master data governance best practices
DESCRIPTION
This presentation illustrates best practices in master data governance through a rich set of case studies. The presentation leverages seven years of in-depth experience in the field from the Competence Center Corporate Data Quality.TRANSCRIPT
Audi-Endowed Chair of
Supply Net Order Management
Best Practices in Master Data Governance
Prof. Dr. Boris Otto | Berlin, 2013/9/23
Audi-Endowed Chair of
Supply Net Order Management
2Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master Data as a Business Success Factor
Five Principles for Master Data Governance
Outlook
Agenda
Audi-Endowed Chair of
Supply Net Order Management
3Prof. Dr. Boris Otto | Berlin, 2013/9/23
Bayer CropScience is a leader in the crop protection market
Audi-Endowed Chair of
Supply Net Order Management
4Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master data quality is a key prerequisite for business process
performance1
1) [EBNER/BRAUER 2011].
Data Object
“Product Hierarchy”Business
Area
Business
Field
Business
Segment
Active
Ingredient
Product
Group
Data Quality Issues
Data not available
Data not complete
Data not consistent
Business Process
Impact
09 11 012 242 3938
Planning: Demand for active ingredients unknown
Revenue reporting: Revenue not transparent on country
Segmentation: Risk of poor portfolio planning
Audi-Endowed Chair of
Supply Net Order Management
5Prof. Dr. Boris Otto | Berlin, 2013/9/23
Johnson & Johnson is a leading producer of consumer products
Skin Care, Baby Care, Consumer Healthcare, OTCFranchises
Skillman, NJ (USA)Headquarter
Audi-Endowed Chair of
Supply Net Order Management
6Prof. Dr. Boris Otto | Berlin, 2013/9/23
In early 2008, Johnson & Johnson was suffering from poor
master data quality1
Inbound Logistics ProductionSales &
Distribution
Procurement
Financial Accounting
Portfolio Management and New Product Introduction
Controlling
Other Support Processes
“Customers were
invoiced wrong”
“Trucks were waiting
at the docks for
materials to be
activated”
“Production was delayed
at manufacturing plants”
“Purchase
orders were
not ready on
time”
“Project Management did not
know what stage products are in”
“Defective data was
sent to GS1 US”
For less than 30 of products’ dimensions and weights, data was within the allowed 5 % error margin
1) [OTTO 2013].
Audi-Endowed Chair of
Supply Net Order Management
7Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master data quality drivers affect the entire company
Group
Division 2Division 1 Division 3
Business units
Business
processes
Locations
Business units
Business
processes
Locations
Business units
Business
processes
Locations
Compliance to regulations
360 degree view of the customer
Integrated and automated business processes
“Single Source of the Truth”
Audi-Endowed Chair of
Supply Net Order Management
8Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master data quality evolves over time according to a “jigsaw”
pattern
Legend: Master data quality issues.
Master data quality
TimeProject 1 Project 2 Project 3
Audi-Endowed Chair of
Supply Net Order Management
9Prof. Dr. Boris Otto | Berlin, 2013/9/23
The case of Bayer CropScience illustrates the various data
quality issues companies have to deal with1
Data
quality
issues
Employees Data Maintenance
Data Quality Management Standards Organization
Training and education
inadequate
Data quality not integrated in
performance management systems
Various software
solutions in place
Master data can be edited in
target systems
No integrated software
support
Data maintenance not
harmonized on global level
No data quality
metrics
No continuous data
quality monitoring
No binding rules,
standards, operating
procedures
Too many local rules,
exceptions
No
“Data Governance”
Missing business
responsibilities
1) [BRAUER 2009].
Audi-Endowed Chair of
Supply Net Order Management
10Prof. Dr. Boris Otto | Berlin, 2013/9/23
Corporate Data Quality Management (CDQM)1 comprises six
key enablers
1) [OTTO ET AL. 2011].
Audi-Endowed Chair of
Supply Net Order Management
11Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master Data as a Business Success Factor
Five Principles for Master Data Governance
Outlook
Agenda
Audi-Endowed Chair of
Supply Net Order Management
12Prof. Dr. Boris Otto | Berlin, 2013/9/23
Data Governance and Data Quality Management are closely
interrelated
Legend: Goal Function Data.
Data
Governance
Data Quality
Management
Maximize
Data Quality
Maximize
Data Value
Data Assets
Data
Management
is sub-goal of
supports supports
is led by is sub-function
of
are object of are object of
are object of
Source: Otto, B.: Data Governance, in: WIRTSCHAFTSINFORMATIK, 53, 4, 2011, S. 235-238.
Audi-Endowed Chair of
Supply Net Order Management
13Prof. Dr. Boris Otto | Berlin, 2013/9/23
Data Governance effectiveness still varies widely today1
25.0
30.0
30.0
7.5
7.5
very good good mediocre adequate poor
1) [MESSERSCHMIDT/STÜBEN 2011].
