ESE 2015 recap – Themes and Observations

Search Success in 2016

Jeff FriedCTO, BA Insight

2

We Haven’t Discussed- Failure- Futures

We’ve Discussed

- some GREAT success stories- success factors- some technology and techniques

ESE 2015 Themes

The Enterprise Search Promise

RDBMS(JDBC, ODBC,

SQLNet, DW, DM)

Applications(e.g. ERM, CRM,

Help Desk)

Legacy Data(e.g. ISAM, VSAM, IMS)

Message Queues(e.g. TIBCO, MQ-Series)

DMS(e.g. M’Soft CMS,

Documentum)

eMail Systems(e.g. Notes,Exchange)

Files(e.g. Word, Excel,pdf, images, mp3)

Portals(e.g. WebSphere,

WebLogic)

WWW(HTML, XML, WML,

JavaScript)

Private Webs(e.g. news feeds,

Intranets)

Direct Push

UNSTRUCTUREDSTRUCTUREDREAL--TIME

Enterprise Search Platform

SITE

SEA

RCH

MAI

L SE

ARCH

BI S

EARC

H

DMS

SEAR

CH

CORP

ORAT

ESE

ARCH

ECOM

MER

CESE

ARCH…

4

Search for agile integration / aggregation Ownership, staffing, change management Context and tailored search experiences Efficiency and Risk Avoidance Open source and/or commercial products

Themes over the last 2 days

5

Findwise 2015 “findability maturity model”

Choose a new Search

Engine

Try and Index all

your content

Users Complain they can’t

find anything

Attempt to Tune

Relevancy and ask

users to tag

Determine the

problem is the search

engine

Search Immaturity Model

SurveysQuestionnairesFeedback

The problem must be the content is not indexed

RFI/RFP

FrustrationRoot Cause Analysis

Attempt Fix

Request Feedback

The Enterprise Search Cycle

?

EnvironmentWork Task

Situ

ati

on

User

Repository

Outcome

DateFormat

LanguageGenre

Authenticity

TopicDomain

ObjectiveTypeSolution

DomainWorkplace CultureManagerial factorsOrganizational factors

DistractionInterruptionTime

OrientationOverviewEvidence

ExplanationInstructionsDefinition

KnowledgeExperience MotivationPhysical abilityCognitive abilityLearning styleMental Model

Interface

Search Task

Source: Adapted from Toms (2011). Task-based information searching and retrieval inRuthven & Kelly (eds.) Interactive Information Seeking, Behaviour and Retrieval, p. 49

Context for search is … complicated

Mobile

, Con

text-

Aware

, Dev

ice-

Indep

ende

nt

Massive

ly

Scalab

le Data

Analyti

cs in t

he

Cloud

Social, User

Behavior

Analytics for

PersonalizationTechnolog

y Expectati

ons

End-User Expectations Consumerization

of IT & Intuitive User

Experience

Commoditization of Search Engines

Structured AND

Unstructured

BIG DATA

The Concierge

The Butler

Soon?

Will you take back to your organization from ESE 2015?

Do you think should be even better at ESE 2016?

What