1) The document discusses data governance design options for large enterprises, including a line organization, staff organization per business unit, shared service center, and externalization models. 2) It provides two examples of how companies have implemented data governance: a high tech company that used a central function and a chemical company that used a shared service center for governance and operational responsibility. 3) Key principles for an effective governance design are discussed, including being global, shared, governing, service-oriented, managed, and empowered. The evolution from a shared service to outsourced data management processes is also covered.

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2. University of St. Gallen, Institute of Information Management
Evolution of Data Governance Excellence in Large
Enterprises: Lessons Learned and Strategic
Directions
Andreas Reichert
London, April 17th, 2013

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1) Actual und former partner companies since November 2006
2) Institute of Information Management at the University of St. Gallen
Approach  Design of solutions (e.g. architecture designs, models, methods,
prototypes) supporting a quality oriented management of corporate data
 Set up of community for exchange of best practices for master data and
data quality management
Supporting
companies1
Organization  Consortium consisting of IWI-HSG2 and partner companies
 Joint creation of solution within workshops (5x per year) and projects
 Organization an management by IWI-HSG, since 2012 jointly with BEI
St. Gallen
The research of the Competence Center Corporate Data Quality (CDQ)
is based on interaction with companies listed below

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Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options

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Data Governance is necessary in order to meet several strategic business
requirements
 Legal and regulatory
requirements
 Contractual
obligations
Risk Management
 “Single Point of Truth”
 Standardized reports
and KPIs
Corporate
Reporting
 Business process
harmonization
 “End-to-end” business
processes
Global Business
Processes
 360°view on
customers
 Hybrid products
Customer-centric
business models
 Integration of acquired
businesses
 Data due diligence
Mergers &
Acquisitions
 IT consolidation (“do
more with less”)
 Flexible architectures
Complexity
management
1 2
3 4
5 6

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Business impact of data quality?
A product data example, consumer goods industry
GTIN: Global Trade Item Number, standardized by Global Standards One (GS1, www.gs1.org)
1
2
3
4
5
2
 To add additional filling may be reasonable with transparent bottles
 But: Not maintaining changed gross weight my cause wrong packing
Capacity2
 Wrong shelf planning at customers (retail) due to inaccurate measures
 Repacking of pallets due to inaccurate gross weights
Logistic
Data
1
 Flawed products due to too high or too low temperature during transport
 Temperature tolerance depends on product formula (bill of material)
Temperature
for transportation
3
 Different formats in several countries
 No globally standardized but changing formats (e.g. date, duration)
Format of
expiry date
4
 Wrong GTINs may cause complaints and compensations
 Product changes may require a new GTIN
 GTIN allocation depends on global and local guidelines
GTIN5
Data quality is a prerequisite for correct
product information and supply chain efficiency

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Complexity drivers indicate a strong need for Data Governance
CDQ
Data volumes
RFID, customer loyalty programs
etc.
Global processes
Multilingualism, “Follow the sun“-
principle etc.
“Taylorism”
Segregation of data creation and
data use
Constant Change
M&A, “Divestments”, Change
Management
“Hyper-connectivity”
New, external data sources, Data-
Supply-chains etc.
Size
Revenue Nestlé 2008: 110 billion CHF
Federal budget CH 2008: 57 billion CHF

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Defining Data Governance
 Data governance aims at the identification of decision rights and roles to
facilitate a consistent, company-wide behavior in the use of corporate data
 Also, data governance allocates responsibilities to roles to ensure the
execution of assigned decision rights
 Data governance results in company-wide standards, guidelines and
methodologies for creation and use of corporate data
Management of sustainable
and reliable high quality master data

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The typical evolution of data quality over time in companies shows a
strong need for action
Legend: Data quality pitfalls
(e. g. Migrations, Process
Touch Points, Poor
Management Reporting Data.
Data Quality
Time
Project 1 Project 2 Project 3
 No risk management possible
 Impedes planning and controlling of budgets and resources
 No targets for data quality
 Purely reactive - when too late
 No sustainability, high repetitive project costs (change requests, external consulting etc.)

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The CDQ Framework – Success Factors for effective Data Governance
Strategy
Organization
System
CDQ Controlling
Applications for CDQ
Corporate Data Architecture
CDQ
Organization
CDQ Processes and
Methods
CDQ Strategy
lokal global
Mandate
Strategy document
Value management
Roadmap
KPI system
Measurement process
Dimensions of data
quality
Data Governance
Roles and
responsibilities
Change management
Standards & Guidelines
Data life cycle
management
Metadata management
Methods and
processes
Conceptual corporate
data model
Distribution architecture
Data storage
architecture
Software for corporate
data quality
management
As-is and To-be-
planning of application
system support

