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© 2010 IBM Corporation Real Insight, Right Action Business Analytics & Optimization Sanjeev Shukla Associate Partner (BAO) IBM Global Business Services
© 2010 IBM Corporation
Real Insight, Right Action Business Analytics & Optimization
Sanjeev ShuklaAssociate Partner (BAO)IBM Global Business Services
© 2009 IBM Corporation2
Businesses are challenged by rapid change
Years to reach 50M users:
Radio38 years
TV13 years
Internet4 years
Facebook2 years
Supplier lead time is 62% faster than just two years ago
© 2009 IBM Corporation3
Analytics correlates to performance
Top Performers are more likely to use an analytic approach over intuition*
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright ©
Massachusetts Institute of Technology 2010.
Organizations that lead in analytics outperform those who are just beginning to adopt analytics
*within business processes
5.4x3x
© 2009 IBM Corporation4
Insight – everyone needs better hindsight, insight and foresight
What is the probability this borrower will
default?
How has number portability affected my
customer base?
Will my bank face liquidity shortfalls in the
next 12 months?
Is this insurance claim fraudulent, abusive or
excessive?
Which customers are more prone to usage
of Value Added Services
Why did we experience sub-optimal insurance
policy renewals?
Which customers do I move to e-channels?
© 2009 IBM Corporation5
Organizational, not data or financial concerns, are holding back adoption
Ability to get the data
Lack of management bandwidth due to competing priorities
Lack of skills internally in the line of business
Lack of understanding how to use analytics to improve the business
Culture does not encourage sharing information
Ownership of the data is unclear or governance is ineffective
Lack of executive sponsorship
Concerns with the data
Perceived costs outweigh the projected benefits
No case for change
38%
34%
28%
24%
23%
23%
22%
21%
21%
15%
Primary obstacles to widespread analytics adoption
Organizational
Data
Financial
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright ©
Massachusetts Institute of Technology 2010.
© 2009 IBM Corporation6
Historic trend analysis and forecasting
Standardized reporting
Data visualization
Analytics applied within business processes
Simulations and scenario development
Clustering and segmentation
Regression analysis, discrete choice modeling, and mathematical optimization
Data visualization
Simulations and scenario development
Analytics applied within business processes
Regression analysis, discrete choice modeling, and mathematical optimization
Historic trend analysis and forecasting
Clustering and segmentation
Standardized reporting
Increased or sustained value
Decreased in value
Today In 24 months
Organizations want to “see” insights more clearly – and act on them
Analytic techniques that provide the most value
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright ©
Massachusetts Institute of Technology 2010.
© 2009 IBM Corporation7
Analyze
Integrate
Manage
Business Analytics Applications
External Information Sources
Cubes
Streams
Big Data Master
Data
Content
Data
StreamingInformation
Govern
Quality
Security & Privacy
Lifecycle
Data Warehouses
Standards
Transactional & Collaborative
Applications
Mastering Information to Optimize Business Results
© 2009 IBM Corporation8
Core Banking
Treasury
HRMS / CRM / Financials
External Data
Historical Data
Enterprise Data Warehouse
Planning and Budgeting
Capital Computation
Downstream Analytical Engines
Metadata
Security and Data Privacy
System Management and Administration, Hardware and Software Platforms, Network Connectivity
Data Integration
System of Record
System
of Records
• Data Profiling/ Cleansing
• Extract source data
• Transform into target format
• Load into Data Warehouse
• Historical Data Migration
Campaign Management
ALM / FTP
Business Solution Areas
End User
Access
Executive Users
Power Users
General Users
Basic Users
Internet
Office
Handheld
Staging
Analytical and Tactical Reporting
Illustrative Analytics Architecture
Data Mining / Statistical Modeling
Master Data Management
Rules Engines
Originations, Collections
Other Sources
The Sources
Getting The D
ata
Storing & Processing The
Data
Downstream Engines
Analytics Engines
Information C
onsumption
Data Definitions
© 2009 IBM Corporation9
Core Banking
Treasury
HRMS / CRM / Financials
External Data
Historical Data
Enterprise Data Warehouse
Planning and Budgeting
Capital Computation
Downstream Analytical Engines
Metadata
Security and Data Privacy
System Management and Administration, Hardware and Software Platforms, Network Connectivity
Data Integration
System of Record
System
of Records
• Data Profiling/ Cleansing
• Extract source data
• Transform into target format
• Load into Data Warehouse
• Historical Data Migration
Campaign Management
ALM / FTP
Business Solution Areas
End User
Access
Executive Users
Power Users
General Users
Basic Users
Internet
Office
Handheld
Staging
Analytical and Tactical Reporting
Illustrative Analytics Architecture
Data Mining / Statistical Modeling
Master Data Management
Rules Engines
Originations, Collections
Other Sources
© 2009 IBM Corporation10
Core Banking
Treasury
HRMS / CRM / Financials
External Data
Historical Data
Enterprise Data Warehouse
Planning and Budgeting
Capital Computation
Downstream Analytical Engines
Metadata
Security and Data Privacy
System Management and Administration, Hardware and Software Platforms, Network Connectivity
Data Integration
System of Record
System
of Records
• Data Profiling/ Cleansing
• Extract source data
• Transform into target format
• Load into Data Warehouse
• Historical Data Migration
Campaign Management
ALM / FTP
Business Solution Areas
End User
Access
Executive Users
Power Users
General Users
Basic Users
Internet
Office
Handheld
Staging
Analytical and Tactical Reporting
IBM Components
Data Mining / Statistical Modeling
Master Data Management
Rules Engines
Originations, Collections
Other Sources
The Sources
•DataStage
•Quality Stage
•Infosphere CDC
iSAS
TM1
iMiner
•Metadata Workbench & Infosphere MDM
Tivoli Directory Server
© 2009 IBM Corporation11
Analytics Coverage - Illustrative
Prescriptive
•MIS, Reports, Cubes, Queries, Views, Graphs and Charts
Acquisition Origination Customer Management
Recoveries & Collection
•Statistical Data Analysis
•Modeling
•Scoring
•Predictive
•Segmentation
•Optimization
•Strategies, decision frameworks, rules, scoring and segmentation engines, exclusions, filters, alerts +
•Campaign definition, optimization
•Product /process decision, price, terms,
•Cross sell, up sell, attrition/ retention, Usage, Balance Build up
•Pre/early delinquencies to recoveries/write off
Data Types:
• Structured vs Unstructured
• Internal vs External
• Business Information vs Social Networking
Descriptive
InvestigativeAdvanced
© 2009 IBM Corporation12
Case Study: One of the Largest Indian Banks
Core Banking
Treasury
HRMS / CRM / Financials
External Data
Historical Data
Enterprise Data Warehouse
Planning and Budgeting
Capital Computation
Downstream Analytical Engines
Metadata
Security and Data Privacy
System Management and Administration, Hardware and Software Platforms, Network Connectivity
Data Integration
System of Record
System
of Records
• Data Profiling/ Cleansing
• Extract source data
• Transform into target format
• Load into Data Warehouse
• Historical Data Migration
Campaign Management
ALM / FTP
Business Solution Areas
End User
Access
Executive Users
Power Users
General Users
Basic Users
Internet
Office
Handheld
Staging
Analytical and Tactical Reporting
Data Mining / Statistical Modeling
Master Data Management
Rules Engines
Originations, Collections
Other Sources
The Sources
•DataStage
•Quality Stage
•Infosphere CDC
iSAS
TM1
iMiner
© 2009 IBM Corporation13
Case Study: One of the Largest Indian Banks
Sour
ces
© 2009 IBM Corporation14
