Corporate Data Quality Management Research and Services

Corporate Data Quality
Management
Research and Services Overview
Prof. Dr. Boris Otto, Dr. Dimitrios Gizanis
June, 2013

© BEI St. Gallen – St. Gallen, June 2013, 2
Business Engineering Institute
Engagement Models
Table of Content

A spin-off of the University of St. Gallen
St. Gallen
HEADQUARTERS
2003
FOUNDATION
Prof. Dr. Österle
CHAIRMAN
Prof. Dr. Otto
HEAD OF RESEARCH &
INNOVATION MANAGEMENT
Thomas Zerndt
CEO
Divisions
CORPORATE DATA QUALITY MANAGEMENT
SOURCING IN THE FINANCIAL INDUSTRY
INDEPENDENT LIVING

IT
Innovations
Transformation
of
the Enterprise
Business Engineering – the model we apply to produce
results
http://de.wikipedia.org/wiki/Business_Engineering

Business Engineering Forum 2013
The Business Engineering Forum is an
annual business conference attracting
more than 150 global leaders.
11. – 12. October 2013
Executive Campus HSG in St. Gallen
https://be-forum.iwi.unisg.ch

Engagement Models
Table of Content

Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking

Data quality is necessary to respond to a number of
strategic business requirements
Enterprise
Division 2Division 1 Division 3
Business Units
Business Processes
Locations
Departments
Business Units
Business Processes
Locations
Departments
Business Units
Business Processes
Locations
Departments
Risk Management and «Compliance»
Customer-centric business models
Integration of acquired businesses and business process harmonization
Corporate Reporting
Complexity management

Complexity drivers pose challenges on data quality
management
Corporate
Data Quality
“Big Data”
RFID, customer loyalty
programs etc.
Globalized Operations
Multilingualism, “Follow the
sun“-principle etc.
“Taylorism”
Segregation of data creation
and data use
Constant Change
M&A, “Divestments”, Change
Management
Size
Revenue Nestlé 2012: 92 billion CHF
Federal Budget CH 2012: 64 billon CHF
“Hyper-Connectivity”
Social media, data supply
chains etc.

Today, companies manage data quality purely in a
reactive mode
Legend: DQ issues
Data quality
Time
Project 1 Project 2 Project 3
 No risk management possible
 No chance to plan and to control budgets and resources
 No target values for corporate data quality

The Competence Center Corporate Data Quality
(CC CDQ) comprises more than 20 partner companies
AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AG
CORNING CABLE SYSTEMS
GMBH
DAIMLER AG DB NETZ AG
DRÄGERWERK AG & Co.
KGaA
E.ON AG ERICSSON AB
ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBH
KION INFORMATION
MANAGEMENT SERVICE
GMBH
MIGROS-
GENOSSENSCHAFTS-BUND
NESTLÉ SA NOVARTIS PHARMA AG OSRAM GMBH ROBERT BOSCH GMBH
SAP AG
SCHWEIZERISCHE
BUNDESBAHNEN SBB
SIEMENS ENTERPRISE
COMMUNICATIONS GMBH &
CO. KG
SWISSCOM IT
SERVICES AG
SYNGENTA CROP
PROTECTION AG
TELEKOM DEUTSCHLAND
GMBH
ZF FRIEDRICHSHAFEN AG
NB: Overview comprises both current and past research partner companies.

The CC CDQ responds to urgent issues
 How does Corporate Data Quality contribute to the strategic business objectives?
 How does our company compare to others in our peer group?
 How can we measure our performance in Corporate Data Quality Management?
 What are the costs and benefits of Corporate Data Quality?
 How can we establish Data Governance in the company?
 What is the appropriate degree of standards and regulation for our company?
 How do we achieve consistent understanding of corporate data? What is the
baseline of Corporate Data Quality?
 Which data architecture is the right one and how do we implement it?
 How do we benefit from innovative technologies (e.g. Social Media, Linked Data)?