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Design options for implementing Data Governance
Key: BU: Business Unit; SSC: Shared Service Center Line Organization (Sold Line)
Dotted Line
Coordination via SLA
Local Function/Staff Organization per BU Central Function
Shared Service Center Externalization
Group Level
BU BU BU BU
Group Level
BU BU BU
Central
Function
Group Level
BU BU BU
External
Party
Group Level
BU BU BU SSC
1 2
3 4

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Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options

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Example 1 - High Tech Industry
Business drivers for Data Governance
 Changing business model
 From product & system business to solution orientation
 Focus on indirect business models
 Trend to managed services
 Higher competition leads to higher cost pressure
 Need to simplify and harmonize processes and IT
 Need to simplify and strengthen the organization
 Changes in the market require high flexibility
 Reduce the complexity in products and services
 Enable rapid merger and acquisitions
Accurate and trustful master data are the basis for business processes and
enable to react flexible on changes!

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The need for high quality master data for the new business environment
to GRID
The GRID (Global Responsibility for Integrated Data) initiative
aims at setting up a global Enterprise Data Management (EDM)
consisting of governance (organizational structures, roles,
responsibilities, tasks), processes (data management, business
processes) as well as the information technology
(systems, interfaces, automation).
GRID has the mission to secure the global consistency of
master data – product, product information, supplier, customer - in
order to smoothly operate the business.

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Why do we need global master data Governance?
BusinessprocessesCorporate
Enterprise Data Management is the backbone of the business processes!
Global planning capabilities & integration of 3rd party products
Efficient marketing and e-commerce enablement (e2e)
Clean & full integration of service business into MDM
Spend transparency and volume consolidation
SCM
Mark / Sales
Service
Purchasing
Information
Compliance
Projects
High reporting quality and timely reporting
Traceability of products and export compliance
Acceleration of project delivery and reduction of efforts

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Processes are defined on strategic, governance, and operational level
EDM Life Cycle
Management
EDM Life Cycle
Management
Customer
EDM Life Cycle
Management
EDM Life Cycle
Management
EDM Strategy
1
EDM Standards
& Guidelines
Develop
vision
Define
EDM
roadmap
Develop
com./change
strategy
Set up
organization
responsib.
Align with
business/IT
strategy
EDM Quality-
Assurance
Define
measure-
ment metrics
Define
quality
targets
Define
reporting
structures
Monitor &
report
2
3
Define
nomen-
clature
Define lifec.
processes
Define
authoriza-
tion concept
Define & roll
out lifecycle
procedures
EDM
Data Model
4 Detect
requirements
for model
Analyze
implication of
changes
Model
master data
Test master
data model
changes
GovernanceStrat.
EDM
Architecture
5 Detect
requirements
for arch.
Analyze
implication of
changes
Model data
architecture
Roll out EDM
architecture
Implement
workflows/
UIs
Implement
measure-
ment metrics
Roll out data
model
changes
Model
workflows /
UIs
EDM Support
7
Provide
trainings
Provide
business
support
Provide
project
support
EDM Life Cycle
Management
6
Operations
Source
/approve
information
Deploy
master data
Archive
master data
Create
master data
Maintain
master data
Executed by EDM
organization
Governed by EDM
organization
Mass data
changes
Business object specific
tasks and responsibilities
Common tasks
Tasks and
responsibilities of
different
business objects
(e.g. supplier,
customer, etc.)
may differ on the
operational level.
SupplierSupplier
CustomerCustomer
……

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Roles are defined on strategic, governance, and operational level
Governance Level
Operational Level
Strategic Level
Set strategic direction of
EDM and ensure alignment
with business and IT
strategy.
Define and control standards
and guidelines for enterprise
data according to the business
requirements.
Request, create, maintain
and approve enterprise data
following defined standards
and guidelines. Establish
technical readiness of IT
systems.
EDM Community
EDM Board
Head of IT
Business
Data Steward
Technical
Data Steward
Executive Sponsor
Head of EDM
Corporate Data Operator
Business process owner
EDM organization
Other SEN organization
Global roles
Global or regional roles

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Solution – Data Governance as central function
Interaction
Head of EDM
Strategiclevel
Governance/
Operationallevel
Business processes EDM
EDM-Board
Operative
in SAP
Business Process
Owner
Business Process
Owner
Data OwnerCorporate Data
Operator
Communicate /
improve standards
Define standards
Business Data
Steward
Business Data
Steward
Enforce standards
during data update
Align process /
data requirements
IT
Head of IT
Align IT strategy
IT implementation
IT Data
Steward

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Example 2 – Chemical Industry
Business drivers for Data Governance
 Process Efficiency
 Delayed delivery to customers due to wrong material master
 Invoicing to the wrong customer
 Wrong labels
 Cost Reduction
 High inventories due to lack of trust in master data
 Additional air freight costs to ensure on time arrival
 Management Decision Support
 Reporting inaccuracy due to inconsistent data