NB: Figures are percentages.
Audi-Endowed Chair of
Supply Net Order Management
14Prof. Dr. Boris Otto | Berlin, 2013/9/23
What issues does upper management see with regard to Data
Governance? The case of Syngenta
Business benefits
“Keep in mind to balance costs for double-handling on one hand and of high
discipline on the other.”
“Emphasize usability of MDM, its value.”
Organizational readiness
“Data owners and data stewards are terms people don‘t understand. Be
educational and promotive.”
“Organizational maturity differs in the divisions.”
Data Governance implementation and execution
“What’s the migration path? Are there intermediate staging gates?”
“Is it a journey or can one make a choice? Or both?”
“How to integrate this strategy into the program of next year?”
“How to integrate the 35,000 ft view with daily operations?”
NB: Selected quotes from a series of eight interviews with line managers conducted in October and November 2011.
Audi-Endowed Chair of
Supply Net Order Management
15Prof. Dr. Boris Otto | Berlin, 2013/9/23
Capture Data at the Source
Enter Data “First Time Right”
Measure to Manage
Build up a Data Governance Capability
Scale Capabilities Globally
Five key principles lead to excellence in master data
governance
Audi-Endowed Chair of
Supply Net Order Management
16Prof. Dr. Boris Otto | Berlin, 2013/9/23
Typically, Data Governance capabilities have first to be built up
NB: Based on data from eight cases (Bayer
CropScience, Corning Cable Systems, DB
Netz, Deutsche Telekom, Johnson &
Johnson, Robert Bosch, Syngenta, ZF)
Audi-Endowed Chair of
Supply Net Order Management
17Prof. Dr. Boris Otto | Berlin, 2013/9/23
Note taken in a meeting with Johnson & Johnson on November
29, 2011, in Skillman, NJ
Audi-Endowed Chair of
Supply Net Order Management
18Prof. Dr. Boris Otto | Berlin, 2013/9/23
The ideal lifecycle of Data Governance capabilities follows an
“S” curve
Founding Phase „First Time Right“ Cleansing
Legend: E Effectiveness; A Amount of Activity.
E
A
Audi-Endowed Chair of
Supply Net Order Management
19Prof. Dr. Boris Otto | Berlin, 2013/9/23
It is not a perfect world, though
2008 2009 2010 2011
1. CDM unit launched
2. Data creation workflow
3. DQ metrics launched
1.
2.
3.
Pattern I
2008 2009 2010 2011
1. DG project launched
2. Address to board
3. DQ metrics launched
4. „Community“ approach
proposed
5. DG council launched
1.
2.
Pattern II
3.
4.
5.
2007 2008 2009 2010
1.
1. CDM unit launched
2. Progress report to the board
proposed
3. Inventory data quality
assessment
4. CDM reorganized
2.
3. 4.
Pattern IIIE E E
A A A
Legend: E Effectiveness; A Amount of Activity; CDM Corporate Data Management; DQ Data Quality; DG Data
Governance.
Audi-Endowed Chair of
Supply Net Order Management
20Prof. Dr. Boris Otto | Berlin, 2013/9/23
Data RequestData Quality
CheckApproval of Data Quality
Creation of Data Record
Data quality must before assured before transaction MM01 is
executed …
Audi-Endowed Chair of
Supply Net Order Management
21Prof. Dr. Boris Otto | Berlin, 2013/9/23
… which is easier said than done ...
34+
Audi-Endowed Chair of
Supply Net Order Management
22Prof. Dr. Boris Otto | Berlin, 2013/9/23
… with so many different stakeholders involved.
R&D Marketing SalesProduction Purchasing
Quality
Management
Planning Financial
AccountingControlling Materials
Management
Warehouse
Management
11+
Audi-Endowed Chair of
Supply Net Order Management
23Prof. Dr. Boris Otto | Berlin, 2013/9/23
Many companies assess the lifecycle costs of their master data
assets
Before use 200 EUR(Creation of new code)
2.500 EUR - -
During use 175 EUR(Code change)
1.500 EUR 2.400 EUR(3.000 CHF)
2.861 EUR
After use 133 EUR - - -
Audi-Endowed Chair of
Supply Net Order Management
24Prof. Dr. Boris Otto | Berlin, 2013/9/23
1) [FOHRER 2012].
Data must be captured at the source of the knowledge about it
Audi-Endowed Chair of
Supply Net Order Management
25Prof. Dr. Boris Otto | Berlin, 2013/9/23
1) [EBNER/BRAUER 2011].
