Business Intelligence – The Next Major Competitive Differentiator Boris Evelson Principal Analyst Forrester Research

Business Intelligence – The Next Major Competitive Differentiator

Boris EvelsonPrincipal AnalystForrester Research

3Entire contents © 2008 Forrester Research, Inc. All rights reserved.

Theme

“Information about transactions will become more important than the

transactions themselves.”

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


Agenda

• Reemerging significance of BI

• BI market definition

• Trends

• Best practices

• Recommendations


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.


… and need to be optimized to compete

EnterpriseOptimization

BI

BPM/BRE

Effectiveness

Efficiency

=

=

Agility

=

Dynamic Apps


Top companies competing on analytics


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


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

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


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

IT budget-setters are investing in BI


… 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.


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


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

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

AppliancesNetezza, DATAllegro, Sun/Greenplum

Tier 2

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

Open source JasperSoft, Pentaho

Type SampleAlternative Analysis Methods

Spotfire, FAST Search, Endeca, Attivio

Dashboard pure-plays

iDashboards, Corda

BPM Lombardi, Global360, Savvion

BREPegasystems, 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


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 StructuredStructured and unstructured

Information (data type agnostic)


BI is evolving fast and across many dimensions

Yesterday Today Tomorrow

Functionality Report, analyze Monitor Predict

Mode Reactive ProactiveProactive and predictive

Data models StarStar 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


• 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 3Row 1Row 2Row 3

Col 1 Col 2 Col 3Row 1Row 2Row 3

Other trends: alternative BI DBMS


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


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.


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


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


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

Current StateAssessment

Target StateVision

Gap Analysis Prioritization Road Map

• Requirements

• Governance

• Human resources

• Architecture / technology

• Change management

• Risk management


Best practices – do’s and don’t’s

Data Governance

DataArchitecture

TechnicalArchitecture

TechnicalArchitecture

DataArchitecture

DataGovernance


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.


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.


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


Thank you

Boris Evelson

+1 617/613-6297

[email protected]

www.forrester.com

Documents

Business Intelligence – The Next Major Competitive Differentiator Boris Evelson Principal Analyst Forrester Research