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The MDM organization will sustain efficiency and quality of master data
• Defining and monitoring of SLAs and KPIs in a global governance framework
• Acting as a global stewardship organization, driving the global standardization and
optimization of processes
• Providing one global lead steward for each data object to ensure accountability and a
high level of support to business users
3. The MDM organization act as a catalyst through…
• Accountabilities for master data are defined and data quality monitored
• Maintenance processes are globally standardized and automated
• A small number of data specialists concentrate on continuous improvement instead of
firefighting and data typing
2. We have to come to a state where…
• No clear accountability for master data on a global level
• Lack of standardization and automation
 Inefficient and heterogeneous ways of managing master data
 Poor data quality troubles users of global systems (APO, EDWH, global product
costing
1. The situation today shows…

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Process landscape for MDM services
 Each process delivers services to the business organizations
 The implementation of the services will follow of structured roadmap for the defined master
data types (Material, Vendor, Customer, Finance, Employee)
 The services are measured by Service Level Agreements (SLAs) in order to assure the
quality of the services
Process landscape
Master Data Maintenance2
Master Data
Standards
Training &
Support
Quality
Assurance
3 4 5
Master Data Infrastructure6
Master Data Strategy1
Scope of services
Material
Vendor
Customer
Finance
Employee

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Solution - Shared Service Center for governance and operational
responsibility
Data & System Architecture
Data
Lifecycle
Management
Data Quality
Assurance
MDM
Organisation
Data Governance
Enables a single view on
each master data class
Creates, changes
and retires a data
object
Ensures that the quality of
data objects supports the
dependent business
processes
Ensures that the
MDM agenda can
be driven across
the enterprise

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Organizational integration of MDM
CEO
Functional
Grouping
Service
Functions
BS (HR, IS,
FI, LT etc)
etc
Strategic
Functions
HR
FI
Marketing
etc
Divisional
Grouping
Geographic
structure
Product
structure
Market
structure
Head of
Business
Services
Head of MDM
Regional MDM
Heads
Head of NAFTA
MDM
Head of LATAM
MDM
Head of
EAME/APAC
MDM
Lead Data
Stewards
Material HR
Customer Vendor
Finance
Data Architect
Company structure MDM structure

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Main benefit of the global MDM organization is the overall improved data
quality enabling the business to focus on value add activities
• Change of functional reporting from business to a business neutral MDM unit
• Change of regional reporting lines to global reporting line
Impacts
• Harmonized processes and policies and governance across regions & business units
• Higher scalability: faster integration of new companies or processes, systems etc.
• Bigger pool of trained people
• Reduced headcount
• Reduced number of codes in system (big issue in material today as well as vendor and
customer)
• Improved data quality & reporting also since global team has higher authority to advise regional
teams to not “manipulate data in ERP system)
• Attraction for higher skilled employees based on career opportunities
Benefits
• Strong and visible SLAs in place including tracking of KPIs
• Strong governance model between business and MDM
• Quick wins for Business in order to Business to accept organization
• Outsourcing only when internal processes work well
Critical success factors

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Governance design principles
Global
 Global responsibility
 Regional and local presence
Shared
 Center of excellence for the business
 Efficiency and speed
Governing
 Binding standards and guidelines for the use of master data
 Defined methodologies and tools
Service-
oriented
 Aiming at internal customer satisfaction
 Service level agreements for measurable performance
Managed
 Preventive measures instead of “firefighting”
 Clear objectives and standard operating procedures
Empowered
 Sponsored by executive management
 Appropriate resource assignment

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The way forward – From shared service to outsourced data management
processes
IS Outsourcing
Partner
Company
Domain MDM
Teams
MDM Leads
MDM Data
Stewards
Company
Service Delivery &
Operations Teams
Service Delivery
Managers
Master Data
Requestors
Business
Process
Outsourcing
Partner
Master Data
Processors
Clients
Master Data Request Originators

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Key success factors for implementing Data Governance
Demonstrate staying power! Data Governance is a change
issue and requires involvement of all stakeholders.
No bureaucracy! Use existing board structures and processes.
No ivory tower, no silver bullet! Use “real-life” examples to get
buy in from local business units.
Define clear objectives and standard operation procedures to
prevent “firefighting”.

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Contact
http://www.bei-sg.ch
http://cdq.iwi.unisg.ch
Andreas Reichert
University of St. Gallen
CC Corporate Data Quality
andreas.reichert@unisg.ch
Tel.: +41 71 224 3880

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Further information
Institute of Information Management at the University of St. Gallen
http://www.iwi.unisg.ch
Business Engineering Institute St. Gallen
http://www.bei-sg.ch
Competence Center Corporate Data Quality
http://cdq.iwi.unisg.ch
CC CDQ Benchmarking Platform
https://benchmarking.iwi.unisg.ch/
CC CDQ Community at XING
http://www.xing.com/net/cdqm