The CC CDQ Framework
Strategy
Organization
System
CDQ Controlling
Applications for CDQ
Corporate Data Architecture
Organization
for CDQ
CDQ Processes and
Methods
Strategy for CDQ
local global
Mandate
Strategy document
Value management
Roadmap
Goals and targets
Data quality metrics
Data Governance
Roles and
responsibilities
Change
management
Standards &
Guidelines
Data life cycle
management
Business metadata
management
Data-driven
business process
management
Conceptual
corporate data
model
Data distribution
architecture
Authoritative data
sources
Software support
(e.g. MDM
applications)
System landscape
analysis and
planning

Customers and partners benefit from an unmatched
pool of knowledge and expertise
90+ Best Practice Presentations
35 Consortium Workshops
27 Partner Companies
14 PhD Students
1 Competence Center
StrategyStrategy
Strategy for CDQStrategy for CDQ
SystemeSysteme
Applications for CDQApplications for CDQ
Corporate Data ArchitectureCorporate Data Architecture
lokallokal globalglobal
OrganisationOrganisation
CDQ ControllingCDQ Controlling
CDQ Processes and
Methods
CDQ Processes and
Methods
Organisation
for CDQ
Organisation
for CDQ
Strategy
Strategy for CDQ
Systeme
lokal global
Organisation
CDQ Controlling
CDQ Processes and
Methods
Organisation
for CDQ
850+ Contacts in the overall CC CDQ
community
180+ Members in the XING Community1
150+ Bilateral Project Workshops
NB: as of June 2013. Data covers period from 2006 until today.
1) See www.xing.com/net/cdqm.

Achieved results provide a “tool box” for establishing
Corporate Data Quality Management
EFQM Excellence Model for
Data Quality Management
Data Quality Management
Strategy Design Method
Reference model for Data
Governance
Method for establishing Data
Governance
Method for integrating DQ in
process management
Method for specifying data
quality metrics
Method for master data
integration
Reference model for
DQ Management software
386
DQ-Cockpit
0 1000
I
II
III
386
DQ-Cockpit
0 1000
I
II
III
Sponsor
Data Owner
Corporate Data
Steward
Fachlicher
Datensteward
Technischer
Datensteward
SDQM-
Komitee
Daten-
steward-
Team
Lebenszyklus-
management für
Stammdaten
Metadaten-
management und
Stammdaten-
modellierung
Qualitäts-
management für
Stammdaten
Stammdaten-
integration
Querschnitt-
funktionen
Administration
A
Stammdatenanlage Stammdatenpflege
Stammdaten-
deaktivierung
Stammdaten-
archivierung
Datenmodellierung Modellanalyse
Datenanalyse Datenanreicherung Datenbereinigung
Datenimport Datentransformation Datenexport
Automatisierung Berichte Suche
Workflow-
management
Änderungs-
management
Benutzerverwaltung
Metadaten-
management
B
C
D
E
F
1 2 3 4
1
1
1
1
1
2
2
2
2
2
3
3
3
3 4
MDS
Quelle 1 Quelle 2 Quelle m
Ziel 1 Ziel 2 Ziel n
MDS
Ziel 1 Ziel 2 Ziel n
TransaktionKoexistenz
Strategische Anforderungen und WertbeitragA
ProzesseB OrganisationC QualitätssicherungD ArchitekturE
Umsetzungsplan (Transformation)F

The CC CDQ “knowledge pool” provides access to a
variety of existing knowledge and expertise

The “CC CDQ Awards” recognize excellent results
CDQ Good Practice AwardCDQ Excellence Award
Apply for the CDQ Awards
On-site visits and interviews
Winners are recognized at the annual Business Engineering Forum
Winners are selected on basis
of the assessment score
Winners are selected by a
jury1 (representatives from
both scientific and
practitioner’s community)
Jury Members (planned):
• Prof. Dr. Andy Koronios (University of South Australia)
• Henning Uiterwyk (Managing Director of eCl@ss)
• Màrta Nagy Rothengrass (European Commission, Content and Technology Unit - Data Value Chain)
• Bernhard Thalheim (Head of the German Chapter of DAMA International)
• Frank Boller (VP SwissICT, Swiss ICT industry association)
• Lwanga Yonke (International Association for Information and Data Quality)
Starting 2014 Starting 2013

Consulting Services
Training courses

StrategyStrategy
SystemsSystems
CDQ Processes and
Methods
CDQ Processes and
Methods
Organisation
for CDQ
Organisation
for CDQ
Strategy
Strategy for CDQ
Systems
lokal global
Organisation
CDQ Controlling
CDQ Processes and
Methods
Organisation
for CDQ
BEI offers a comprehensive service portfolio
Strategy
 Maturity Assessment & Strategic Roadmap Design
 Data Management Strategy Design
 Cost-/Benefit Analysis & Value Proposition
Organisation
 Data Governance Design & Implementation
 Data Process Design & Implementation
 Data Quality Cockpit Design & implementation
Audits / Reviews, Coaching, Quality Assurance
On-Boarding Data Stewards, Trainings
Project & Change Management
Systems
 Metadata Design for Master Data Documentation
 System Architecture Design & Implementation
 Functional Requirement Analysis & Software Tool Evaluations
 Evaluation of Data Standards

