Janky" - Now What? The Path to Search-Based Applications

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à OK so Enterprise Search is "Janky" - Now What? The Path to Search-Based Applications

Similaire à OK so Enterprise Search is "Janky" - Now What? The Path to Search-Based Applications (20)

Plus de Earley Information Science

Plus de Earley Information Science (18)

Dernier

Dernier (20)

OK so Enterprise Search is "Janky" - Now What? The Path to Search-Based Applications