Paper presented in the London Data Governance conference on 21st of April 2010 By Theo Kester, DQ Manager ABN AMRO, theo.kester@nl.abnamro.com Thibaut De Vylder, CEO Deployments Factory

1. CASE STUDY: Data Quality
Continuous Improvement
Processes at ABN Amro
By
Theo Kester, DQ Manager ABN AMRO, theo.kester@nl.abnamro.com
Thibaut De Vylder, CEO Deployments Factory, tdv@deploymentsfactory.com
DG2010, London, 21st of April 2010
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2. Agenda
PART 1 – THE ISSUES
1.1 - Data Governance challenge in a simple
theoretical model
1.2 - Data Governance challenge in the real world
PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION
2.1 Data Quality Management Framework: 3.1 Major data quality dimensions
Basic Principles
3.2 7 modules to make DQ come true
2.2. Data Quality Organizational Framework
3.3 1 additional module to make FORECASTING
2.3. Relationships among Organizational layers come true
2.4 Issue Management
2.5 Cost of non quality in Basel 2
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3. PART 1 – THE ISSUES
PART 1 – THE ISSUES
1.1 - Data Governance challenge in a simple
theoretical model
1.2 - Data Governance challenge in the real world
PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION
2.1 Data Quality Management Framework: 3.1 Major data quality dimensions
Basic Principles
3.2 7 modules to make DQ come true
2.2. Data Quality Organizational Framework
3.3 1 additional module to make FORECASTING
2.3. Relationships among Organizational layers come true
2.4 Issue Management
2.5 Cost of non quality in Basel 2
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4. 1.1 - Data Governance challenge in a simple theoretical model
Data are transferred, stored, extracted,
prepared, calculated and reconciled several
times before being reported. A long and risky
journey !
Operational
Real World
systems
t1 tranfer Central Chains
A
t2 storing t3 extraction t4 preparation t5 calculation
t6 reporting
B C D E F G
Information presented in report G depends on succession of embedded transformations
Quality of G = Quality of [t6(t5(t4(t3(t2(t1(data in operational system A)))))))]
 Substantial part of data may be lost or deteriorated during the process !
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5. 1.2 - Data Governance challenge in the real world
Reality is even more complex A t1 transfer
 Duplication of stores
t2 storing t3 extraction t4 preparation t5 calculation t6 reporting
 Many chains run in parallel
B C D E F G
 Reconciliations
t3’ extraction t4’’ preparation t5’ calculation t6’ reporting
between chains
 Human factor D’ E’ F’ G’
 Re runs t1 transfer
T3’’ extraction T4’’ preparation T5’’ calculation T6’’ reporting
 Errors and manual D’’ E’’ F’’ G’’
corrections t2 storing t3 extraction t4 preparation t5 calculation t6 reporting
 ... H I J F L M
t3’ extraction t4’’ preparation t5’ calculation t6’ reporting
J’ F’ L’ M’
Complexity is exponential
2 types of risks
 Internal risk : availabiliy of right information for management
decisions
 External risk : inconsistent reporting to third parties
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6. PART 2 – ADAPT THE ORGANISATION
PART 1 – THE ISSUES
1.1 - Data Governance challenge in a simple
theoretical model
1.2 - Data Governance challenge in the real world
PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION
2.1 Data Quality Management Framework: 3.1 Major data quality dimensions
Basic Principles
3.2 7 modules to make DQ come true
2.2. Data Quality Organizational Framework
3.3 1 additional module to make
2.3. Relationships among Organizational layers FORECASTING come true
2.4 Issue Management
2.5 Cost of non quality in Basel 2
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7. 2.2 - Data Quality Organizational Framework
Central
governance
(a.o. DQCC)
BAU, “domains”
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8. 2.3 - Relationships among Organizational layers
CFO
(lead of MB)
EVP of Finance
organization DQ Management
(chairman of Center Relevant EVP and
DQMC) SVPs of BUs
• DQCC reports to EVP Decisions of DQMC are
• Provides support funcion for DQMC communicated to DQOC or
(agenda, minutes) product chain meetings
DQ Head DQCC DQ Business
Competence (chairman Operations process
DQOC) chain meetings
Center Relevant VPs SVP, relevant
of BUs BAU people
DQ people within
BUs / domains
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9. 2.4 - Issue Management
 Importance:
– Data Quality issues must be fixed as early in the data logistical chain as possible as the graph below
will show
– Studies prove that the costs grow exponentially while data progress through the data logistical chain
 Goal: solutions, not issues
 Process:
As Data Quality is being analysed and checks are performed issues will be identified. The issues are
addressed by the Issue Management Team (part of DQCC) in cooperation with the domains. All issues are
given a priority, a deadline and addressed to an action owner.
