This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.

1. © Fraunhofer ISST
DATA GOVERNANCE
Prof. Dr.-Ing. Boris Otto  28 September 2018  Dortmund
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Bildquelle: guinnessworldrecords.com (2017).
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CONTENT
 A Brief History of Data Governance
 Data Governance in Business Ecosystems
 The IDS Approach to Data Governance
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Around the millennium change Data Governance increasingly received
attention as a response to compliance risks
Image sources: infrapark-baselland.com (2018), bruecken.deutschebahn.com (2018). Logos from company websites
and Wikipedia (2018).
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Financial Regulations
 Bankruptcy of energy giant Enron due to
fictional financial reporting
 In the course of this process, Arthur Andersen
found guilty of obstruction of justice for
shredding thousands of documents
 The company surrendered its CPA license on
August 31, 2002, and 85,000 employees lost
their jobs
Governmental Regulations
 »Leistungs- und Finanzierungsvereinbarung
(LuFV)« links funding of Deutsche Bahn to
quality of infrastructure inventory
 Direct relationship between quality of data and
financial situation
Environmental Regulations
 Chemical spill into the river Rhine in 1986 at
Sandoz plant in Basel-Schweizerhalle
 No data about nature and implications of
chemical substances spilled
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Business drivers for Data Governance were – and still are – multifold and
affect the company as a whole
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Group Level
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« for business reporting
Smooth business integrations
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Data quality evolves over time according to a »jigsaw« pattern
Legend: Data quality issues.
Data Quality
Time
Project 1 Project 2 Project 3
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Reasons for poor data quality are manifold – as the example of Bayer
CropScience shows
NB: For background on the case study see Ebner et al. (2011).
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Data Quality
Issues
Employees Data Maintenance
DQ 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
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Corporate life is hard without Data Governance
Image source: Strassmann (1995).
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Data Governance and Data Quality Management are closely interrelated
Source: Otto (2011).
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Legend: Goal Function Data.
Data
Governance
Data Quality
Management
Maximize
Data Quality
Maximize
Data Value
Data Resource
Data Resource
Management
is sub-goal of
supports supports
is led by is sub-function
of
are object of is object of
are object of
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A strategic resource is a source of competitive advantage
Strategic
Resource
V Value
R Rarity
I Inimitability
N/O
Non-substitutability
Organization
Source: Barney (1991); Makadok (2001).
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VRIN/VRIO Framework
 Resources
 »all assets, capabilities, organizational processes,
firm attributes, information, knowledge, etc.
controlled by a firm that enable the firm to conceive
of and implement strategies that improve its
efficiency and effectiveness«
 Capabilities
 »special type of resource, specifically an
organizationally embedded non-transferable firm-
specific resource whose purpose is to improve the
productivity of the other resources possessed by the
firm«
Resource-Based View of the Firm
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Despite its intangible nature, industrial data has a value which can be
quantified
Source: Moody & Walsh (1999).
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Number of users
Share of value
100%
Data
Tangible
Goods
Tangible
Goods
Value
Data
Usage Time
Potential value
Data
Data quality
Value
100%
Data
Integration
Value
Data
Volume
Value
Data
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Many examples exist demonstrating the applicability of valuation procedures
in the data domain
Source: Otto (2012); Otto (2015), Zechmann (2017).
Company Industry Country Data domain
Valuation
approach
Value per record
Retail US
Customer data
including shopping
profile
Market value 1.6 EUR
Social Network US User data Market value 225 USD
Automation and
drives
DE Master data on parts
Production
costs
500 to 5.000 EUR
Agrochemical CH Material master data
Use/income
value
184 CHF
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Data Governance aims at allocating decision rights for the management and
use of data within an organization
Source: Otto (2011).
Data Governance Organization
Data Governance Goals Data Governance Structure
Formal Goals
Business Goals
 Ensure compliance
 Enable decision-making
 Improve customer satisfaction
 Increase operational efficiency
 Support business integration
IS/IT-related Goals
 Increase data quality
 Support IS integration (e.g. migrations)
Functional Goals
 Create data strategy and policies
 Establish data quality controlling
 Establish data stewardship
 Implement data standards and metadata management
 Establish data life-cycle management
 Establish data architecture management
Locus of Control
Functional Positioning
 Business department
 IS/IT department
 Executive management
 Middle management
Hierarchical Positioning
Organizational Form
 Centralized
 Decentralized/local
 Project organization
 Virtual organization
 Shared service
Roles and Committees
 Sponsor
 Data governance council
 Data owner
 Data stewards (business and technical)
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Data Governance is typically established as an enterprise-wide virtual
organization – as the example of BOSCH shows
Source: Bosch (2008).
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Master Data
Owner n
Executive Management
Master Data Management
Steering Committee
…
Group Division/
Central Function
Accountability on
Business Unit Level
(Data Maintenance)
IT Projects
IT Platforms, IT Target Systems
Overall Accountability
(organizational level) Master Data
Owner A
Master Data
Domain 1
Master Data
Domain n
Report
Governance
Working Group
Team of Experts
ConceptsConcepts
Governance
… …
e.g. Vendor Master Data Chart of Accounts
Interdisciplinarily
staffed
Master Data
Officer
Master Data
Officer
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A data quality index is an effective performance management tool at Bayer
CropScience
Source: Ebner & Brauer (2011).
