This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.

1. One Size Does Not Fit All:
Best Practices for Data Governance
Prof. Dr. Boris Otto, Assistant Professor
St. Gallen, Switzerland, March 2012
University of St. Gallen, Institute of Information Management
Chair of Prof. Dr. Hubert Österle

2. Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options
3. Best Practice Cases
4. Competence Center Corporate Data Quality
St. Gallen, Switzerland, March 2012, B. Otto / 2

3. Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options
3. Best Practice Cases
4. Competence Center Corporate Data Quality
St. Gallen, Switzerland, March 2012, B. Otto / 3

4. Data Governance is necessary in order to meet several strategic business
requirements
Compliance with regulations and contractual obligations
Integrated customer management (“360 degree view”)
Company-wide reporting needs (“Single Source of the Truth”)
Business integration
Global business process harmonization
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5. The typical evolution of data quality over time in companies shows a
strong need for action
Data Quality
Legend: Data quality pitfalls
(e. g. Migrations, Process
Touch Points, Poor
Management Reporting Data.
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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6. Data Governance and Data Quality Management are closely interrelated
Maximize is sub-goal of Maximize
Data Value Data Quality
supports supports
is led by is sub-function
Data Data Data Quality
Governance Management of Management
are object of
are object of are object of
Data Assets
Legend: Goal Function Data.
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7. Data Governance is also about cost trade-off’s
Costs
Total Costs
ΔC
Costs of Poor Data
Quality
DQM Costs
ΔDQ Data Quality
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8. Without Data Governance companies are missing direction with regard to
their data assets
Source: Strassmann, P.: The Politics of Information Management, The Information Economics Press, New Canaan, CT, 1995.
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9. Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options
3. Best Practice Cases
4. Competence Center Corporate Data Quality
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10. As Data Governance is an organizational task, design decisions must be
made in five organizational areas
Data Governance
Organization
Organizational Goals Organizational Structure
Functional Locus of Organizational Roles &
Formal Goals
Goals Control Form Committees
Source: Otto, B.: A Morphology of the Organisation of Data Governance, Proceedings of the 19th European Conference on Information Systems, Helsinki, Finland, 11.06.2011,
2011.
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11. Six cases from global companies are used to illustrate the different design
options
Case A B C D E F
Industry Chemicals Automotive Mfg. Telecom Chemicals Automotive
Headquarter Germany Germany USA Germany Switzerland Germany
Revenue 2009 [million €] 6,510 38,174 4,100 64,600 8,354 9,400
Staff 2009 [1,000] 18,700 275,000 23,500 260,000 25,000 60,000
Role of main contact person Head of Program Head of Data Head of Data Head of Project
for the case study Enterprise Manager Governance Governance MDM SSC Manager
MDM MDM MDM
Key: MDM - Master Data Management, Mfg. - Manufacturing; SSC - Shared Service Center.
NB: All case study companies are research partner companies in the Competence Center Corporate Data Quality (CC CDQ).
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12. Data Governance design options can be broken down into 28 individual
items
Data Governance Organization
Data Governance Goals Data Governance Structure
Formal Goals Locus of Control
Business Goals Functional Positioning
• Ensure compliance • Business department
• Enable decision-making • IS/IT department
• Improve customer satisfaction
• Increase operational efficiency Hierarchical Positioning
• Support business integration
• Executive management
IS/IT-related Goals • Middle management
• Increase data quality Organizational Form
• Support IS integration (e.g. migrations)
• Centralized
Functional Goals • Decentralized/local
• Project organization
• Create data strategy and policies • Virtual organization
• Establish data quality controlling • Shared service
• Establish data stewardship
• Implement data standards and Roles and Committees
metadata management • Sponsor
• Establish data life-cycle management • Data governance council
• Establish data architecture • Data owner
management • Lead data steward
• Business data steward
• Technical data steward
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13. For example, the design area “Roles & Committees” comprises six
individual roles
Sponsor
Data
Data Owner Governance
Council
Lead Data
Steward
Business Data Technical Data
Steward Steward
Legend: Disciplinary reporting line (“solid”); Functional reporting line (“dotted”); is part of.
Business IT Data Team.
Single role Composite role.
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14. The cases show a variety of different Data Governance designs
Data Governance Goals Data Governance Structure
Case Formal goals Functional goals Locus of control Org. form Roles, committees
A No formal quantified DQ, data lifecycle, data arch., Business (IM and Central MDM dept., MDM council, data
goals; DQ index and software tools, training SCM), 3rd level virtual global owners, lead steward,
data lifecycle time organisation technical steward
measured
B No formal quantified Business: Data definitions, Business (corporate Central project Steering committee,
goals ownership, data lifecycle, data accounting), 3rd level organisation, virtual master data owner,
arch.; IS/IT: Data models, IT organisation master data officer
arch., projects, DQ
C No formal quantified Data ownership, data lifecycle, Business (shared Central data DG manager, DQ
goals, data lifecycle DQ, service level service centre), 4th management org.; manager, data owner,
time measured, SLAs management, project support level virtual global data stewardship
with internal customers organisation manager, data steward;
planned no committee