Roadmap blueprint for master data management
design and implementation
As-is
Analysis
Strategy
Design
Governance
Design
Roadmap
Design Detailed Specification OperationsOrganizational & Technical Implementation
Controlling
Organization & People
Processes & Methods
Data Architecture
Applications
 Continuous
Roll-out
 Continuous
Data Quality
Monitoring &
Improvement
Analysis & Design
Controlling System
Implementation Metrics, Cockpit,
Goals, Reports
Implement & Establish
SLAs in the Organization
Governance Implementation (Roles and Processes),
Integration in existing Organization
Analysis & Design
Roles and Resp.
Implementation Data Maintenance & Business Support Processes
Analysis & Design
Data Processes
Technical System
Blueprint
Evaluation of MDM
Software
MDM Software
Integration
Analysis & Design
Application Funct.
Business Metadata Management
Design conceptual
Data Model
Analysis & Design
Data Flow Architecture
Design Data &
Integration Architecture
Analysis & Design
Core Data Objects
 As-Is Analysis
 Strategic Guidelines
 Strategic Goals
 Data Governance
Concept
 Roadmap
StrategicAssessment
Project Management, Change Management & Communication
Data Cleansing

Consulting Services
Training courses

The Framework for CDQM is a standardized maturity and
benchmarking model
Download PDF: http://benchmarking.iwi.unisg.ch/Framework_for_CDQM.pdf
Bestellung der Broschüre (Print-Exemplar): http://www.efqm.org/
Getragen durch die Praxis

Assessment interview results – Overall result
Project Example
29.0%
35.9%
35.7%
36.7%
36.8%
42.7%
40.3%
46.0%
36.3%
45.6%
0% 20% 40% 60% 80% 100%
Key results
Compliance Results
People Results
Customer Results
Applications
Data Architecture
Processes
Organization
Controlling
Strategy
Achieved assessment level
EnablerandResults
Maturity Assessment
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1A 1B 1C 1D 1E 2A 2B 2C 2D 2E 3A 3B 3C 3D 3E 4A 4B 4C 4D 5A 5B 5C 5D 6A 6B 6C 6D 7A 7B 8A 8B 9A 9B 10A 10B
Strategy Controlling Organisation Processes
& Methods
Data Architecture Applications Customer
Results
People
Results
Compliance
Results
Key
Results
Needforaction
Priority Analysis
Need for action Need to follow up (Threshold) Urgent need for action (Threshold)
Key results
Complianc
e Results
People
Results
Customer
Results
Application
s
Data
Architectur
e
Processes
Organizati
on
Controlling
Strategy
Intended Improvement
386
CDQM Performance and
Maturity Level
0 1000
I
II
III

The CDQM Framework has been applied in 40+ maturity
and benchmarking projects across different industries
Beiersdorf AG
Corning Cable Systems GmbH
Drägerwerke AG & Co. KGaA
Elektrizitätswerke des Kantons
Zürich
Novartis Pharma AG
Robert Bosch GmbH
Schweizerische Bundesbahnen
Siemens Enterprise
Communications GmbH & Co KG
Stadtwerke München
SWM Services GmbH
Swisscom IT Services AG
Telekom Deutschland GmbH
ZF Friedrichshafen AG

BEI products and services support the whole
Assessment Lifecycle
Conduct initial maturity
assessment with
restricted scope
Learn how to
apply the model
in your
organization
Define CDQM
targets
Assess CDQM
Performance
Derive
actions Analyze results
Benchmark
with others
Extend scope as
required
Assessment
frequency: once per
year (example)Maturity Assessment and
Benchmarking
Empowerment Training
Performance Dashboard
Option: Benchmarking Database
Assessor Training
Consulting
Services
Software as
a Service
(SaaS)
Trainings
Option: Good Practice Database

