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
Research and Services Overview
Engagement Models
Table of Content
© BEI St. Gallen – St. Gallen, June 2013, 3
Business Engineering Institute
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
© BEI St. Gallen – St. Gallen, June 2013, 4
IT
Innovations
Transformation
of
the Enterprise
Business Engineering – the model we apply to produce
results
http://de.wikipedia.org/wiki/Business_Engineering
© BEI St. Gallen – St. Gallen, June 2013, 5
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
© BEI St. Gallen – St. Gallen, June 2013, 6
Business Engineering Institute
Research and Services Overview
Engagement Models
Table of Content
© BEI St. Gallen – St. Gallen, June 2013, 7
Research and Services Overview
Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking
© BEI St. Gallen – St. Gallen, June 2013, 8
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
© BEI St. Gallen – St. Gallen, June 2013, 9
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.
© BEI St. Gallen – St. Gallen, June 2013, 10
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
© BEI St. Gallen – St. Gallen, June 2013, 11
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.
© BEI St. Gallen – St. Gallen, June 2013, 12
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)?
© BEI St. Gallen – St. Gallen, June 2013, 13
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
© BEI St. Gallen – St. Gallen, June 2013, 14
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
Applications for CDQ
Corporate Data Architecture
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.
© BEI St. Gallen – St. Gallen, June 2013, 15
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
© BEI St. Gallen – St. Gallen, June 2013, 16
The CC CDQ “knowledge pool” provides access to a
variety of existing knowledge and expertise
© BEI St. Gallen – St. Gallen, June 2013, 17
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
© BEI St. Gallen – St. Gallen, June 2013, 18
Research and Services Overview
Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking
© BEI St. Gallen – St. Gallen, June 2013, 19
StrategyStrategy
Strategy for CDQStrategy for CDQ
SystemsSystems
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
Systems
Applications for CDQ
Corporate Data Architecture
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
© BEI St. Gallen – St. Gallen, June 2013, 20
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
© BEI St. Gallen – St. Gallen, June 2013, 21
Research and Services Overview
Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking
© BEI St. Gallen – St. Gallen, June 2013, 22
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
© BEI St. Gallen – St. Gallen, June 2013, 23
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
© BEI St. Gallen – St. Gallen, June 2013, 24
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 St. Gallen – St. Gallen, June 2013, 25
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
© BEI St. Gallen – St. Gallen, June 2013, 26
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
© BEI St. Gallen – St. Gallen, June 2013, 27
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
© BEI St. Gallen – St. Gallen, June 2013, 28
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
© BEI St. Gallen – St. Gallen, June 2013, 29
Research and Services Overview
Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking
© BEI St. Gallen – St. Gallen, June 2013, 30
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
Corporate Data League
Collective
Business
Partner Data
read
write
© BEI St. Gallen – St. Gallen, June 2013, 31
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
© BEI St. Gallen – St. Gallen, June 2013, 32
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
Corporate Data League
© BEI St. Gallen – St. Gallen, June 2013, 33
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
© BEI St. Gallen – St. Gallen, June 2013, 34
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
© BEI St. Gallen – St. Gallen, June 2013, 35
Research and Services Overview
Competence Center CDQ
Consulting Services
Training courses
Corporate Data League
Assessments & Benchmarking
© BEI St. Gallen – St. Gallen, June 2013, 36
BEI supports different kind of trainings
Internal Trainings
Basic
Training Courses:
General information on
data management
according to the
Framework for
Corporate Data Quality
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)
© BEI St. Gallen – St. Gallen, June 2013, 37
CDQ-Days 2013
External public training course
Day 1 Day 2
Framework for
Corporate Data Quality
Management
Corporate Data Quality
Management
Strategy Design
Performance Measurement of
Corporate Data Quality
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
© BEI St. Gallen – St. Gallen, June 2013, 38
Business Engineering Institute
Research and Services Overview
Engagement Models
Table of Content
© BEI St. Gallen – St. Gallen, June 2013, 39
BEI has a unique service portfolio
MDM/CDQMDomainKnowledge
Solution Approach
Strategy/Organization Technology
highLow
BEI
NB: Positioning in the case of a partner company.
© BEI St. Gallen – St. Gallen, June 2013, 40
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 St. Gallen – St. Gallen, June 2013, 41
BEI transfers scientific innovations in practical solutions
DEVELOP STANDARDIZE IMPLEMENT
Applied Research Transfer Consulting
© BEI St. Gallen – St. Gallen, June 2013, 42
BEI offers a set of proven engagement models
Competence Center CDQ
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
© BEI St. Gallen – St. Gallen, June 2013, 43
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
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
Applications for CDQ
Corporate Data Architecture
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.
© BEI St. Gallen – St. Gallen, June 2013, 44
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 Overview

  • 1.
    Corporate Data Quality Management Researchand Services Overview Prof. Dr. Boris Otto, Dr. Dimitrios Gizanis June, 2013
  • 2.
    © BEI St.Gallen – St. Gallen, June 2013, 2 Business Engineering Institute Research and Services Overview Engagement Models Table of Content
  • 3.
    © BEI St.Gallen – St. Gallen, June 2013, 3 Business Engineering Institute 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
  • 4.
    © BEI St.Gallen – St. Gallen, June 2013, 4 IT Innovations Transformation of the Enterprise Business Engineering – the model we apply to produce results http://de.wikipedia.org/wiki/Business_Engineering
  • 5.