 Tools:
– Formalized Issue Management process
– Quality Centre: A tool in which DQ issues are logged and managed
– Prioritisation Tool: A tool which is used to prioritise the DQ issues
– Issue Management Process Guideline: A guideline for the domains how they could set up their own
Issue Management Framework
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10. 2.5 - Cost of non quality in Basel 2
DQ versus the calculated, reported and real RWA/EC
I. Calculated RWA / EC
without corrections Is already realised,
RWA but not structural
or EC and opaque.
Goal DQ infrastructure
I.
Can only be
II. realised by
II. Reported RWA / EC with current means of a
workarounds infrastructural
improvement.
III. III. Real RWA / EC
I. The monthly calculated RWA/EC is volatile; This is not the result of a changed risk profile, but due to Data Quality defects
as a result of changes in systems, reference tables and changes in the business and so on.
II. Many of the irregularities are manually corrected, which results in a more stable monthly reported RWA/EC. Due to the
“defaulting” rules is line II lower than line I. However these corrections are often not robust, opaque and could lead to
incompliance.
III. The real RWA only changes as a result of changes in the risk profile of ABN AMRO. The real RWA is lower than the
reported RWA, because many Data Quality issues can only be solved by means of changes in the system- and IT-
infrastructure and not by manual corrections.
0 Time
Goal: Aligning of calculated and reported RWA/EC with the real RWA/EC
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11. PART 3 – ENABLE THE ORGANISATION
PART 1 – THE ISSUES
1.1 - Data Governance challenge in a simple
theoretical model
1.2 - Data Governance challenge in the real world
PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION
2.1 Data Quality Management Framework: 3.1 Major data quality dimensions
Basic Principles
3.2 7 modules to make DQ come true
2.2. Data Quality Organizational Framework
2.3. Relationships among Organizational layers 3.3 1 additional module to make
FORECASTING come true
2.4 Issue Management
2.5 Cost of non quality in Basel 2
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12. 3.1 - Major data quality dimensions
Accuracy Completeness Integrity & Bus. Rules
Operational
Real world
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
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13. 3.2 – 7 modules to make DQ come true
LOCAL  Module 1: Launch data
CENTRAL CHAINS
quality actions in the local
COLLECTOR CHAIN 1 systems
 Module 2: Measure the data
CHAIN 2
quality sourced in the
collector & feedback to the
CHAIN 3
sources
DQ DQ
 Module 3 : Define common
OPERATIONAL SOURCED measures (thermometers &
SYSTEMS DATA
1 2
3
THERMOMETERS & KPI’s
PRODUCTION KPI’s) across the chain(s)
CUBE
PREDICTION TOOL (AS IS)  Module 4: Create an
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5 aggregated multi-sources
/multi-periods reporting
environment
6  Module 5: Challenge the
DQ INDUSTRIAL BACK OFFICE results produced in the
Reporting layer chains
 Module 6: Industrialize the
DQ PREVENTION, ANALYSIS & CONTROL production of the
deliverables (reports,
referential, distribution)
 Module 7: Industrialize the
DQ IMPROVEMENT & COMMUNICATION
DQ analysis & follow-up of
7 issues
DQ INDUSTRIAL FRONT OFFICE
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14. 3.3 – 1 additional module to make FORECASTING come true
LOCAL CENTRAL CHAINS  Module 8 : Evaluate
the impact of scenarios
COLLECTOR CHAIN 1
based on the evolution
of the parameters
CHAIN 2
(stress, simulations,
CHAIN 3
senticity analysis..) &
Store results
DQ
OPERATIONAL
DQ
SOURCED
 Module 7’ upgrade:
SYSTEMS
2
DATA Industrialize the DQ
1 THERMOMETERS & KPI’s
3 PRODUCTION information AND
CUBE
PREDICTION TOOL (AS IS) forecasting analysis
4
5
SIMULATIONS
SIMULATION
STRESS TESTS CUBEs
(FUTURE)
6 SENSITIVITY
8
DQ INDUSTRIAL BACK OFFICE 8
Reporting layer
DQ PREVENTION, ANALYSIS & CONTROL Effective DQ can
help organisation
FORECASTING
to forecast their
DQ IMPROVEMENT & COMMUNICATION future potential
7 states.
DQ INDUSTRIAL FRONT OFFICE
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15. Thanks to…
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