A data quality index is an effective performance management
tool at Bayer CropScience
84
86
88
90
92
94
96
98
100
11/2009 01/2010 03/2010 05/2010 07/2010 09/2010 11/2010 01/2011
Asia Pacific
Europe
Latin America
North America
[%]
Evolution of Material Master Data Quality
Audi-Endowed Chair of
Supply Net Order Management
26Prof. Dr. Boris Otto | Berlin, 2013/9/23
Johnson & Johnson has reached a six sigma data quality level1
Evolution of Material Master Data Quality
1) [OTTO 2013].
Audi-Endowed Chair of
Supply Net Order Management
27Prof. Dr. Boris Otto | Berlin, 2013/9/23
Data Governance at Bosch engages different roles on different
organizational levels across the company1
Master Data
Owner X
Executive Management
Master Data Management
Steering Committee
…
corporate sector/
corporate department
Responsibility
in relevant units (data
maintenance/ application)
IT ProjectsIT platforms, IT target systems
Overall responsibilityfor a master data class
(specialist/organizational
level)
Master Data
Owner A
Master dataclass 1
Master dataclass N
report
Governance
Function
working group /
competence team
ConceptsConcepts
Governance
Function
…
Master Data
Officer
…
Master Data
Officer
e. g. Supplier master data Chart of accounts
Inte
rdis
cip
linary
(MD
Ow
ner, IT
, ..)
1) [HATZ 2008].
Audi-Endowed Chair of
Supply Net Order Management
28Prof. Dr. Boris Otto | Berlin, 2013/9/23
The “business case” for Data Governance and Corporate Data
Quality must take into account their very nature
Energy Networks Highway Networks Corporate Data Quality
Audi-Endowed Chair of
Supply Net Order Management
29Prof. Dr. Boris Otto | Berlin, 2013/9/23
Master Data as a Business Success Factor
Five Principles for Master Data Governance
Outlook
Agenda
Audi-Endowed Chair of
Supply Net Order Management
30Prof. Dr. Boris Otto | Berlin, 2013/9/23
Many enterprises are on the way towards a new corporate data
architecture
Data in the outer circles is of higher “fuzziness”,
volume, change frequency…
Data in the outer circles is of less
control, criticality, unambiguity…
“Nucleus Data”
(Customer master
data, product master
data etc.)
“Community Data”
(Geo-information,
GTIN, addresses
etc.)
“Open Big Data”
(Tweets, social media
streams, sensor data etc.)
Audi-Endowed Chair of
Supply Net Order Management
31Prof. Dr. Boris Otto | Berlin, 2013/9/23
SAP and the CC CDQ have published a joint white paper
Audi-Endowed Chair of
Supply Net Order Management
32Prof. Dr. Boris Otto | Berlin, 2013/9/23
The Competence Center Corporate Data Quality (CC CDQ)
channels “best practices” of market-leading companies
NB: Past and present partner companies.
Audi-Endowed Chair of
Supply Net Order Management
33Prof. Dr. Boris Otto | Berlin, 2013/9/23
Your Speaker
Univ.-Prof. Dr. Ing. Boris Otto
TU Dortmund University
Audi-Endowed Chair of
Supply Net Order Management
LogistikCampus
Joseph-Fraunhofer-Straße 2-4
D-44227 Dortmund
Audi-Endowed Chair of
Supply Net Order Management
34Prof. Dr. Boris Otto | Berlin, 2013/9/23
References[BRAUER 2009]
B. BRAUER, Master Data Quality Cockpit at Bayer CropScience, 4. Workshop des Kompetenzzentrums Corporate Data Quality 2 (CC
CDQ2), Universität St. Gallen, Luzern, 2009.
[EBNER/BRAUER 2011]
V. EBNER, B. BRAUER: Fallstudie zum Führungssystem für Stammdatenqualität bei der Bayer CropScience AG. In: HMD - Praxis
der Wirtschaftsinformatik 48 (2011), S. 64-73.
[FOHRER 2012]
M. FOHRER, 2012. Driving Corporate Data Quality @ Hilti through the use of Consumer Technology. 10. CC CDQ3-Workshop.
Bregenz: Universität St. Gallen, Institut für Wirtschaftsinformatik.
[HATZ 2008]
A. HATZ, BOSCH Master data Management, 6. CC CDQ Workshop, St. Gallen, 2008.
[MESSERSCHMIDT/STÜBEN 2011]
M. MESSERSCHMIDT, J. STÜBEN: Verborgene Schätze: Eine internationale Studie zum Master-Data-Management,
PricewaterhouseCooopers AG, 2011
[OTTO ET AL. 2011]
B. OTTO, J. KOKEMÜLLER, A. WEISBECKER, D. GIZANIS: Stammdatenmanagement: Datenqualität für Geschäftsprozesse. In:
HMD - Praxis der Wirtschaftsinformatik 48 (2011), S. 5-16.
[OTTO 2013]
B. OTTO, 2013. On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America.
In: SADIQ, S. (ed.) Handbook of Data Quality - Research and Practice. Berlin: Springer.