CASE STUDY: Data Quality

Continuous Improvement

Processes at ABN Amro

1

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

Agenda

PART 1 – THE ISSUES

1.1 - Data Governance challenge in a simple

theoretical model

1.2 - Data Governance challenge in the real world

2

PART 2 – ADAPT THE ORGANISATION

2.1 Data Quality Management Framework:

Basic Principles

2.2. Data Quality Organizational Framework

2.3. Relationships among Organizational layers

2.4 Issue Management

2.5 Cost of non quality in Basel 2

PART 3 – ENABLE THE ORGANISATION

3.1 Major data quality dimensions

3.2 7 modules to make DQ come true

3.3 1 additional module to make FORECASTING

come true

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

3

PART 2 – ADAPT THE ORGANISATION

2.1 Data Quality Management Framework:

Basic Principles

2.2. Data Quality Organizational Framework

2.3. Relationships among Organizational layers

2.4 Issue Management

2.5 Cost of non quality in Basel 2

PART 3 – ENABLE THE ORGANISATION

3.1 Major data quality dimensions

3.2 7 modules to make DQ come true

3.3 1 additional module to make FORECASTING

come true

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 !

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 !

4

Central Chains

Operational

systems

A

B C D E F

t1 tranfer

t2 storing t3 extraction t4 preparation t5 calculation

Gt6 reporting

Real W

orl

d

1.2 - Data Governance challenge in the real world

A

B C D E F

t1 transfer

t2 storing t3 extraction t4 preparation t5 calculation

G

t6 reporting

D’ E’ F’

t3’ extraction t4’’ preparation t5’ calculation

G’

t6’ reporting

D’’ E’’ F’’

T3’’ extraction T4’’ preparation T5’’ calculation

G’’

T6’’ reporting

H I J F L

t2 storing t3 extraction t4 preparation t5 calculation

M

t6 reporting

J’ F’ L’

t3’ extraction t4’’ preparation t5’ calculation

M’

t6’ reporting

Complexity is exponential

t1 transfer

Reality is even more complex Duplication of stores

Many chains run in parallel

Reconciliations between chains

Human factor

Re runs

Errors and manual corrections

...

5

2 types of risks

Internal risk : availabiliy of right information for management decisions

External risk : inconsistent reporting to third parties

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

6

PART 2 – ADAPT THE ORGANISATION

2.1 Data Quality Management Framework:

Basic Principles

2.2. Data Quality Organizational Framework

2.3. Relationships among Organizational layers

2.4 Issue Management

2.5 Cost of non quality in Basel 2

PART 3 – ENABLE THE ORGANISATION

3.1 Major data quality dimensions

3.2 7 modules to make DQ come true

3.3 1 additional module to make

FORECASTING come true

8

2.2 - Data Quality Organizational Framework

8

Central

governance

(a.o. DQCC)

BAU, “domains”

9

2.3 - Relationships among Organizational layers

9

DQ

Competence

Center

DQ Management

Center

DQ

Operations

EVP of Finance

organization

(chairman of

DQMC)

Head DQCC

(chairman

DQOC)

Relevant EVP and

SVPs of BUs

Relevant VPs

of BUs

DQ people within

BUs / domains

• DQCC reports to EVP

• Provides support funcion for DQMC

(agenda, minutes)

Business

process

chain meetings

Decisions of DQMC are

communicated to DQOC or

product chain meetings

SVP, relevant

BAU people

CFO

(lead of MB)

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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2.5 - Cost of non quality in Basel 2

DQ versus the calculated, reported and real RWA/EC

Time

RWA

or EC

III. Real RWA / EC

I. Calculated RWA / EC

without corrections

II. Reported RWA / EC with current

workarounds

Can only be

realised by

means of a

infrastructural

improvement.

Is already realised,

but not structural

and opaque.I.

II.

III.

0

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.

Goal: Aligning of calculated and reported RWA/EC with the real RWA/EC

Go

al D

Q in

frastr

uctu

re

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

12

PART 2 – ADAPT THE ORGANISATION

2.1 Data Quality Management Framework:

Basic Principles

2.2. Data Quality Organizational Framework

2.3. Relationships among Organizational layers

2.4 Issue Management

2.5 Cost of non quality in Basel 2

PART 3 – ENABLE THE ORGANISATION

3.1 Major data quality dimensions

3.2 7 modules to make DQ come true

3.3 1 additional module to make

FORECASTING come true

3.1 - Major data quality dimensions

Quarter - 1Month - 2

Month - 1

Central Chains

Operational

systems

A

B C D E F

G

Re

al

wo

rld

This Month

// Chains

D’ E’ F’

Accuracy

Consistency Intra-chain

Completeness

Consistency Inter-chains

Consistency Cross-Months

Integrity & Bus. Rules

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CENTRAL CHAINS

CHAIN 1

CHAIN 2

CHAIN 3

COLLECTOR

LOCAL

DQ INDUSTRIAL FRONT OFFICE

DQ INDUSTRIAL BACK OFFICE

PREDICTION TOOL

PRODUCTION

CUBE

(AS IS)

DQ PREVENTION, ANALYSIS & CONTROL

DQ IMPROVEMENT & COMMUNICATION

DQ

SOURCED

DATA

DQ

OPERATIONAL

SYSTEMS

THERMOMETERS & KPI’s1 2

5

3

4

6

7

Reporting layer

3.2 – 7 modules to make DQ come true

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Module 1: Launch data

quality actions in the local

systems

Module 2: Measure the data

quality sourced in the

collector & feedback to the

sources

Module 3 : Define common

measures (thermometers &

KPI’s) across the chain(s)

Module 4: Create an

aggregated multi-sources

/multi-periods reporting

environment

Module 5: Challenge the

results produced in the

chains

Module 6: Industrialize the

production of the

deliverables (reports,

referential, distribution)

Module 7: Industrialize the

DQ analysis & follow-up of

issues

CENTRAL CHAINS

CHAIN 1

CHAIN 2

CHAIN 3

COLLECTOR

LOCAL

DQ INDUSTRIAL FRONT OFFICE

DQ INDUSTRIAL BACK OFFICE

PREDICTION TOOL

PRODUCTION

CUBE

(AS IS)

DQ PREVENTION, ANALYSIS & CONTROL

DQ IMPROVEMENT & COMMUNICATION

SIMULATIONS

STRESS TESTSSIMULATION

CUBEs

(FUTURE)SENSITIVITY

DQ

SOURCED

DATA

DQ

OPERATIONAL

SYSTEMS

THERMOMETERS & KPI’s

FORECASTING

1 2

5

3

4

886

7

Reporting layer

3.3 – 1 additional module to make FORECASTING come true

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Module 8 : Evaluate

the impact of scenarios

based on the evolution

of the parameters

(stress, simulations,

senticity analysis..) &

Store results

Module 7’ upgrade:

Industrialize the DQ

information AND

forecasting analysis

Effective DQ can

help organisation

to forecast their

future potential

states.

Thanks to…

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