The assessment and benchmarking process follows a
well-proven procedure model
 Define scope of
assessment and
interviewees
 Adapt questionnaire
 Define and
implement
communication plan
Planning Assessment
 Conduct interviews
 Document results
per interview
 Approve
assessment by
interviewee
Analysis
 Compile overall
assessment results
 Consolidate
strengths and
weaknesses profile
 Document key
findings and area of
improvements
Acitivities
Bench-
marking
 Define selection
criteria and identify
benchmarking peers
 Collect
benchmarking
scores from peers
 Collect good
practices from peers
Innovation and learning
6-8 weeks
 Analyze
benchmarking and
assessment results
 Document
improvement
actions in a
roadmap
 Present and
communicate
results to
stakeholders
Action
planning
 Adapted
Questionnaire
 Communication
plan
 Interview
documentations
 Interview approvals
 Assessment results
 Priority analysis
 Strengths and
weaknesses profile
 Benchmarking
report
 Best Practice
recommendation(s)
 List of action(s)
 Roadmap
 Final report
Results

The Performance Dashboard supports the whole
assessment and benchmarking process
Reporting:
Generate
assessment profile
including overall
score, priority
analysis and
strengths and
weaknesses report
(PPT, PDF)
Model
Configuration
Management:
Configure
company-specific
questionnaires
and select
interviewees
Assessment Management: Automatically send-out invitations and fill-
out questionnaires in online or offline mode
Assessment
Management: Create
and configure a new
Assessment
Support:
Technical support
per mail and hotline

The Good Practice Database accelerates strategic
action planning
Integration: The
Good Practice
Database is
integrated in the
Performance
Dashboard
Peer Management:
Respond to peer
requests
Peer
Management:
Contact repre-
sentatives of
companies to
initiate in-depth
best practice
sharing and
networking
Good Practices
Database: Learn
from good practices
(structured by the
CDQM Framework)
and align your data
strategy and action
planning accordingly

Consulting Services
Training courses

BEI is setting up a trusted network of user companies
for the exchange of business partner data
First-movers have
exceptional
advantages
 Improve data maintenance processes through network collaboration
 Start collaboration with 6-8 trusted and renowned companies
Goals
 30% lower maintenance efforts / costs by the use of verified data in the network
 First-movers define the rules for collaboration
Benefits
Kick-off:
December 10, 2013 Prospects
Collective
Business
Partner Data
read
write

Community-sourcing for business partner data reduces
data maintenance effort and increases data quality
C: Customer S: Supplier
 Three companies have the same supplier
 Each company maintains master data of the supplier
 3 times redundant data maintenance effort and reference data costs
As-is situation: Redundant data management
C
C C
S
 The three companies share their supplier master data
 And they agree on collaborative data maintenance processes
 Reduced data maintenance effort and costs
Solution approach: Community-sourcing
C
C C
S
 The supplier (Data Owner) also contributes to the sourcing community
 Additional data maintenance effort and cost reduction
 Increased data quality because data is maintained “at source”
Best-case option: Community-sourcing with Data Owners
C
C C
S
Data maintenance

Experience: Only few attributes cause about 70% of
business partner data maintenance effort
Number of attributes Maintenance effort
Business partner data attributes
Legal name, legal form, legal address
Bill-to / ship-to / ordering / EDI addresses
Legal / organizational / geographical hierarchies
Value added tax (VAT) identification number
Banking data
Solvency rating, blacklisting information
Certificates (e.g. SAS70, ISO 9000)
…
Credit limit
Currency data
Dunning terms
Business contacts
Inventory posting data
Customer / Supplier classification
Procurement and shipping conditions
Payment terms and methods
Partner functions
Accounting data
Insurance data
Pricing data
…
5%
30%
70%
95%
Data scope for

Data “Shareconomy” will increase efficiency of
business partner data management
 Business partner data (e.g. addresses, legal hierarchies) is maintained by all companies
 Different companies have same business partners – especially companies of one industry sector
 Most high-maintenance business partner data attributes (e.g. addresses, hierarchies, blacklist flags)
are not critical for competitive advantage
 Data quality requirements (e.g. accuracy, completeness, timeliness) are similar for most companies
Opportunities from a cross-company perspective
 Find better and/or cheaper address and compliance data providers
 Increase data management team and proactively monitor situation of business partners
 Force business partners to self-maintain e.g. address and banking data
Opportunities for a single company