D Alignment with DQ standards and rules, data Hybrid (both central IT Central organization, “Data responsible”, data
business strategic quality measuring, ownership, and business), 3rd and supported by projects architect, data manager,
goals, no quantification data models and arch., audits 4th level DQ manager, no
committee
E Alignment with Data strategy, rules and Business (shared Shared service Head of MDM, data
business drivers, standards, ownership, DQ service centre), 4th owners, lead stewards
formalisation through assurance, data & system level (per domain), regional
SLAs arch. MDM heads, data
architect; no committee
F No formal quantified MDM strategy, monitoring, IS/IT, 3rd level Central organisation, Head of MDM, data
goals organisation, processes, and supported by projects owners, DG council,
data arch., system arch., data architect
application dev.
Key: DG - Data governance; Org. - Organisational; DQ - Data quality; arch. - architecture; IM - Information Management; SCM - Supply Chain Management; MDM - Master Data
Management, dept. - department; IS - Information Systems; IT - Information Technology; SLA - Service Level Agreement.
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15. Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options
3. Best Practice Cases
4. Competence Center Corporate Data Quality
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16. In Case A data quality is measured on a continuous basis
Overall data quality indices per
region and per country are
published on the corporate
intranet.
Regions and countries can
monitor their own progress (as
well as the progress of best-in-
class countries)
Measurement and data quality
indices are made transparent to
everybody.
Calculation of indices can be
track down to the individual
record level.
Chemical Industry
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17. Data Governance in Case B is well-balanced between IT and business
functions as well as between corporate and business units
Executive Management
report
corporate sector/
corporate department
Overall responsibility
(MD Owner, IT, ..)
Interdisciplinary
for a master data class Master Data Master Data Master Data Management
(specialist/organizational Owner A Owner X Steering Committee
level)
working group /
Responsibility Master Data Master Data competence team
in relevant units (data Officer Officer
maintenance/ application) … …
Governance Governance
Function Function
IT Projects
Concepts Concepts IT platforms, IT target systems
Master data Master data
class 1 … class N
e. g. Supplier master data Chart of accounts
Automotive Industry
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18. Case D is an example of a formalized Data Governance organization with
hybrid location of responsibilities
Deutsche
Telekom AG
T-Home T-Mobile T-Systems
MQM
Line of
Marketing and …
Business CIO
Quality Mngmt.
MQM2 IT1 IT2
Quality IT Strategy and Enterprise IT …
Management Quality Architecture
MQM27 ZIT7
Data Quality … Information …
Management Processing
IT73/74
ZIT72
… Data …
MDM
Management
ZIT721 ZIT722
Data DQ Measurement
Governance and Assurance Telecom Industry
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19. In Case E Master Data Management is organized as a shared service and
operated as a “data factory”
Ensures that the quality of
data objects supports the
dependent business
processes
Data Governance
Creates, changes
and retires a data
object
Data Ensures that the
Data Quality MDM
Lifecycle MDM agenda can
Assurance Organisation
Management be driven across
the enterprise
Data & System Architecture
Enables a single view on
each master data class
Chemical Industry
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20. Case F is an example for locating the Data Management Organization
within the IS/IT function
Process
Management Board CFO
Harmonization Board
Corporate
Divisions Process Corporate IT
Mgmt.
Business Business IT Architecture
Division
Management Process Archi- & SCO
Committee tecture Mgmt. IT Org. Consulting
Project Information
Corporate IT Competence Master Data
Portfolio and Application
Departments Center Management
Mgmt. Integration
Corporate Advanced
Applications Development
Key: Recently established.
Automotive Industry
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21. Some key success factors become apparent when analyzing the cases
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.
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22. Agenda
1. Business Rationale for Data Governance
2. Data Governance Design Options
3. Best Practice Cases
4. Competence Center Corporate Data Quality
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23. The Competence Center Corporate Data Quality comprises 22 partner
companies1
AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AG
CORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AG
ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBH
KION INFORMATION MANAGEMENT
MIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AG
SERVICE GMBH
SIEMENS ENTERPRISE
ROBERT BOSCH GMBH SAP AG SYNGENTA CROP PROTECTION AG
COMMUNICATIONS GMBH & CO. KG
TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies.
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24. The Competence Center Corporate Data Quality channels the knowledge
and experience of a large network of practitioners and researchers
850+ Contacts in the overall CC CDQ community
150+ Members in the XING Community
140+ Bilateral Project Workshops
70+ Best Practice Presentations
28 Consortium Workshops
22 Partner Companies
13 Scientific Researchers/PhD Students
1 Competence Center
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25. Life is good with Data Governance…
Source: Strassmann, P.: The Politics of Information Management, The Information Economics Press, New Canaan, CT, 1995.
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26. CC CDQ Resources on the Internet
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
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27. Contact
Prof. Dr. Boris Otto
University of St. Gallen, Institute of Information Management
Tuck School of Business at Dartmouth College
Boris.Otto@unisg.ch
Boris.Otto@tuck.dartmouth.edu
+1 603 646 8991
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