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
Material Master Data Quality Index
Asia Pacific
Europe
Latin America
North America
[%]
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Johnson & Johnson has reached a six sigma data quality level
Source: Otto (2013).
99,503
94,586
95,506
96,102
95,778
96,312
95,656
89,855
91,629
96,324 96,383
97,433
95,417
99,135
99,885 99,971 99,993 99,999
84
86
88
90
92
94
96
98
100
02.15.11 04.15.11 06.15.11 08.15.11 10.15.11 12.15.11 02.15.12 04.15.12 06.15.12
Data Quality Index
Data Quality Index
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Five key principles lead to excellence in master data governance
Source: Otto & Österle (2015).
Capture Data at the Source
Enter Data »First Time Right«
Measure to Manage
Build up a Data Governance Capability
Scale Capabilities Globally
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Life’s good with Data Governance
Image source: Strassmann (1995).
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Developed by the Competence Center Corporate Data Quality, the Data Excellence
Model (DXM) defines building blocks for data management
Source: Competence Center Corporate Data Quality (2017).
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GOALS ENABLERS RES ULTS
D A T A
S T R A T E G Y
P E O P L E , R O L E S &
R E S P O N S I B I L I T I E S
P R O C E S S E S &
ME T H O D S
D A T A
L I F E C Y C L E
D A T A
A P P L I C A T I O N S
D A T A
A R C H I T E C T U R E
P E R F O R MA N C E
MA N A G E ME N T
B U S I N E S S
C A P A B I L I T I E S
D A T A
MA N A G E ME N T
C A P A B I L I T I E S
B U S I N E S S
V A L U E
D A T A
E X C E L L E N C E
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Smart Data Engineering is model-based, method-oriented approach for
building up an effective Data Resource Management capability
 Defining the data strategy
 Assigning roles and responsibilities for
core data domains
 Managing data as an economic good
 Designing a consistent data
architecture for the digitalized
enterprise
 Controlling the business benefit
contribution of the data resource
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CONTENT
 A Brief History of Data Governance
 Data Governance in Business Ecosystems
 The IDS Approach to Data Governance
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Data has become a strategic enterprise resource
Legend: MRP – Manufacturing Resource Planning; ERP – Enterprise Resource Planning.
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Data as a Process Result Data as a Process Enabler Data as a Product Enabler Data as a Product
Information systems have been used
since the 1960s and 1970s to support
enterprise functions, but data wasn‘t
shared between functions, let alone
enterprises.
With the proliferation of MRP and
ERP systems in the 1980s and 1990s
data enabled end-to-end business
processes such as order-to-cash,
procure-to-pay, make-to-stock etc.
Since the millennium change, data
has increasingly become an enabler
of innovative product-service-
systems and integrated solutions.
Recently, data marketplaces
emerged offering data APIs at a
volume or frequency based fee.
Data has become a product in its
own right.
Mainframe Computing Enterprise Systems Electronic Business Data Economy
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In the era of digitalization, companies must develop their Data Management
from »Defense« to »Offense«
Source: DalleMulle & Davenport (2017).
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Defense Offense
Key Objectives
Ensure data security, privacy, integrity,
quality, regulatory compliance, and
governance
Improve competitive position and
profitability
Core Activities
Optimize data extraction, standardization,
storage, and access
Optimize data analytics, modeling,
visualization, transformation, and
enrichment
Data Management
Orientation
Control Flexibility
Enabling Architecture Single Source of Truth Multiple Versions of the Truth
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 Data Intelligence Hub
 Data sharing platform
 Data sovereignty and security
The data economy is here
Sources: Deutsche Telekom (2018); HERE (2018); CDQ (2018).
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 HERE Tracking Cloud
 Community approach to data
management
 Using the power of many
Deutsche Telekom HERE Corporate Data League
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Sharing data is a prerequisite for ecosystems
Image sources: Johns Hopkins University (2016), Umweltbundesamt (2016), Smellgard, Schneider & Farkas (2016),
urbanmanagement.nl (2017).
Data Sharing
Energy
Health Care
Material Sciences
Manufacturing and
Logistics
»Smart Cities«
Sharing of material information along the entire
product life cycle
Shared use of process data for predictive asset
maintenance
Exchange of master and event data along the entire
supply chain
Anonymized, shared data pool for better drug
development
Shared use of data for end-to-end consumer services
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Data sovereignty is a prerequisite for innovative business models in various
domains
Image sources: perm4.com (2017); hccs.edu (2017); dvz.de (2017).
