1. 1

2. Selecting Right Data Modeling Strategy
by Vinay Patange

3. 3
Introduction
3
Who We Are: Facts and Figures
 About 150 years old with Market Cap of USD$50 bn
 1700 branches, 5600 ATMs in more than 70 countries.
 Presence predominantly in Emerging Markets
 About 75,000 worldwide staff
 Technology Development in Singapore, Malaysia, U.K. and India
 Singapore has about 6000 staff
 Leaders in Foreign Exchange Currencies (FX) in Emerging Market
Consumer Banking Wholesale Banking
Financial Markets
Corporate Banking

4.  Business Decision Making
 Timely Information (Operational Decisions, running daily business)
 Ad-Hoc Analysis (Explorative, what went well and what did not go well)
 Predictive Analytic Tools (What if scenarios)
 Interactive Visualization (Trends and Patterns)
 Areas of Information and Intelligence
 Online Transaction Systems
 Data Warehouse
 Data Marts
 Operational Data Stores
 End User Computing
4
What lies beneath the Business Analytics?
Data is the Company’s most valuable asset and keeping it in right data structure is
equally important to derive the information and intelligence.

5.  Data Structures/Models influence
 Business Rules Implementation (Cardinality, Optionality and Business Representation)
 Information Retrieved and Interpreted (Metadata, Hierarchies and Metrics)
 Data Integrity and Quality
 Data Stewardship & Governance
 Operational Efficiencies in Data Management
5
Why Data Modeling is Important ?
Data Models explain the Business Rules and Selecting Right Data Modeling
Strategy is key to the success of Data Management

6.  Multiple persistent data layers create data obsolescence with silos and redundant processes.
 There are approximately three times more offline systems than on-line systems
6
Challenges in Information Management Landscape
Account
(Africa)
Local Reg
Reporting
Cost Reporting
MS Access
MM APPLE
SQL Svr
Trades
Mango
SQL Svr
Capital
AP
eProc
T&E
AM
GL
RMI
PMI
ECMI
BS MI
Risk Frontier
Accounting & Costs
TP Systems
Core and Loan TP
Systems
SIP
Commodities
FX DB
MS Access
DPLSales
BIReportingLayer(XLCube)
Sales & Marketing
Product Control
Sales Cube
Country Finance
Money Market Derivatives
Exotics
Equities
Equity Derivatives
Cash
Fixed Income
Bond Trades
Repos
Bonds
Bloomberg
PnL
IREC
Party Static
DPL Cube
Traders P&L
ODS
Balance
Sheet
Liquidity
Fund
Pricing
Gabriel
(FSA)
FSA
RRT
Tool
Calculation Engine
Performance
Reports
Liquidity MI
Risk Mgt
Reports
EBBS
Core BankingCorp Loans
RDS
DIH
RLS
CENTRY
Auto Loans
Collateral
Auto Loans
Credit Cards
Netting
Cash & Pooling
Collateral
AR/Billing
Pipeline
Collection
System
InterComp
Server
DMS
Hyperion
(Management)
Pipeline
Management
Reporting
Financial
Reporting
WB Cube
CB Cube
Magic Packs
Insight Packs
NF KPI Non Financials
PDW
People
Wise
HR BFS
(Access)
Static
Non PSGL entities
Management Allocations
Country Finance
Country Finance
IDS
Local Reg
Hyperion
(Statutory)
Centrally
managed BU’s
T1-T4
Journals
IFRS Cube
Reg, Tax,
WB, Others
Segmental
Cube
CMM Data
Collection
Templates
DCS
Budgets and
Forecast
GBS Cube
(Budgetting)
Financial Markets
Countries not in CDW
1
2
3
4
5
6
7
8
10
1111
1212
9
1313 1414
1515
1516
Manual Journal
Entries by
Country Finance
Group Finance
(Adjustments)
ALM
PSGL Cube
PAS 2
Management
Adjustments
Payroll
Non PSGL Info
PAS
UK MI
RFRAME
(UK)
MFU
MFU & Mapping
Tables
Mapping Tables
Templates
Adjustments
Downgrade
Provisions
Cheques
Private Banking
Collateral
CollateralAggregator
Local Reg
Reporting
CRM
Sales Pipeline
FDW
FDW
FDW
RSA
RSA
RSA
Other Other Other
CP
Spring
watch
ALM
ALM Reports
RSA
FDW
PAS
No prescriptive data modeling strategies available in OLAP environment

