BI - Information Builders

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Business Intelligence


The Next
Major Competitive Differentiator


Boris Evelson

Principal Analyst

Forrester Research

3

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Theme

“Information about
transactions will become
more important than the
transactions themselves.”

Walter Wriston, chair and CEO of
Citicorp/Citibank, 1967
-
1984

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Agenda


Reemerging significance of BI


BI market definition


Trends


Best practices


Recommendations

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Enterprises are feeling the pain


It is becoming increasingly difficult for enterprises to compete.



Only change is constant.



Productivity gains and efficiencies
were enough yesterday, but today
and tomorrow businesses need to
reemphasize effectiveness to win
market shares and grow.


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… and need to be optimized to compete

Enterprise

Optimization

BI

BPM/BRE

Effectiveness

Efficiency

=

=

Agility

=

Dynamic
Apps

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Top companies competing on analytics

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Robust BI environments are needed to turn
mountains of data into information


The globe’s information production in 2003 was 5
exabytes.

»
5,000,000,000,000,000,000


18 zeros

»
Equivalent in size to the information
contained in 37,000 new libraries, each as
big as the US Library of Congress book
collection

»
92% of new information is stored on magnetic
media, primarily hard disks

»
Almost a gigabyte per every person on Earth


This figure is growing at 30% a year, so we’ll be
reaching zetabyte sizes by year 2010


that’s a
number with 21 zeros!

Source: University of California, Berkeley

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… and that’s why I&KM Initiatives are hot

Source: January 25, 2008, “The State Of Enterprise Software Adoption: 2007 To 2008” report

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Source: March 27, 2008, “The State Of Enterprise IT Budgets: 2008” report

IT budget
-
setters are investing in BI

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… but BI stack is complex and heterogeneous

Business intelligence is a
set of methodologies,
processes, architectures,
and technologies that
transform raw data into
meaningful and useful
information used to
enable more effective
strategic, tactical, and
operational insight and
decision
-
making.

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. . . and it’s only one of the components in IW

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BI “Ecosystem”


over 350 players!

Type

Sample

Pure
-
plays

Cognos (IBM), Business
Objects (SAP),
MicroStrategy
,
Actuate, SAS, Information
Builders,
Informatica
,
AbInitio

Stack

Oracle, Microsoft, SAP

Combination

Teradata
, Sybase, HP

ERP

Lawson,
Infor

BPS

OutlookSoft (SAP), Cartesis
(Business Objects), Longview,
Applix(Cognos)

Appliances

Netezza, DATAllegro,
Sun/Greenplum

Tier 2

ASG, QlikTech
, Panorama,
LogiXML
, Dimensional Insight,
InforSense
,
Inetsoft
, BOARD,
VisualMining
, Tableau,
SeaTab
,
Skytide

Open source

JasperSoft
,
Pentaho

Type

Sample

Alternative
Analysis
Methods

Spotfire, FAST Search, Endeca,
Attivio

Dashboard
pure
-
plays

iDashboards, Corda

BPM

Lombardi, Global360, Savvion

BRE

Pegasystems, Fair Isaac, CA,
Corticon

BAM/CEP

TIBCO, Syndera, Coral8

SIs

Big 4, boutique, offshore

SaaS

SeaTab, 1010data, Lucidera

BPO

Oco, Omniture, Coremetrics

Supporting
apps

Portals, Collaboration, Search,
Metadata, MDM, ILM, and many
others

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BI is evolving fast and across many dimensions

Yesterday

Today

Tomorrow

Decision types

Strategic

Tactical

Operational

Criticality

Nice to have

Differentiator

Mission critical

Data volumes

Gigabytes

Terabytes

Petabytes

No users

<100

<10,000

10,000+

Architecture

Proprietary

Open

Optimized

Data type

Structured

Structured and
unstructured

Information
(data type
agnostic)

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BI is evolving fast and across many dimensions

