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Data Quality Challenges for Financial Institutions and large corporations

BI-Community.org Seminar (V1_6)

30/4/2009, Leuven A ÂŤ BU Corporate Reporting Âť presentation Thibaut De Vylder, Nicolas Sayde, Quentin Deschepper

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DQ in newspapers

2


DQ in newspapers

3


Cost of DQ in perspective In billion US $

1200 1000 800 600 400

200 0

2009

2010

2011

Estyimated cost of DQ problems for U.S. Businesses

600

600

600

Obama's Plan Jan 2009 Federal Spending

1000

Madoff's Fraud

50

Belgium PIB (2007 base in US$)

452

(*) Source:Data Warehousing Institude, Data Quality and and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data, http://www.dw-institute.com/

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Objective & Experience 

Objective: 

Present the DEPFAC “data governance” and “data quality” approaches.

Banking environment relevant experience: Regulatory compliance (Basel 2) & corporate reporting   

5 billions of data sourced monthly representing hundred of billions in assets & liabilities Chains supported by old and new systems Non homogeneous IT infrastructure (Mainframe / Server…) Overlapping responsabilities

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Data Governance definition “Data Governance is a system of decision rights and accountabilities for informationrelated processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.� DGI - Gwen Thomas

Reliable information for right decisions

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Challenge in assembly lines

2nd hand paper Paste transformation

Wood

Paper

End Product transformation

Paper paste

Boxes

Water

Controls on raw material

Defects

Controls on intermediate products Controls on processes

Controls on final products

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Management challenge in Financial Institutions Process for creating information

Management Report

data

Management decision

Executives base their management decision on information received

ď ľ

ď ľ

How are data proceeded, checked and cross checked ? Are decisions taken on the basis of reliable management reports ?

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A Data Governance challenge

Real World

Data are transferred, stored, extracted, prepared, calculated and reconciled several times before being reported? A long and risky journey !

Operational systems

A

Central Chains

t1 tranfer

t2 storing

B

C

t3 extraction

D

t4 preparation

E

t5 calculation

F

t6 reporting

G

Information G in report depends on succession of embedded tranformations = t6(t5(t4(t3(t2(t1(data in operational system A))))))) ďƒ  20 to 30% of data may be lost or deteriorated during the process ! 9


A Data Governance challenge A

     

t1 tranfert t2 storing

Reality is even more complex Duplication of stores Many chains in parallel High risk reconciliations between chains Human factor Re runs Errors and corrections

B

t3 extraction t4 preparation t5 calculation t6 reporting

C

D

E

F

G

t3’ extraction t4’’ preparationt5’ calculation t6’ reporting

D’ T3’’ extraction t1 tranfert

H

I

F’

G’

T4’’ preparationT5’’ calculationT6’’ reporting

D’’ t2 storing

E’ E’’

F’’

G’’

t3 extraction t4 preparation t5 calculation t6 reporting

J

F

L

M

t3’ extraction t4’’ preparationt5’ calculation t6’ reporting

J’

F’

L’

M’

Real chains look more like this

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Some Data quality dimensions

Real world

Accuracy

Completeness

Integrity & Bus. Rules

Operational systems

A

Central Chains

B

C

D

E

F

G // Chains

D’

E’

F’ This Month Month - 1 Month - 2 Quarter - 1

Consistency

Consistency

Consistency

Intra-chain

Inter-chains

Cross-Months 11


A Data Quality Factory besides the chain

Local

DQ source data

Central Thermometers & KPI’s

Process Quality

Stress, Sensitivity, Simulation…

Prod Cube

DQ Factory

Back Office

Prevention

Front Office

Analysis

Stress Cube Control

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DQ Framework for DQ continuous improvement

13


Planning & Resources

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Data Governance applications 

Basel 2 Chains  

Already operational (Europe) Being implemented (US, Middle east)

Solvency 2 Chains

Corporate reporting chain in Financial Institutions  

Banks Insurance companies Regulators

Any « high data volume » reporting chains: telecom industry, postal services, invoices, travel reservation systems, hospitals… 

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Conclusion Large corporate reporting chains must be supported by a Data Quality factory 

One € invested in Data Quality improvement has a greater ROI than any other investment (such as adding additional pieces of 

software, redevelopements…)

When budgets are scarce, investment in Data Quality is the best investment strategy 

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Deployments Factory Services 

Back and Front Office implementation  

DQ Factory & DQ Framework  

Reconciliate the past and the present (data and processes) Continuous DQ improvement process

Stress Factory 

Automated production of reporting & data quality information Automated analysis and communication

Understand the future (through simulations, stress, sensitivity analysis, capital allocation…)

Bypass  

« Do things differently » Re-write the whole chain (process and data)  in an integrated and homogeneous environment  with fixed price implementation,  fast delivery  and reduced operating costs

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Why Deployments Factory? 

Unique culture of Data Quality management

Expertise and experience   

in Financial Industry with Risk, Finance, IT and various Business Lines In complex non homogenous system environments

Able to deliver short term

International & mobile consultants

methodological & pragmatic approach

« Off the shelf » tools & processes: no additonal IT investment required 

Limited budgets for great returns…. … the Best ROI you can get ! 19


APPENDIXES   

Appendix 1 : Sample of DQ issues Appendix 2 : DQ issue and challenge Appendix 3 : DQ Quotes

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AP1 : Sample of DQ issues for Basel 2 Syntaxic Inaccuracy Completeness

Semantic Inaccuracy Counter party ID

Name

Counter party Type

Exposure Clasee

EAD

PD

LGD

Start Maturity

End Maturity

123

SME Trilili

SME

Mortgage

100

1%

NULL

2008

2011

124

Company Coca Coli INC

CORPORAT E

Corporate Fin

120

NULL

30%

2010

2012

125

Company HP INC

SME

Corporate Fin

1000000

NULL

0.45

2007

2024

126

Trululu SME

SME

Mortgage

10000

110%

2500

2007

2024

127

Mr John

INDIVIDUAL

Personal Loan

1000

2%

45%

2006

Intra-relation Integrity

Inter-relation Integrity 21


AP2 : DQ issue & challenge defined

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AP3 : DQ quotes

Source: http://www.dqguide.com

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Bi-Community.org presentation on Dataquality