Call for first-movers: BEI wants to start the Corporate
Data League as early as possible
Acceleration
Service available in 2014
Customer-centric development
Individual functional requirements will be considered and implemented
Governance control
First-movers design and control collaboration governance
Financial advantage
Service free of charge for 3 years
Quick wins
Deduplicated business partner data
Benefits for first-movers

Consulting Services
Training courses

BEI supports different kind of trainings
Internal Trainings
Basic
Training Courses:
General information on
data management
according to the
Framework for
Management
Role-specific
Training Courses:
Customized trainings
for specific roles, e.g.:
- Data Stewards
- Data Architects
- Data Operators
Empower your
employees with
training material
accessible via a
web-based interface
Empower your
employees with
training material
accessible via a
web-based interface
and vocally
explained (recorded
or live session)
Classroom
Training
(role specific)
Online
Trainings
Web
Casts
Classroom
Training
(general)

CDQ-Days 2013
External public training course
Day 1 Day 2
Framework for
Management
Management
Strategy Design
Performance Measurement of
Management
Design of
Data Quality Metrics
14. – 15. November 2013
Radisson Blu Hotel, St. Gallen
Training of data management professionals in CDQ methods and reference models

Engagement Models
Table of Content

BEI has a unique service portfolio
MDM/CDQMDomainKnowledge
Solution Approach
Strategy/Organization Technology
highLow
BEI
NB: Positioning in the case of a partner company.

Consulting
BEI combines research and consulting expertise
Research
Prof. Dr.
Hubert Österle
Prof. Dr.
Boris Otto
Verena Ebner Clarissa Falge Ehsan Baghi
Dr. Dimitrios
Gizanis
Dr. Kai
Hüner
Martin
Ofner
Andreas
Reichert
Max
Zurkinden
Dr. Peter
Mayer
Simon
Schlosser
Rene
Abraham
Dr. Ulrich
Wiesweg
Wolfgang
Dietrich
Patrizia
Pesche
Bernd
Wiesing

BEI transfers scientific innovations in practical solutions
DEVELOP STANDARDIZE IMPLEMENT
Applied Research Transfer Consulting

BEI offers a set of proven engagement models
Projects Individual Arrangements
C
Engagement Model Services Included Annual Fee [CHF]
Associate Membership A 75,000
Standard Membership A and B 125,000
Projects B Individual Agreements
Individual Arrangements C License fees
A
B
B = Results are for example strategy design, specification of DQ metrics, data governance review
C = License fees are related for example to the Corporate Data League

Why choose BEI?
The Framework for CDQM is used by the
European Foundation for Quality Management
Best practice transfer is a daily practice
BEI applies well-founded methods to accelerate
projects
BEI is independent, business-driven and has
strong expertise in data management
Besides highly specialized employees
BEI provides a broad network of experts
StrategyStrategy
SystemeSysteme
CDQ Processes and
Methods
CDQ Processes and
Methods
Organisation
for CDQ
Organisation
for CDQ
Strategy
Strategy for CDQ
Systeme
lokal global
Organisation
CDQ Controlling
CDQ Processes and
Methods
Organisation
for CDQ
Living proof that scientific innovation is
successfully transformed into business
BEI has long lasting relationships
with over 70 companies
Business Engineering is the basis for
services for more then 20 years
Framework for CDQM:
Has successfully been applied by 40+ international organizations.
It is used as common language and vocabulary for networking, benchmarking and in projects.

Contacts & Resources on the Internet
http://www.iwi.unisg.ch
Institute of Information Management at the University of St. Gallen
http://www.bei-sg.ch
Business Engineering Institute St. Gallen
http://cdq.iwi.unisg.ch
Competence Center Corporate Data Quality
https://benchmarking.iwi.unisg.ch/
CC CDQ Benchmarking Platform
http://www.xing.com/net/cdqm
CC CDQ Community at XING
Prof. Dr. Boris Otto
Assistant Professor
University of St. Gallen
boris.otto@unisg.ch
Phone: +41 71 224 3220
Dr. Dimitrios Gizanis
BEI St.Gallen AG
Divisional Head CDQ
dimitrios.gizanis@bei-sg.ch
Tel.: +41 76 583 1507

Corporate Data Quality Management Research and Services

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Corporate Data Quality Management Research and Services

Similar to Corporate Data Quality Management Research and Services (20)

More from Boris Otto

More from Boris Otto (20)

Recently uploaded

Recently uploaded (20)

Corporate Data Quality Management Research and Services