7. 7
Approach to Modeling Strategy Selection
 Types of Persistent Layers & Model
Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration - Business
Area Specific v/s Enterprise Model
 Vendor Models v/s In-house Models
 Selection of Hierarchical Structures
 Technology Considerations
 Business Coverage
 Business Entities
 Operational V/s Analytics
 Consumption Type (Raw,
Standardised, Derived)
 Metadata Requirements
 Timeliness
 History
Business Requirement Modeling Aspect
Selection of Right Modeling Strategy Depends on Business Drivers and Other Aspects
Hybrid
(3+2NF)
ROLAP/
MOLAP
Modeling Strategy
Star

8. 8
Modeling Levels – Top Down Approach
Conceptual
Model
TreasuryRisk Finance
Customer Product Employee Geography
Organization
Transactions
Channels
Subject
Area Model
Logical Model
Physical Model
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations

9. What are the Applicable Modeling Levels?
Subject Area
Level
Conceptual
Logical
Physical
SemanticODS Core DWStagingOLTP
Certain Persistent Layers do not require all levels of modeling.
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations

10. What Normalization Techniques are appropriate?
Subject Area
Level
Conceptual
Logical
Physical
SemanticODS Core DWStagingOLTP
3NF/2NF
(Hybrid)
Schema Type
Normalisation Type depends on the selected persistent layer
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
ROLAP/
MOLAP
Flat3NF
3NF/2NF
(Hybrid)
Most Disputed Modeling
Strategies

11. 11
What Level of Abstraction is appropriate?
Business
Area Specific
Highly
Abstracted
 Readily Usable with Business Specific Attribute
 Easy to understand
 Quick to develop and easy to manage
 Not suitable for Enterprise Data Level Integration
 Higher Abstraction accommodates more types
 General Practice is to use Vendor supplied models
 Most Industry Specific Models come with 2000+ entities and 10,000+
Attributes. Highly Flexible
 Suitable for Enterprise Level Data Warehouse.
 Harder to comprehend for business
 Requires extensive modeling effort
 Longer and Expensive to implement and affects Time to Market.
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations

12. 12
Example of Highly Abstract Models
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
Choose carefully whether abstracted models is the right choice to solve your analytic problem
Require several joins to
explain highly abstracted
concepts such as Locator
Example of Attributes in Enterprise Data Model
 Customer Domicile Country Code
 Incorporation Country Code

13. 13
Example of Business Area Specific Model
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
In 80% Cases, analytic needs require Business Area Specific problems to be solved
Example of Fixed Income Derivative Model
Abstraction Level is at Product Family/Asset Class
Extensions
anchor on main
entity

14. Financial Markets
14
Levels of Integration Requirements
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
Fixed Income
Flow Products Exotics
Bonds Money Market
Equities
Retail Banking
Commodities
Currencies
Corporate
Banking
Compliance
Department Level
Integration
Division Level
Integration
Determine the right level of data integration to get the right level of abstraction
Enterprise Level
Integration
FinanceRiskHR

15. 15
Vendor Models v/s In-house
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
- Use vendor models as reference and carve out applicable entities and customize it
- Consider usage of frameworks
In-House
 A good starting reference point
 Requires In-depth understanding
of definitions of generic concepts
 Discipline in customizing models
 Ambiguity in data mapping to
several thousand entities and
attributes
Vendor Models
 Requires high degree of
modeling skills .
 Solves business specific or
division level problems
 Can be customized to be flexible
enough for that level
 Easier to create business
specific model views.

16. 16
Hierarchical Structures
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
Recursive relationships do not work all the time. Consider using flattened hierarchy.

17. 17
Technology Considerations
 Types of Persistent Layers &
Model Levels
 Levels of Normalisation
 Levels of Abstraction
 Levels of Integration -
Business Area Models v/s
Enterprise Model
 Vendor Models v/s In-house
Models
 Selection of Hierarchical
Structures
 Technology Considerations
Appliance vendors emerging strong in OLAP environment and many banks investing in
building enterprise level warehouse
 Certain Database Appliances designed to work well with
abstracted models but can be expensive
 Highly normalized physical models difficult to implement. Most
turn into hybrid of 3NF and 2NF
 Constraints can be on number of joins and length of composite
surrogate keys in dimensional models
 BI tool adaptability to consumption layer

18.  Efficient Information Delivery Model
 Consistent data model views along the food chain
 Reduction in development costs
 Qualitative Data (usefulness of data)
 Reduction of redundancies in data processing
 Speed to market
 Elimination of manual processes
18
Value Proposition in Selecting Right Data Modeling Strategy

19. 19
References
 IBM Corporation
 Teradata Corporation
 Kimball University – Dr. Ralph Kimball
 Information Management Magazine – September 2002