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DQ in fianncial institutions: slide to support the presentation made by Thibaut De Vylder on 30/4/2009 in Leven, Belgium. Support from Nicolas Saydé and Thibaut De Vylder
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1
BI-Communityorg
Seminar
(V1_6)
3042009 Leuven
A laquo BU Corporate Reporting raquo presentation
Thibaut De Vylder Nicolas Sayde
Quentin Deschepper
Data Quality
Challenges for
Financial
Institutions and
large
corporations
2
DQ in newspapers
3
DQ in newspapers
4
2009 2010 2011
Estyimated cost of DQ
problems for US
Businesses600 600 600
Obamas Plan Jan 2009
Federal Spending1000
Madoffs Fraud 50
Belgium PIB
(2007 base in US$)452
0
200
400
600
800
1000
1200
In b
illio
n U
S $
Cost of DQ in perspective
() SourceData Warehousing Institude Data Quality and and the Bottom Line Achieving Business Success through a Commitment to High Quality Data httpwwwdw-institutecom
5
Objective amp Experience
Objective
Present the DEPFAC ldquodata governancerdquo and ldquodata qualityrdquo approaches
Banking environment relevant experience
Regulatory compliance (Basel 2) amp corporate reporting
5 billions of data sourced monthly representing hundred of billions in assets amp liabilities
Chains supported by old and new systems
Non homogeneous IT infrastructure (Mainframe Serverhellip)
Overlapping responsabilities
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
2
DQ in newspapers
3
DQ in newspapers
4
2009 2010 2011
Estyimated cost of DQ
problems for US
Businesses600 600 600
Obamas Plan Jan 2009
Federal Spending1000
Madoffs Fraud 50
Belgium PIB
(2007 base in US$)452
0
200
400
600
800
1000
1200
In b
illio
n U
S $
Cost of DQ in perspective
() SourceData Warehousing Institude Data Quality and and the Bottom Line Achieving Business Success through a Commitment to High Quality Data httpwwwdw-institutecom
5
Objective amp Experience
Objective
Present the DEPFAC ldquodata governancerdquo and ldquodata qualityrdquo approaches
Banking environment relevant experience
Regulatory compliance (Basel 2) amp corporate reporting
5 billions of data sourced monthly representing hundred of billions in assets amp liabilities
Chains supported by old and new systems
Non homogeneous IT infrastructure (Mainframe Serverhellip)
Overlapping responsabilities
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
3
DQ in newspapers
4
2009 2010 2011
Estyimated cost of DQ
problems for US
Businesses600 600 600
Obamas Plan Jan 2009
Federal Spending1000
Madoffs Fraud 50
Belgium PIB
(2007 base in US$)452
0
200
400
600
800
1000
1200
In b
illio
n U
S $
Cost of DQ in perspective
() SourceData Warehousing Institude Data Quality and and the Bottom Line Achieving Business Success through a Commitment to High Quality Data httpwwwdw-institutecom
5
Objective amp Experience
Objective
Present the DEPFAC ldquodata governancerdquo and ldquodata qualityrdquo approaches
Banking environment relevant experience
Regulatory compliance (Basel 2) amp corporate reporting
5 billions of data sourced monthly representing hundred of billions in assets amp liabilities
Chains supported by old and new systems
Non homogeneous IT infrastructure (Mainframe Serverhellip)
Overlapping responsabilities
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
4
2009 2010 2011
Estyimated cost of DQ
problems for US
Businesses600 600 600
Obamas Plan Jan 2009
Federal Spending1000
Madoffs Fraud 50
Belgium PIB
(2007 base in US$)452
0
200
400
600
800
1000
1200
In b
illio
n U
S $
Cost of DQ in perspective
() SourceData Warehousing Institude Data Quality and and the Bottom Line Achieving Business Success through a Commitment to High Quality Data httpwwwdw-institutecom
5
Objective amp Experience
Objective
Present the DEPFAC ldquodata governancerdquo and ldquodata qualityrdquo approaches
Banking environment relevant experience
Regulatory compliance (Basel 2) amp corporate reporting
5 billions of data sourced monthly representing hundred of billions in assets amp liabilities
Chains supported by old and new systems
Non homogeneous IT infrastructure (Mainframe Serverhellip)
Overlapping responsabilities
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
5
Objective amp Experience
Objective
Present the DEPFAC ldquodata governancerdquo and ldquodata qualityrdquo approaches
Banking environment relevant experience
Regulatory compliance (Basel 2) amp corporate reporting
5 billions of data sourced monthly representing hundred of billions in assets amp liabilities
Chains supported by old and new systems
Non homogeneous IT infrastructure (Mainframe Serverhellip)
Overlapping responsabilities
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
6
Data Governance definition
ldquoData Governance is a system of decision rights and accountabilities for information-related processes executed according to agreed-upon models which describe who can take what actions with what information and when under what circumstances using what methodsrdquo
DGI - Gwen Thomas
Reliable information for right decisions
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