    © BEI St.Gallen – St. Gallen, June 2013, 5 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
  • 6.
    © BEI St.Gallen – St. Gallen, June 2013, 6 Business Engineering Institute Research and Services Overview Engagement Models Table of Content
  • 7.
    © BEI St.Gallen – St. Gallen, June 2013, 7 Research and Services Overview Competence Center CDQ Consulting Services Training courses Corporate Data League Assessments & Benchmarking
  • 8.
    © BEI St.Gallen – St. Gallen, June 2013, 8 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
  • 9.
    © BEI St.Gallen – St. Gallen, June 2013, 9 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.
  • 10.
    © BEI St.Gallen – St. Gallen, June 2013, 10 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
  • 11.
    © BEI St.Gallen – St. Gallen, June 2013, 11 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.
  • 12.
    © BEI St.Gallen – St. Gallen, June 2013, 12 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)?
  • 13.
    © BEI St.Gallen – St. Gallen, June 2013, 13 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
  • 14.
    © BEI St.Gallen – St. Gallen, June 2013, 14 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 Applications for CDQ Corporate Data Architecture 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.
  • 15.
    © BEI St.Gallen – St. Gallen, June 2013, 15 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
  • 16.
    © BEI St.Gallen – St. Gallen, June 2013, 16 The CC CDQ “knowledge pool” provides access to a variety of existing knowledge and expertise
  • 17.
    © BEI St.Gallen – St. Gallen, June 2013, 17 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
  • 18.
    © BEI St.Gallen – St. Gallen, June 2013, 18 Research and Services Overview Competence Center CDQ Consulting Services Training courses Corporate Data League Assessments & Benchmarking
  • 19.
    © BEI St.Gallen – St. Gallen, June 2013, 19 StrategyStrategy Strategy for CDQStrategy for CDQ SystemsSystems 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 Systems Applications for CDQ Corporate Data Architecture 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
  • 20.
    © BEI St.Gallen – St. Gallen, June 2013, 20 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
  • 21.
    © BEI St.Gallen – St. Gallen, June 2013, 21 Research and Services Overview Competence Center CDQ Consulting Services Training courses Corporate Data League Assessments & Benchmarking
  • 22.
    © BEI St.Gallen – St. Gallen, June 2013, 22 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
  • 23.
    © BEI St.Gallen – St. Gallen, June 2013, 23 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
  • 24.
    © BEI St.Gallen – St. Gallen, June 2013, 24 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
  • 25.
    © BEI St.Gallen – St. Gallen, June 2013, 25 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
  • 26.
    © BEI St.Gallen – St. Gallen, June 2013, 26 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
  • 27.
    © BEI St.Gallen – St. Gallen, June 2013, 27 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
  • 28.
    © BEI St.Gallen – St. Gallen, June 2013, 28 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
  • 29.
    © BEI St.Gallen – St. Gallen, June 2013, 29 Research and Services Overview Competence Center CDQ Consulting Services Training courses Corporate Data League Assessments & Benchmarking
  • 30.
    © BEI St.Gallen – St. Gallen, June 2013, 30 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 Corporate Data League Collective Business Partner Data read write
  • 31.
    © BEI St.Gallen – St. Gallen, June 2013, 31 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
  • 32.
    © BEI St.Gallen – St. Gallen, June 2013, 32 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 Corporate Data League
  • 33.
    © BEI St.Gallen – St. Gallen, June 2013, 33 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
  • 34.
    © BEI St.Gallen – St. Gallen, June 2013, 34 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
  • 35.
    © BEI St.Gallen – St. Gallen, June 2013, 35 Research and Services Overview Competence Center CDQ Consulting Services Training courses Corporate Data League Assessments & Benchmarking
  • 36.
    © BEI St.Gallen – St. Gallen, June 2013, 36 BEI supports different kind of trainings Internal Trainings Basic Training Courses: General information on data management according to the Framework for Corporate Data Quality 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)
  • 37.
    © BEI St.Gallen – St. Gallen, June 2013, 37 CDQ-Days 2013 External public training course Day 1 Day 2 Framework for Corporate Data Quality Management Corporate Data Quality Management Strategy Design Performance Measurement of Corporate Data Quality 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
  • 38.
    © BEI St.Gallen – St. Gallen, June 2013, 38 Business Engineering Institute Research and Services Overview Engagement Models Table of Content
  • 39.
    © BEI St.Gallen – St. Gallen, June 2013, 39 BEI has a unique service portfolio MDM/CDQMDomainKnowledge Solution Approach Strategy/Organization Technology highLow BEI NB: Positioning in the case of a partner company.
  • 40.
    © BEI St.Gallen – St. Gallen, June 2013, 40 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
  • 41.
    © BEI St.Gallen – St. Gallen, June 2013, 41 BEI transfers scientific innovations in practical solutions DEVELOP STANDARDIZE IMPLEMENT Applied Research Transfer Consulting
  • 42.
    © BEI St.Gallen – St. Gallen, June 2013, 42 BEI offers a set of proven engagement models Competence Center CDQ 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
  • 43.
    © BEI St.Gallen – St. Gallen, June 2013, 43 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 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 Applications for CDQ Corporate Data Architecture 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.
  • 44.
    © BEI St.Gallen – St. Gallen, June 2013, 44 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