Business Intelligence – The Next Major Competitive Differentiator
Boris EvelsonPrincipal AnalystForrester Research
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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
EnterpriseOptimization
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
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
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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 StructuredStructured 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 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
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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 3Row 1Row 2Row 3
Col 1 Col 2 Col 3Row 1Row 2Row 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 StateAssessment
Target StateVision
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
DataArchitecture
TechnicalArchitecture
TechnicalArchitecture
DataArchitecture
DataGovernance
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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
www.forrester.com