7
2nd hand paper
Wood
Water
Paper paste
Paper
Boxes
Paste transformation End Product transformation
Defects
Controls on processes
Controlson raw
material
Controls on intermediate products Controlson final
products
Challenge in assembly lines
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
8
Management challenge in
Financial Institutions
data
Process for
creating information
Management
decision
Management
Report
How are data proceeded checked and cross checked
Are decisions taken on the basis of reliable management reports
Executives base their management
decision on information received
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
9
A Data Governance challenge
Central Chains
Operational
systems
A
B C D E F
t1 tranfer
t2 storing t3 extraction t4 preparation t5 calculation
Gt6 reporting
Re
al W
orld
Data are transferred stored extracted prepared
calculated and reconciled several times before being reported A long and risky journey
Information G in report depends on succession of embedded tranformations
= t6(t5(t4(t3(t2(t1(data in operational system A)))))))
20 to 30 of data may be lost or deteriorated during the process
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
10
A Data Governance challenge
A
B C D E F
t1 tranfert
t2 storing t3 extraction t4 preparation t5 calculation
G
t6 reporting
Drsquo Ersquo Frsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Grsquo
t6rsquo reporting
Drsquorsquo Ersquorsquo Frsquorsquo
T3rsquorsquo extraction T4rsquorsquo preparationT5rsquorsquo calculation
Grsquorsquo
T6rsquorsquo reporting
H I J F L
t2 storing t3 extraction t4 preparation t5 calculation
M
t6 reporting
Jrsquo Frsquo Lrsquo
t3rsquo extraction t4rsquorsquo preparationt5rsquo calculation
Mrsquo
t6rsquo reporting
Real chains look more like this
t1 tranfert
Reality is even more complex
Duplication of stores
Many chains in parallel
High risk reconciliations between chains
Human factor
Re runs
Errors and corrections
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
11
Some Data quality dimensions
Quarter - 1Month - 2
Month - 1
Central Chains
Operational
systems
A
B C D E F
G
Real w
orld
This Month
Chains
Drsquo Ersquo Frsquo
Accuracy
Consistency Intra-chain
Completeness
Consistency Inter-chains
Consistency Cross-Months
Integrity amp Bus Rules
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
12
A Data Quality Factory besides the chain
DQ FactoryBack Office
Local Central Thermometers amp KPIrsquosDQ
source
data
Process Quality
Stress Sensitivity
Simulationhellip
Prod
Cube
Stress
Cube
Front Office
Prevention Analysis Control
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
13
DQ Framework for DQ continuous
improvement
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
14
Planning amp Resources
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
16
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 laquo high data volume raquo reporting chains telecomindustry postal services invoices travel reservationsystems hospitalshellip
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
17
Conclusion
Large corporate reporting chains must besupported by a Data Quality factory
One euro invested in Data Qualityimprovement has a greater ROI than anyother investment (such as adding additional pieces of software redevelopementshellip)
When budgets are scarce investment in Data Quality is the best investment strategy
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
18
Deployments Factory Services
Back and Front Office implementation Automated production of reporting amp data quality information
Automated analysis and communication
DQ Factory amp DQ Framework Reconciliate the past and the present (data and processes)
Continuous DQ improvement process
Stress Factory Understand the future (through simulations stress sensitivity
analysis capital allocationhellip)
Bypass laquo Do things differently raquo
Re-write the whole chain (process and data)
in an integrated and homogeneous environment
with fixed price implementation
fast delivery
and reduced operating costs
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
19
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 amp mobile consultants
methodological amp pragmatic approach
laquo Off the shelf raquo tools amp processes no additonal IT investmentrequired
Limited budgets for great returnshellip
hellip the Best ROI you can get
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
20
APPENDIXES
Appendix 1 Sample of DQ issues
Appendix 2 DQ issue and challenge
Appendix 3 DQ Quotes
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
21
AP1 Sample of DQ issues for Basel 2
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 045 2007 2024
126 Trululu SME SME Mortgage 10000 110 2500 2007 2024
127 Mr John INDIVIDUAL Personal
Loan
1000 2 45 2006
Syntaxic Inaccuracy
Completeness
Intra-relation Integrity
Inter-relation Integrity
Semantic Inaccuracy
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
22
AP2 DQ issue amp challenge defined
23
AP3 DQ quotes
Source httpwwwdqguidecom
23
AP3 DQ quotes
Source httpwwwdqguidecom