Yesterday

Today

Tomorrow

Functionality

Report, analyze

Monitor

Predict

Mode

Reactive

Proactive

Proactive and
predictive

Data models

Star

Star and
normalized

All (star,
normalized, flat)

Refresh rates

Batch

Near real time

Real time

Executed by

IT

Power users

Self service

Centricity

Data
-
centric

Data
-
centric

Process
-
centric

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Columnar

»
More agile

»
Less space

»
Faster queries


In Memory

»
Lightning fast

»
No OLAP limitations


Index

»
More agile

»
Seamless structured and
unstructured BI

Col 1

Col 2

Col 3

Row 1

Row 2

Row 3

Col 1

Col 2

Col 3

Row 1

Row 2

Row 3

Other trends: alternative BI DBMS

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Other trends: alternative analytical methods


Visual pattern recognition

»
Most traditional OLAP methods fail when number of dimensions
exceeds a few dozen.

»
Best at analyzing “broad” data sets with 100+ dimensions

»
Today: life sciences, energy/mining

»
Tomorrow: financial services

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Other trends: spreadsheets and BI


Spreadsheets


the most widely used business intelligence (BI)
tool


play an integral role in all layers of the BI stack.


Lack of controls, security, and integrity, as well as integration with
business processes create tremendous challenges


and
opportunities






“Getting rid of spreadsheets” battle was fought and lost.


With proper governance, methodology and latest tools, one can
continue to reap the benefits of spreadsheet applications while
getting arms around control and risk issues.

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More Trends: Continued Innovation


BI market is consolidating but not commoditizing.


Consolidation


Large vendors increasingly have to balance integration vs.
innovation priorities.


Innovation


still plenty of room


Data discovery


Guided analytics /search


Consumerization of enterprise technology or “Tech
Populism”


Knowledge Shadows / Blind Spots

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Unfortunately there are still many inhibitors
to successful BI implementations

Lack of centralized data / BI governance

BI application does not have

access to all relevant data

BI application too complex to learn

and navigate

Too much dependency on IT

for new reports and report
enhancements

BI application is too inflexible

and slow to react to changes

Unclear

ROI

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So what do we do? Best practices / strategy to
the rescue

Current State

Assessment

Target State

Vision

Gap Analysis

Prioritization

Road Map


Requirements


Governance



Human resources


Architecture / technology


Change management


Risk management


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Best practices


do’s and don’t’s

Data

Governance

Data

Architecture

Technical

Architecture

Technical

Architecture

Data

Architecture

Data

Governance

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Best practices

Start by picking a senior C
-
level (non
-
IT) executive to
sponsor and champion data governance organization.

Proceed to creating and empowering data governance and
data stewardship organization.

Conduct analysis of the current state as a starting point of
the BI strategy journey.

Define logical and physical data requirements, which will
serve as the basis and will drive the rest of the BI
architecture.

Identify all types of users involved in the BI initiative


remember: not all users are created equal.

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Best practices continued

If appropriate to your organization’s culture and environment,
start with an industry standard analytical data model.

Make sure that all BI stack components (not just the obvious
ones) are addressed in your BI strategy vision and
architecture.

Pick a reputable systems integrator partner with extensive BI
strategy and implementation background.

Make sure that the strategic roadmap is divided into “baby
step” tasks, with concrete deliverables no more than a few
weeks apart.

Pick high value, low cost, low complexity targets for the first
few iterations to ensure initial success and momentum.

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Final thoughts


Three top keys to successful BI implementations are:
data governance, data governance, and data
governance.



Ensure that the BI foundation is comprehensive and
supportive of future trends.



Understand that BI for multi
-
terabyte data sets may
require different architectures and technologies.

»
Research and implement the most appropriate
VLDB/BI options.

»
Plan for explosive data growth: 10x
-
100x.



Have a strategy / approach for handling lightweight BI
applications: Excel, Microsoft Access


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Thank you

Boris Evelson

+1 617/613
-
6297

bevelson@forrester.com

www.forrester.com