Health Care Patient Data
 Use purpose
 Anonymization
 System constraints
 Personalized medicine
 Better healthcare
services
Domain Data Usage Conditions Value Potential
Production
Product Data
Process Data
 Usage frequency
 Usage types
 Use purpose
 Innovative production
networks
 »Production as a Service«
Automotive Planning and Risk Data
 Use purpose
 Expiration date
 System constraints
 Better risk management
 Less production bottle
necks
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The role of Data Governance differs between Offense and Defense Data
Management…
Image source: ebay (2018).
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Defense Offense
Scope Enterprise-internal Ecosystem, Customer
Ownership Setting data standards Executing property rights
Stewardship Quality Curation
Organization Hierarchy Market, Community
Data Flows Internal between application systems Data value chains in networks
Usage Access Rights Usage Rights
Economics Cost and Use Value Market value
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CONTENT
 A Brief History of Data Governance
 Data Governance in Business Ecosystems
 The IDS Approach to Data Governance
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The IDS Reference Architecture Model responds to the most important issues
in data sharing
Source: PwC (2017). The International Data Spaces (IDS) Association publishes the IDS Reference Architecture Model
(IDS-RAM). The Industrial Data Space is a vertical application of the IDS-RAM.
57%
worry about revealing
valuable data and
business secrets.
59%
fear the loss of control
over their data.
55%
feel inconsistent
processes and systems
as a (very) big obstacle.
32%
fear that platforms do
not reach the critical
mass, so that data
exchange will be
interesting.
InteroperabilityData SovereigntyTrust and Security Join us!
Today
IDS Approach
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Data sovereignty is needed for effective Supply Chain Risk Management
OEM»Tier 1« Supplier
Risk
Management
Supplier
Management
• Contact person
• Risk type
• Risk location
• Affected parts
• Affected sub-
suppliers
• Capacities and
inventory levels
• Contact person
• Parts demand
• Inventory
levels
Use context
Risk
management
Condition
Deletion after 3
days
Use context
Supplier
management
Condition
Deletion after 14
days
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Data sovereignty is needed for innovation in the pharmaceutical industry
Pharma Company
Usage context
Clinical research
Anonymization
Data record must
consists of at least
150 individual
anonymized data
sets
University Hospital
Patient
Management
Smart Drug
Development
• Health data
• Medication plan
• Electronic case
records
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Data sovereignty is a prerequisite for flexible and dynamic production
networks
“Production as a
Service” Provider
OEM
Production
Planning and
Control
• CAD data
• Configuration
parameters
• Production
volume
• Usage time
• Temperature
data
• Certificates
Usage context
Maintenance, no
forwarding
Condition
Operator
anonymous
Maintenance
Usage context
Machine type
Condition
Delete CAD data
after first use
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Usage conditions for data are multifold
Dimension Specification Example
Geo-information
Coordinates 51.493773, 7.407025, radius 1km
Geo polygon
ZIP code 44227
Country code DE
Expiration date Absolute date December 24, 2017
Anonymization
Role, function
Usage purpose
Positive list Use for machine configuration
Negative list Not for marketing use
Propagation
Allow, deny
Allow on a fee Yes, with 20 percent surplus charge
Number of uses Absolute figure Once
Deletion
System constraints
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The Industrial Data Space provides an architecture for the sovereign exchange
of data
Legend: IDS Connector; Usage Constraints; Non-IDS Communication.
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Industrial
Data Cloud
IoT Cloud
Enterprise
Cloud
Data
Marketplace
Company 1 Company 2 Company n + 2Company n + 1Company n
Open Data
Source
IDS
IDS IDS
IDS
IDS IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
IDS
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The Industrial Data Space forms an ecosystem around the sovereign exchange
of data
Quelle: IDS Reference Architecture Model Version 2.0 (2018).
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Data Governance activities are distributed to the different roles in the IDS
ecosystem
NB: Activities in brackets are to be discussed.
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IDS Role Data Governance Activity IDS Software Component
Data
Owner/Provider
 Define usage constraints for data resources
 Publish metadata (incl. usage constraints) to broker
 Transfer data with usage constraints linked to data
 Receive information about data transaction from Clearing House
 Bill data (if required)
 (Monitor policy enforcement)
IDS Connector
Data
Consumer/User
 Use data in compliance with use constraints IDS Connector
Broker  Match data demand and supply Broker Software
Clearing House  Monitor and log data transactions and data value chains
 (Monitor policy enforcement)
 (Perform data accounting)
Clearing House Software
App Store
Provider
 Offer data governance and data quality services App Store Software
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Prof. Dr.-Ing. Boris Otto
Fraunhofer ISST · Executive Director
TU Dortmund · Faculty of Mechanical Engineering
Boris.Otto@isst.fraunhofer.de · Boris.Otto@tu-dortmund.de
https://de.linkedin.com/pub/boris-otto/1/1b5/570
https://twitter.com/drborisotto
https://www.xing.com/profile/Boris_Otto
http://www.researchgate.net/profile/Boris_Otto
http://de.slideshare.net/borisotto
Please get in touch!
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37. © Fraunhofer ISST
DATA GOVERNANCE
Prof. Dr.-Ing. Boris Otto  28 September 2018  Dortmund
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Bildquelle: guinnessworldrecords.com (2017).
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